WorldWideScience

Sample records for recognition feature sets

  1. Comparing Pattern Recognition Feature Sets for Sorting Triples in the FIRST Database

    Science.gov (United States)

    Proctor, D. D.

    2006-07-01

    Pattern recognition techniques have been used with increasing success for coping with the tremendous amounts of data being generated by automated surveys. Usually this process involves construction of training sets, the typical examples of data with known classifications. Given a feature set, along with the training set, statistical methods can be employed to generate a classifier. The classifier is then applied to process the remaining data. Feature set selection, however, is still an issue. This paper presents techniques developed for accommodating data for which a substantive portion of the training set cannot be classified unambiguously, a typical case for low-resolution data. Significance tests on the sort-ordered, sample-size-normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. The technique is applied to comparing feature sets for sorting a particular radio galaxy morphology, bent-doubles, from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) database. Also examined are alternative functional forms for feature sets. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications. The technique also may be applied to situations for which, although accurate classifications are available, the feature set is clearly inadequate, but is desired nonetheless to make the best of available information.

  2. Generalizations of the subject-independent feature set for music-induced emotion recognition.

    Science.gov (United States)

    Lin, Yuan-Pin; Chen, Jyh-Horng; Duann, Jeng-Ren; Lin, Chin-Teng; Jung, Tzyy-Ping

    2011-01-01

    Electroencephalogram (EEG)-based emotion recognition has been an intensely growing field. Yet, how to achieve acceptable accuracy on a practical system with as fewer electrodes as possible is less concerned. This study evaluates a set of subject-independent features, based on differential power asymmetry of symmetric electrode pairs [1], with emphasis on its applicability to subject variability in music-induced emotion classification problem. Results of this study have evidently validated the feasibility of using subject-independent EEG features to classify four emotional states with acceptable accuracy in second-scale temporal resolution. These features could be generalized across subjects to detect emotion induced by music excerpts not limited to the music database that was used to derive the emotion-specific features.

  3. EEG-based recognition of video-induced emotions: selecting subject-independent feature set.

    Science.gov (United States)

    Kortelainen, Jukka; Seppänen, Tapio

    2013-01-01

    Emotions are fundamental for everyday life affecting our communication, learning, perception, and decision making. Including emotions into the human-computer interaction (HCI) could be seen as a significant step forward offering a great potential for developing advanced future technologies. While the electrical activity of the brain is affected by emotions, offers electroencephalogram (EEG) an interesting channel to improve the HCI. In this paper, the selection of subject-independent feature set for EEG-based emotion recognition is studied. We investigate the effect of different feature sets in classifying person's arousal and valence while watching videos with emotional content. The classification performance is optimized by applying a sequential forward floating search algorithm for feature selection. The best classification rate (65.1% for arousal and 63.0% for valence) is obtained with a feature set containing power spectral features from the frequency band of 1-32 Hz. The proposed approach substantially improves the classification rate reported in the literature. In future, further analysis of the video-induced EEG changes including the topographical differences in the spectral features is needed.

  4. Reduced isothermal feature set for long wave infrared (LWIR) face recognition

    Science.gov (United States)

    Donoso, Ramiro; San Martín, Cesar; Hermosilla, Gabriel

    2017-06-01

    In this paper, we introduce a new concept in the thermal face recognition area: isothermal features. This consists of a feature vector built from a thermal signature that depends on the emission of the skin of the person and its temperature. A thermal signature is the appearance of the face to infrared sensors and is unique to each person. The infrared face is decomposed into isothermal regions that present the thermal features of the face. Each isothermal region is modeled as circles within a center representing the pixel of the image, and the feature vector is composed of a maximum radius of the circles at the isothermal region. This feature vector corresponds to the thermal signature of a person. The face recognition process is built using a modification of the Expectation Maximization (EM) algorithm in conjunction with a proposed probabilistic index to the classification process. Results obtained using an infrared database are compared with typical state-of-the-art techniques showing better performance, especially in uncontrolled acquisition conditions scenarios.

  5. Obscene Video Recognition Using Fuzzy SVM and New Sets of Features

    Directory of Open Access Journals (Sweden)

    Alireza Behrad

    2013-02-01

    Full Text Available In this paper, a novel approach for identifying normal and obscene videos is proposed. In order to classify different episodes of a video independently and discard the need to process all frames, first, key frames are extracted and skin regions are detected for groups of video frames starting with key frames. In the second step, three different features including 1- structural features based on single frame information, 2- features based on spatiotemporal volume and 3-motion-based features, are extracted for each episode of video. The PCA-LDA method is then applied to reduce the size of structural features and select more distinctive features. For the final step, we use fuzzy or a Weighted Support Vector Machine (WSVM classifier to identify video episodes. We also employ a multilayer Kohonen network as an initial clustering algorithm to increase the ability to discriminate between the extracted features into two classes of videos. Features based on motion and periodicity characteristics increase the efficiency of the proposed algorithm in videos with bad illumination and skin colour variation. The proposed method is evaluated using 1100 videos in different environmental and illumination conditions. The experimental results show a correct recognition rate of 94.2% for the proposed algorithm.

  6. Probabilistic Open Set Recognition

    Science.gov (United States)

    Jain, Lalit Prithviraj

    Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary

  7. Supplementary features for improving phone recognition

    Science.gov (United States)

    Balaraman, Mridul; Dusan, Sorin; Flanagan, James L.

    2004-10-01

    Traditional speech recognition systems use mel-frequency cepstral coefficients (MFCCs) as acoustic features. The present research aims to study the classification characteristics and the performance of some supplementary features (SFs) such as periodicity, zero crossing rate, log energy and ratio of low frequency energy to total energy, in a phone recognition system, built using the Hidden Markov Model Tool Kit. To demonstrate the performance of the SFs, training is done on a subset of the TIMIT data base (DR1 data set) on context independent phones using a single mixture. When only the SFs and their first derivatives (feature set of dimension 8) are used the recognition accuracy is found to be 42.96% as compared to 54.65% when 12 MFCCs and their corresponding derivatives are used. The performance of the system improves to 56.49%, when the SFs and their derivatives are used along with the MFCCs. A further improvement to 60.34% is observed when the last 4 MFCCs and their derivatives are replaced by SFs and their derivatives, respectively. These results indicate that the supplementary features contain classification characteristics which can be useful in automatic speech recognition.

  8. Dynamic Features for Iris Recognition.

    Science.gov (United States)

    da Costa, R M; Gonzaga, A

    2012-08-01

    The human eye is sensitive to visible light. Increasing illumination on the eye causes the pupil of the eye to contract, while decreasing illumination causes the pupil to dilate. Visible light causes specular reflections inside the iris ring. On the other hand, the human retina is less sensitive to near infra-red (NIR) radiation in the wavelength range from 800 nm to 1400 nm, but iris detail can still be imaged with NIR illumination. In order to measure the dynamic movement of the human pupil and iris while keeping the light-induced reflexes from affecting the quality of the digitalized image, this paper describes a device based on the consensual reflex. This biological phenomenon contracts and dilates the two pupils synchronously when illuminating one of the eyes by visible light. In this paper, we propose to capture images of the pupil of one eye using NIR illumination while illuminating the other eye using a visible-light pulse. This new approach extracts iris features called "dynamic features (DFs)." This innovative methodology proposes the extraction of information about the way the human eye reacts to light, and to use such information for biometric recognition purposes. The results demonstrate that these features are discriminating features, and, even using the Euclidean distance measure, an average accuracy of recognition of 99.1% was obtained. The proposed methodology has the potential to be "fraud-proof," because these DFs can only be extracted from living irises.

  9. An enhanced feature set for pattern recognition based contrast enhancement of contact-less captured latent fingerprints in digitized crime scene forensics

    Science.gov (United States)

    Hildebrandt, Mario; Kiltz, Stefan; Dittmann, Jana; Vielhauer, Claus

    2014-02-01

    In crime scene forensics latent fingerprints are found on various substrates. Nowadays primarily physical or chemical preprocessing techniques are applied for enhancing the visibility of the fingerprint trace. In order to avoid altering the trace it has been shown that contact-less sensors offer a non-destructive acquisition approach. Here, the exploitation of fingerprint or substrate properties and the utilization of signal processing techniques are an essential requirement to enhance the fingerprint visibility. However, especially the optimal sensory is often substrate-dependent. An enhanced generic pattern recognition based contrast enhancement approach for scans of a chromatic white light sensor is introduced in Hildebrandt et al.1 using statistical, structural and Benford's law2 features for blocks of 50 micron. This approach achieves very good results for latent fingerprints on cooperative, non-textured, smooth substrates. However, on textured and structured substrates the error rates are very high and the approach thus unsuitable for forensic use cases. We propose the extension of the feature set with semantic features derived from known Gabor filter based exemplar fingerprint enhancement techniques by suggesting an Epsilon-neighborhood of each block in order to achieve an improved accuracy (called fingerprint ridge orientation semantics). Furthermore, we use rotation invariant Hu moments as an extension of the structural features and two additional preprocessing methods (separate X- and Y Sobel operators). This results in a 408-dimensional feature space. In our experiments we investigate and report the recognition accuracy for eight substrates, each with ten latent fingerprints: white furniture surface, veneered plywood, brushed stainless steel, aluminum foil, "Golden-Oak" veneer, non-metallic matte car body finish, metallic car body finish and blued metal. In comparison to Hildebrandt et al.,1 our evaluation shows a significant reduction of the error rates

  10. Feature selection for data and pattern recognition

    CERN Document Server

    Jain, Lakhmi

    2015-01-01

    This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition. Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks. This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.

  11. Multivariate dice recognition using invariant features

    Science.gov (United States)

    Hsu, Gee-Sern; Peng, Hsiao-Chia; Yeh, Shang-Min; Lin, Chyi-Yeu

    2013-04-01

    A system is proposed for automatic reading of the number of dots on dice in general table game settings. Different from previous dice recognition systems that recognize dice of a specific color using a single top-view camera in an enclosure with controlled settings, the proposed one uses multiple cameras to recognize dice of various colors and under uncontrolled conditions. It is composed of three modules. Module-1 locates the dice using the gradient-conditioned color segmentation, proposed, to segment dice of arbitrary colors from the background. Module-2 exploits the local invariant features good for building homographies, giving a solution to segment the top faces of the dice. To identify the dots on the segmented top faces, a maximally stable extremal region detector is embedded in module-3 for its consistency in locating the dot region. Experiments show that the proposed system performs satisfactorily in various test conditions.

  12. Learning Hierarchical Feature Extractors for Image Recognition

    Science.gov (United States)

    2012-09-01

    Learning Hierarchical Feature Extractors For Image Recognition by Y-Lan Boureau A dissertation submitted in partial fulfillment of the requirements...DATES COVERED 00-00-2012 to 00-00-2012 4. TITLE AND SUBTITLE Learning Hierarchical Feature Extractors For Image Recognition 5a. CONTRACT...pooling for all weighting schemes. With average pooling, weighting by the square root of the cluster weight performs best. P = 16 configuration space

  13. Statistical feature extraction based iris recognition system

    Indian Academy of Sciences (India)

    Iris recognition systems have been proposed by numerous researchers using different feature extraction techniques for accurate and reliable biometric authentication. In this paper, a statistical feature extraction technique based on correlation between adjacent pixels has been proposed and implemented. Hamming ...

  14. Boosting Discriminant Learners for Gait Recognition Using MPCA Features

    Directory of Open Access Journals (Sweden)

    Haiping Lu

    2009-01-01

    Full Text Available This paper proposes a boosted linear discriminant analysis (LDA solution on features extracted by the multilinear principal component analysis (MPCA to enhance gait recognition performance. Three-dimensional gait objects are projected in the MPCA space first to obtain low-dimensional tensorial features. Then, lower-dimensional vectorial features are obtained through discriminative feature selection. These feature vectors are then fed into an LDA-style booster, where several regularized and weakened LDA learners work together to produce a strong learner through a novel feature weighting and sampling process. The LDA learner employs a simple nearest-neighbor classifier with a weighted angle distance measure for classification. The experimental results on the NIST/USF “Gait Challenge” data-sets show that the proposed solution has successfully improved the gait recognition performance and outperformed several state-of-the-art gait recognition algorithms.

  15. Feature coding for image representation and recognition

    CERN Document Server

    Huang, Yongzhen

    2015-01-01

    This brief presents a comprehensive introduction to feature coding, which serves as a key module for the typical object recognition pipeline. The text offers a rich blend of theory and practice while reflects the recent developments on feature coding, covering the following five aspects: (1) Review the state-of-the-art, analyzing the motivations and mathematical representations of various feature coding methods; (2) Explore how various feature coding algorithms evolve along years; (3) Summarize the main characteristics of typical feature coding algorithms and categorize them accordingly; (4) D

  16. Genetic feature selection for gait recognition

    Science.gov (United States)

    Tafazzoli, Faezeh; Bebis, George; Louis, Sushil; Hussain, Muhammad

    2015-01-01

    Many research studies have demonstrated that gait can serve as a useful biometric modality for human identification at a distance. Traditional gait recognition systems, however, have mostly been evaluated without explicitly considering the most relevant gait features, which might have compromised performance. We investigate the problem of selecting a subset of the most relevant gait features for improving gait recognition performance. This is achieved by discarding redundant and irrelevant gait features while preserving the most informative ones. Motivated by our previous work on feature subset selection using genetic algorithms (GAs), we propose using GAs to select an optimal subset of gait features. First, features are extracted using kernel principal component analysis (KPCA) on spatiotemporal projections of gait silhouettes. Then, GA is applied to select a subset of eigenvectors in KPCA space that best represents a subject's identity. Each gait pattern is then represented by projecting it only on the eigenvectors selected by the GA. To evaluate the effectiveness of the selected features, we have experimented with two different classifiers: k nearest-neighbor and Naïve Bayes classifier. We report considerable gait recognition performance improvements on the Georgia Tech and CASIA databases.

  17. Biometric Features in Person Recognition Systems

    Directory of Open Access Journals (Sweden)

    Edgaras Ivanovas

    2011-03-01

    Full Text Available Lately a lot of research effort is devoted for recognition of a human being using his biometric characteristics. Biometric recognition systems are used in various applications, e. g., identification for state border crossing or firearm, which allows only enrolled persons to use it. In this paper biometric characteristics and their properties are reviewed. Development of high accuracy system requires distinctive and permanent characteristics, whereas development of user friendly system requires collectable and acceptable characteristics. It is showed that properties of biometric characteristics do not influence research effort significantly. Properties of biometric characteristic features and their influence are discussed.Article in Lithuanian

  18. Feature Vector Construction Method for IRIS Recognition

    Science.gov (United States)

    Odinokikh, G.; Fartukov, A.; Korobkin, M.; Yoo, J.

    2017-05-01

    One of the basic stages of iris recognition pipeline is iris feature vector construction procedure. The procedure represents the extraction of iris texture information relevant to its subsequent comparison. Thorough investigation of feature vectors obtained from iris showed that not all the vector elements are equally relevant. There are two characteristics which determine the vector element utility: fragility and discriminability. Conventional iris feature extraction methods consider the concept of fragility as the feature vector instability without respect to the nature of such instability appearance. This work separates sources of the instability into natural and encodinginduced which helps deeply investigate each source of instability independently. According to the separation concept, a novel approach of iris feature vector construction is proposed. The approach consists of two steps: iris feature extraction using Gabor filtering with optimal parameters and quantization with separated preliminary optimized fragility thresholds. The proposed method has been tested on two different datasets of iris images captured under changing environmental conditions. The testing results show that the proposed method surpasses all the methods considered as a prior art by recognition accuracy on both datasets.

  19. Semisupervised feature selection via spline regression for video semantic recognition.

    Science.gov (United States)

    Han, Yahong; Yang, Yi; Yan, Yan; Ma, Zhigang; Sebe, Nicu; Zhou, Xiaofang

    2015-02-01

    To improve both the efficiency and accuracy of video semantic recognition, we can perform feature selection on the extracted video features to select a subset of features from the high-dimensional feature set for a compact and accurate video data representation. Provided the number of labeled videos is small, supervised feature selection could fail to identify the relevant features that are discriminative to target classes. In many applications, abundant unlabeled videos are easily accessible. This motivates us to develop semisupervised feature selection algorithms to better identify the relevant video features, which are discriminative to target classes by effectively exploiting the information underlying the huge amount of unlabeled video data. In this paper, we propose a framework of video semantic recognition by semisupervised feature selection via spline regression (S(2)FS(2)R) . Two scatter matrices are combined to capture both the discriminative information and the local geometry structure of labeled and unlabeled training videos: A within-class scatter matrix encoding discriminative information of labeled training videos and a spline scatter output from a local spline regression encoding data distribution. An l2,1 -norm is imposed as a regularization term on the transformation matrix to ensure it is sparse in rows, making it particularly suitable for feature selection. To efficiently solve S(2)FS(2)R , we develop an iterative algorithm and prove its convergency. In the experiments, three typical tasks of video semantic recognition, such as video concept detection, video classification, and human action recognition, are used to demonstrate that the proposed S(2)FS(2)R achieves better performance compared with the state-of-the-art methods.

  20. PHROG: A Multimodal Feature for Place Recognition

    Directory of Open Access Journals (Sweden)

    Fabien Bonardi

    2017-05-01

    Full Text Available Long-term place recognition in outdoor environments remains a challenge due to high appearance changes in the environment. The problem becomes even more difficult when the matching between two scenes has to be made with information coming from different visual sources, particularly with different spectral ranges. For instance, an infrared camera is helpful for night vision in combination with a visible camera. In this paper, we emphasize our work on testing usual feature point extractors under both constraints: repeatability across spectral ranges and long-term appearance. We develop a new feature extraction method dedicated to improve the repeatability across spectral ranges. We conduct an evaluation of feature robustness on long-term datasets coming from different imaging sources (optics, sensors size and spectral ranges with a Bag-of-Words approach. The tests we perform demonstrate that our method brings a significant improvement on the image retrieval issue in a visual place recognition context, particularly when there is a need to associate images from various spectral ranges such as infrared and visible: we have evaluated our approach using visible, Near InfraRed (NIR, Short Wavelength InfraRed (SWIR and Long Wavelength InfraRed (LWIR.

  1. Robust Feature Detection for Facial Expression Recognition

    Directory of Open Access Journals (Sweden)

    Spiros Ioannou

    2007-07-01

    Full Text Available This paper presents a robust and adaptable facial feature extraction system used for facial expression recognition in human-computer interaction (HCI environments. Such environments are usually uncontrolled in terms of lighting and color quality, as well as human expressivity and movement; as a result, using a single feature extraction technique may fail in some parts of a video sequence, while performing well in others. The proposed system is based on a multicue feature extraction and fusion technique, which provides MPEG-4-compatible features assorted with a confidence measure. This confidence measure is used to pinpoint cases where detection of individual features may be wrong and reduce their contribution to the training phase or their importance in deducing the observed facial expression, while the fusion process ensures that the final result regarding the features will be based on the extraction technique that performed better given the particular lighting or color conditions. Real data and results are presented, involving both extreme and intermediate expression/emotional states, obtained within the sensitive artificial listener HCI environment that was generated in the framework of related European projects.

  2. Iris Recognition Using Feature Extraction of Box Counting Fractal Dimension

    Science.gov (United States)

    Khotimah, C.; Juniati, D.

    2018-01-01

    Biometrics is a science that is now growing rapidly. Iris recognition is a biometric modality which captures a photo of the eye pattern. The markings of the iris are distinctive that it has been proposed to use as a means of identification, instead of fingerprints. Iris recognition was chosen for identification in this research because every human has a special feature that each individual is different and the iris is protected by the cornea so that it will have a fixed shape. This iris recognition consists of three step: pre-processing of data, feature extraction, and feature matching. Hough transformation is used in the process of pre-processing to locate the iris area and Daugman’s rubber sheet model to normalize the iris data set into rectangular blocks. To find the characteristics of the iris, it was used box counting method to get the fractal dimension value of the iris. Tests carried out by used k-fold cross method with k = 5. In each test used 10 different grade K of K-Nearest Neighbor (KNN). The result of iris recognition was obtained with the best accuracy was 92,63 % for K = 3 value on K-Nearest Neighbor (KNN) method.

  3. Spectral feature classification and spatial pattern recognition

    Science.gov (United States)

    Sivertson, W. E., Jr.; Wilson, R. G.

    1979-01-01

    This paper introduces a spatial pattern recognition processing concept involving the use of spectral feature classification technology and coherent optical correlation. The concept defines a hybrid image processing system incorporating both digital and optical technology. The hybrid instrument provides simplified pseudopattern images as functions of pixel classification from information embedded within a real-scene image. These pseudoimages become simplified inputs to an optical correlator for use in a subsequent pattern identification decision useful in executing landmark pointing, tracking, or navigating functions. Real-time classification is proposed as a research tool for exploring ways to enhance input signal-to-noise ratio as an aid in improving optical correlation. The approach can be explored with developing technology, including a current NASA Langley Research Center technology plan that involves a series of related Shuttle-borne experiments. A first-planned experiment, Feature Identification and Location Experiment (FILE), is undergoing final ground testing, and is scheduled for flight on the NASA Shuttle (STS2/flight OSTA-1) in 1980. FILE will evaluate a technique for autonomously classifying earth features into the four categories: bare land; water; vegetation; and clouds, snow, or ice.

  4. Jointly Learning Heterogeneous Features for RGB-D Activity Recognition.

    Science.gov (United States)

    Hu, Jian-Fang; Zheng, Wei-Shi; Lai, Jianhuang; Zhang, Jianguo

    2017-11-01

    In this paper, we focus on heterogeneous features learning for RGB-D activity recognition. We find that features from different channels (RGB, depth) could share some similar hidden structures, and then propose a joint learning model to simultaneously explore the shared and feature-specific components as an instance of heterogeneous multi-task learning. The proposed model formed in a unified framework is capable of: 1) jointly mining a set of subspaces with the same dimensionality to exploit latent shared features across different feature channels, 2) meanwhile, quantifying the shared and feature-specific components of features in the subspaces, and 3) transferring feature-specific intermediate transforms (i-transforms) for learning fusion of heterogeneous features across datasets. To efficiently train the joint model, a three-step iterative optimization algorithm is proposed, followed by a simple inference model. Extensive experimental results on four activity datasets have demonstrated the efficacy of the proposed method. A new RGB-D activity dataset focusing on human-object interaction is further contributed, which presents more challenges for RGB-D activity benchmarking.

  5. Gait recognition using spatio-temporal silhouette-based features

    Science.gov (United States)

    Sabir, Azhin; Al-jawad, Naseer; Jassim, Sabah

    2013-05-01

    This paper presents a new algorithm for human gait recognition based on Spatio-temporal body biometric features using wavelet transforms. The proposed algorithm extracts the Gait cycle depending on the width of boundary box from a sequence of Silhouette images. Gait recognition is based on feature level fusion of three feature vectors: the gait spatio-temporal feature represented by the distances between (feet, knees, hands, shoulders, and height); binary difference between consecutive frames of the silhouette for each leg detected separately based on hamming distance; a vector of statistical parameters captured from the wavelet low frequency domain. The fused feature vector is subjected to dimension reduction using linear discriminate analysis. The Nearest Neighbour with a certain threshold used for classification. The threshold is obtained by experiment from a set of data captured from the CASIA database. We shall demonstrate that our method provides a non-traditional identification based on certain threshold to classify the outsider members as non-classified members.

  6. Military personnel recognition system using texture, colour, and SURF features

    Science.gov (United States)

    Irhebhude, Martins E.; Edirisinghe, Eran A.

    2014-06-01

    This paper presents an automatic, machine vision based, military personnel identification and classification system. Classification is done using a Support Vector Machine (SVM) on sets of Army, Air Force and Navy camouflage uniform personnel datasets. In the proposed system, the arm of service of personnel is recognised by the camouflage of a persons uniform, type of cap and the type of badge/logo. The detailed analysis done include; camouflage cap and plain cap differentiation using gray level co-occurrence matrix (GLCM) texture feature; classification on Army, Air Force and Navy camouflaged uniforms using GLCM texture and colour histogram bin features; plain cap badge classification into Army, Air Force and Navy using Speed Up Robust Feature (SURF). The proposed method recognised camouflage personnel arm of service on sets of data retrieved from google images and selected military websites. Correlation-based Feature Selection (CFS) was used to improve recognition and reduce dimensionality, thereby speeding the classification process. With this method success rates recorded during the analysis include 93.8% for camouflage appearance category, 100%, 90% and 100% rates of plain cap and camouflage cap categories for Army, Air Force and Navy categories, respectively. Accurate recognition was recorded using SURF for the plain cap badge category. Substantial analysis has been carried out and results prove that the proposed method can correctly classify military personnel into various arms of service. We show that the proposed method can be integrated into a face recognition system, which will recognise personnel in addition to determining the arm of service which the personnel belong. Such a system can be used to enhance the security of a military base or facility.

  7. Deep Complementary Bottleneck Features for Visual Speech Recognition

    NARCIS (Netherlands)

    Petridis, Stavros; Pantic, Maja

    Deep bottleneck features (DBNFs) have been used successfully in the past for acoustic speech recognition from audio. However, research on extracting DBNFs for visual speech recognition is very limited. In this work, we present an approach to extract deep bottleneck visual features based on deep

  8. Pattern Recognition by Dinamic Feature Analysis Based on PCA

    Directory of Open Access Journals (Sweden)

    Juliana Valencia-Aguirre

    2009-06-01

    Full Text Available Usually, in pattern recognition problems we represent the observations by mean of measures on appropriate variables of data set, these measures can be categorized as Static and Dynamic Features. Static features are not always an accurate representation of data. In these sense, many phenomena are better modeled by dynamic changes on their measures. The advantage of using an extended form (dynamic features is the inclusion of new information that allows us to get a better representation of the object. Nevertheless, sometimes it is difficult in a classification stage to deal with dynamic features, because the associated computational cost often can be higher than we deal with static features. For analyzing such representations, we use Principal Component Analysis (PCA, arranging dynamic data in such a way we can consider variations related to the intrinsic dynamic of observations. Therefore, the method made possible to evaluate the dynamic information about of the observations on a lower dimensionality feature space without decreasing the accuracy performance. Algorithms were tested on real data to classify pathological speech from normal voices, and using PCA for dynamic feature selection, as well.

  9. Selective Gammatone Envelope Feature for Robust Sound Event Recognition

    Science.gov (United States)

    Leng, Yi Ren; Tran, Huy Dat; Kitaoka, Norihide; Li, Haizhou

    Conventional features for Automatic Speech Recognition and Sound Event Recognition such as Mel-Frequency Cepstral Coefficients (MFCCs) have been shown to perform poorly in noisy conditions. We introduce an auditory feature based on the gammatone filterbank, the Selective Gammatone Envelope Feature (SGEF), for Robust Sound Event Recognition where channel selection and the filterbank envelope is used to reduce the effect of noise for specific noise environments. In the experiments with Hidden Markov Model (HMM) recognizers, we shall show that our feature outperforms MFCCs significantly in four different noisy environments at various signal-to-noise ratios.

  10. Local Feature Learning for Face Recognition under Varying Poses

    DEFF Research Database (Denmark)

    Duan, Xiaodong; Tan, Zheng-Hua

    2015-01-01

    In this paper, we present a local feature learning method for face recognition to deal with varying poses. As opposed to the commonly used approaches of recovering frontal face images from profile views, the proposed method extracts the subject related part from a local feature by removing the pose...... related part in it on the basis of a pose feature. The method has a closed-form solution, hence being time efficient. For performance evaluation, cross pose face recognition experiments are conducted on two public face recognition databases FERET and FEI. The proposed method shows a significant...... recognition improvement under varying poses over general local feature approaches and outperforms or is comparable with related state-of-the-art pose invariant face recognition approaches. Copyright ©2015 by IEEE....

  11. Feature Fusion Algorithm for Multimodal Emotion Recognition from Speech and Facial Expression Signal

    Directory of Open Access Journals (Sweden)

    Han Zhiyan

    2016-01-01

    Full Text Available In order to overcome the limitation of single mode emotion recognition. This paper describes a novel multimodal emotion recognition algorithm, and takes speech signal and facial expression signal as the research subjects. First, fuse the speech signal feature and facial expression signal feature, get sample sets by putting back sampling, and then get classifiers by BP neural network (BPNN. Second, measure the difference between two classifiers by double error difference selection strategy. Finally, get the final recognition result by the majority voting rule. Experiments show the method improves the accuracy of emotion recognition by giving full play to the advantages of decision level fusion and feature level fusion, and makes the whole fusion process close to human emotion recognition more, with a recognition rate 90.4%.

  12. Extraction and Recognition of Nonlinear Interval-Type Features Using Symbolic KDA Algorithm with Application to Face Recognition

    Directory of Open Access Journals (Sweden)

    P. S. Hiremath

    2008-01-01

    recognition in the framework of symbolic data analysis. Classical KDA extracts features, which are single-valued in nature to represent face images. These single-valued variables may not be able to capture variation of each feature in all the images of same subject; this leads to loss of information. The symbolic KDA algorithm extracts most discriminating nonlinear interval-type features which optimally discriminate among the classes represented in the training set. The proposed method has been successfully tested for face recognition using two databases, ORL database and Yale face database. The effectiveness of the proposed method is shown in terms of comparative performance against popular face recognition methods such as kernel Eigenface method and kernel Fisherface method. Experimental results show that symbolic KDA yields improved recognition rate.

  13. A Study of Moment Based Features on Handwritten Digit Recognition

    Directory of Open Access Journals (Sweden)

    Pawan Kumar Singh

    2016-01-01

    Full Text Available Handwritten digit recognition plays a significant role in many user authentication applications in the modern world. As the handwritten digits are not of the same size, thickness, style, and orientation, therefore, these challenges are to be faced to resolve this problem. A lot of work has been done for various non-Indic scripts particularly, in case of Roman, but, in case of Indic scripts, the research is limited. This paper presents a script invariant handwritten digit recognition system for identifying digits written in five popular scripts of Indian subcontinent, namely, Indo-Arabic, Bangla, Devanagari, Roman, and Telugu. A 130-element feature set which is basically a combination of six different types of moments, namely, geometric moment, moment invariant, affine moment invariant, Legendre moment, Zernike moment, and complex moment, has been estimated for each digit sample. Finally, the technique is evaluated on CMATER and MNIST databases using multiple classifiers and, after performing statistical significance tests, it is observed that Multilayer Perceptron (MLP classifier outperforms the others. Satisfactory recognition accuracies are attained for all the five mentioned scripts.

  14. Investigation of efficient features for image recognition by neural networks.

    Science.gov (United States)

    Goltsev, Alexander; Gritsenko, Vladimir

    2012-04-01

    In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better. Copyright © 2011 Elsevier Ltd. All rights reserved.

  15. Robust emotion recognition using spectral and prosodic features

    CERN Document Server

    Rao, K Sreenivasa

    2013-01-01

    In this brief, the authors discuss recently explored spectral (sub-segmental and pitch synchronous) and prosodic (global and local features at word and syllable levels in different parts of the utterance) features for discerning emotions in a robust manner. The authors also delve into the complementary evidences obtained from excitation source, vocal tract system and prosodic features for the purpose of enhancing emotion recognition performance. Features based on speaking rate characteristics are explored with the help of multi-stage and hybrid models for further improving emotion recognition performance. Proposed spectral and prosodic features are evaluated on real life emotional speech corpus.

  16. Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble

    Directory of Open Access Journals (Sweden)

    Pham Tuan D

    2011-04-01

    Full Text Available Abstract Background Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi or small-molecule screens. As such, efficient computational methods are required for automatic cellular phenotype identification capable of dealing with large image data sets. In this paper we investigated an efficient method for the extraction of quantitative features from images by combining second order statistics, or Haralick features, with curvelet transform. A random subspace based classifier ensemble with multiple layer perceptron (MLP as the base classifier was then exploited for classification. Haralick features estimate image properties related to second-order statistics based on the grey level co-occurrence matrix (GLCM, which has been extensively used for various image processing applications. The curvelet transform has a more sparse representation of the image than wavelet, thus offering a description with higher time frequency resolution and high degree of directionality and anisotropy, which is particularly appropriate for many images rich with edges and curves. A combined feature description from Haralick feature and curvelet transform can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the random subspace (RS ensemble method for phenotype classification based on microscopy images. A base classifier is trained with a RS sampled subset of the original feature set and the ensemble assigns a class label by majority voting. Results Experimental results on the phenotype recognition from three benchmarking image sets including HeLa, CHO and RNAi show the effectiveness of the proposed approach. The combined feature is better than any individual one in the classification accuracy. The

  17. Feature Selection Using Adaboost for Face Expression Recognition

    National Research Council Canada - National Science Library

    Silapachote, Piyanuch; Karuppiah, Deepak R; Hanson, Allen R

    2005-01-01

    We propose a classification technique for face expression recognition using AdaBoost that learns by selecting the relevant global and local appearance features with the most discriminating information...

  18. Speech recognition using articulatory and excitation source features

    CERN Document Server

    Rao, K Sreenivasa

    2017-01-01

    This book discusses the contribution of articulatory and excitation source information in discriminating sound units. The authors focus on excitation source component of speech -- and the dynamics of various articulators during speech production -- for enhancement of speech recognition (SR) performance. Speech recognition is analyzed for read, extempore, and conversation modes of speech. Five groups of articulatory features (AFs) are explored for speech recognition, in addition to conventional spectral features. Each chapter provides the motivation for exploring the specific feature for SR task, discusses the methods to extract those features, and finally suggests appropriate models to capture the sound unit specific knowledge from the proposed features. The authors close by discussing various combinations of spectral, articulatory and source features, and the desired models to enhance the performance of SR systems.

  19. Efficient Invariant Features for Sensor Variability Compensation in Speaker Recognition

    Directory of Open Access Journals (Sweden)

    Abdennour Alimohad

    2014-10-01

    Full Text Available In this paper, we investigate the use of invariant features for speaker recognition. Owing to their characteristics, these features are introduced to cope with the difficult and challenging problem of sensor variability and the source of performance degradation inherent in speaker recognition systems. Our experiments show: (1 the effectiveness of these features in match cases; (2 the benefit of combining these features with the mel frequency cepstral coefficients to exploit their discrimination power under uncontrolled conditions (mismatch cases. Consequently, the proposed invariant features result in a performance improvement as demonstrated by a reduction in the equal error rate and the minimum decision cost function compared to the GMM-UBM speaker recognition systems based on MFCC features.

  20. Postprocessing for character recognition using pattern features and linguistic information

    Science.gov (United States)

    Yoshikawa, Takatoshi; Okamoto, Masayosi; Horii, Hiroshi

    1993-04-01

    We propose a new method of post-processing for character recognition using pattern features and linguistic information. This method corrects errors in the recognition of handwritten Japanese sentences containing Kanji characters. This post-process method is characterized by having two types of character recognition. Improving the accuracy of the character recognition rate of Japanese characters is made difficult by the large number of characters, and the existence of characters with similar patterns. Therefore, it is not practical for a character recognition system to recognize all characters in detail. First, this post-processing method generates a candidate character table by recognizing the simplest features of characters. Then, it selects words corresponding to the character from the candidate character table by referring to a word and grammar dictionary before selecting suitable words. If the correct character is included in the candidate character table, this process can correct an error, however, if the character is not included, it cannot correct an error. Therefore, if this method can presume a character does not exist in a candidate character table by using linguistic information (word and grammar dictionary). It then can verify a presumed character by character recognition using complex features. When this method is applied to an online character recognition system, the accuracy of character recognition improves 93.5% to 94.7%. This proved to be the case when it was used for the editorials of a Japanese newspaper (Asahi Shinbun).

  1. Image ratio features for facial expression recognition application.

    Science.gov (United States)

    Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

    2010-06-01

    Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

  2. DWT features performance analysis for automatic speech recognition of Urdu.

    Science.gov (United States)

    Ali, Hazrat; Ahmad, Nasir; Zhou, Xianwei; Iqbal, Khalid; Ali, Sahibzada Muhammad

    2014-01-01

    This paper presents the work on Automatic Speech Recognition of Urdu language, using a comparative analysis for Discrete Wavelets Transform (DWT) based features and Mel Frequency Cepstral Coefficients (MFCC). These features have been extracted for one hundred isolated words of Urdu, each word uttered by ten different speakers. The words have been selected from the most frequently used words of Urdu. A variety of age and dialect has been covered by using a balanced corpus approach. After extraction of features, the classification has been achieved by using Linear Discriminant Analysis. After the classification task, the confusion matrix obtained for the DWT features has been compared with the one obtained for Mel-Frequency Cepstral Coefficients based speech recognition. The framework has been trained and tested for speech data recorded under controlled environments. The experimental results are useful in determination of the optimum features for speech recognition task.

  3. A Hybrid Speech Emotion Recognition System Based on Spectral and Prosodic Features

    OpenAIRE

    ZHOU, Yu; LI, Junfeng; SUN, Yanqing; ZHANG, Jianping; YAN, Yonghong; AKAGI, Masato

    2010-01-01

    In this paper, we present a hybrid speech emotion recognition system exploiting both spectral and prosodic features in speech. For capturing the emotional information in the spectral domain, we propose a new spectral feature extraction method by applying a novel non-uniform subband processing, instead of the mel-frequency subbands used in Mel-Frequency Cepstral Coefficients (MFCC). For prosodic features, a set of features that are closely correlated with speech emotional states are selected. ...

  4. Features fusion based approach for handwritten Gujarati character recognition

    Directory of Open Access Journals (Sweden)

    Ankit Sharma

    2017-02-01

    Full Text Available Handwritten character recognition is a challenging area of research. Lots of research activities in the area of character recognition are already done for Indian languages such as Hindi, Bangla, Kannada, Tamil and Telugu. Literature review on handwritten character recognition indicates that in comparison with other Indian scripts research activities on Gujarati handwritten character recognition are very less.  This paper aims to bring Gujarati character recognition in attention. Recognition of isolated Gujarati handwritten characters is proposed using three different kinds of features and their fusion. Chain code based, zone based and projection profiles based features are utilized as individual features. One of the significant contribution of proposed work is towards the generation of large and representative dataset of 88,000 handwritten Gujarati characters. Experiments are carried out on this developed dataset. Artificial Neural Network (ANN, Support Vector Machine (SVM and Naive Bayes (NB classifier based methods are implemented for handwritten Gujarati character recognition. Experimental results show substantial enhancement over state-of-the-art and authenticate our proposals.

  5. Human Activity Recognition Using Hierarchically-Mined Feature Constellations

    NARCIS (Netherlands)

    Oikonomopoulos, A.; Pantic, Maja

    In this paper we address the problem of human activity modelling and recognition by means of a hierarchical representation of mined dense spatiotemporal features. At each level of the hierarchy, the proposed method selects feature constellations that are increasingly discriminative and

  6. Individual discriminative face recognition models based on subsets of features

    DEFF Research Database (Denmark)

    Clemmensen, Line Katrine Harder; Gomez, David Delgado; Ersbøll, Bjarne Kjær

    2007-01-01

    person from another using only subsets of features will both decrease the computational cost and increase the generalization capacity of the face recognition algorithm. Moreover, identifying which are the features that better discriminate between persons will also provide a deeper understanding...

  7. Local shape feature fusion for improved matching, pose estimation and 3D object recognition

    DEFF Research Database (Denmark)

    Buch, Anders Glent; Petersen, Henrik Gordon; Krüger, Norbert

    2016-01-01

    of the descriptor employed by the recognition system. In addition to this, we evaluate several aspects of the matching task, including the efficiency of the different features, and the potential in using dimension reduction. To arrive at better generalization properties, we introduce a method for fusing several......We provide new insights to the problem of shape feature description and matching, techniques that are often applied within 3D object recognition pipelines. We subject several state of the art features to systematic evaluations based on multiple datasets from different sources in a uniform manner...... feature matches with a limited processing overhead. Our fused feature matches provide a significant increase in matching accuracy, which is consistent over all tested datasets. Finally, we benchmark all features in a 3D object recognition setting, providing further evidence of the advantage of fused...

  8. Facial Expression Recognition Using Stationary Wavelet Transform Features

    Directory of Open Access Journals (Sweden)

    Huma Qayyum

    2017-01-01

    Full Text Available Humans use facial expressions to convey personal feelings. Facial expressions need to be automatically recognized to design control and interactive applications. Feature extraction in an accurate manner is one of the key steps in automatic facial expression recognition system. Current frequency domain facial expression recognition systems have not fully utilized the facial elements and muscle movements for recognition. In this paper, stationary wavelet transform is used to extract features for facial expression recognition due to its good localization characteristics, in both spectral and spatial domains. More specifically a combination of horizontal and vertical subbands of stationary wavelet transform is used as these subbands contain muscle movement information for majority of the facial expressions. Feature dimensionality is further reduced by applying discrete cosine transform on these subbands. The selected features are then passed into feed forward neural network that is trained through back propagation algorithm. An average recognition rate of 98.83% and 96.61% is achieved for JAFFE and CK+ dataset, respectively. An accuracy of 94.28% is achieved for MS-Kinect dataset that is locally recorded. It has been observed that the proposed technique is very promising for facial expression recognition when compared to other state-of-the-art techniques.

  9. Action Recognition by Joint Spatial-Temporal Motion Feature

    Directory of Open Access Journals (Sweden)

    Weihua Zhang

    2013-01-01

    Full Text Available This paper introduces a method for human action recognition based on optical flow motion features extraction. Automatic spatial and temporal alignments are combined together in order to encourage the temporal consistence on each action by an enhanced dynamic time warping (DTW algorithm. At the same time, a fast method based on coarse-to-fine DTW constraint to improve computational performance without reducing accuracy is induced. The main contributions of this study include (1 a joint spatial-temporal multiresolution optical flow computation method which can keep encoding more informative motion information than recent proposed methods, (2 an enhanced DTW method to improve temporal consistence of motion in action recognition, and (3 coarse-to-fine DTW constraint on motion features pyramids to speed up recognition performance. Using this method, high recognition accuracy is achieved on different action databases like Weizmann database and KTH database.

  10. Multimodal recognition based on face and ear using local feature

    Science.gov (United States)

    Yang, Ruyin; Mu, Zhichun; Chen, Long; Fan, Tingyu

    2017-06-01

    The pose issue which may cause loss of useful information has always been a bottleneck in face and ear recognition. To address this problem, we propose a multimodal recognition approach based on face and ear using local feature, which is robust to large facial pose variations in the unconstrained scene. Deep learning method is used for facial pose estimation, and the method of a well-trained Faster R-CNN is used to detect and segment the region of face and ear. Then we propose a weighted region-based recognition method to deal with the local feature. The proposed method has achieved state-of-the-art recognition performance especially when the images are affected by pose variations and random occlusion in unconstrained scene.

  11. Object Recognition by Using Multi-level Feature Point Extraction

    OpenAIRE

    Cheng, Yang; Dubois, Timeo

    2017-01-01

    In this paper, we present a novel approach for object recognition in real-time by employing multilevel feature analysis and demonstrate the practicality of adapting feature extraction into a Naive Bayesian classification framework that enables simple, efficient, and robust performance. We also show the proposed method scales well as the number of level-classes grows. To effectively understand the patches surrounding a keypoint, the trained classifier uses hundreds of simple binary features an...

  12. Are Haar-like Rectangular Features for Biometric Recognition Reducible?

    DEFF Research Database (Denmark)

    Nasrollahi, Kamal; Moeslund, Thomas B.

    2013-01-01

    ? This paper proposes total sensitivity analysis about the mean for this purpose for two different biometric traits, iris and face. Experimental results on multiple public databases show the superiority of the proposed system, using the found influential features, compared to state-of-the-art biometric......Biometric recognition is still a very difficult task in real-world scenarios wherein unforeseen changes in degradations factors like noise, occlusion, blurriness and illumination can drastically affect the extracted features from the biometric signals. Very recently Haar-like rectangular features...... which have usually been used for object detection were introduced for biometric recognition resulting in systems that are robust against most of the mentioned degradations [9]. The problem with these features is that one can define many different such features for a given biometric signal...

  13. Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

    Directory of Open Access Journals (Sweden)

    Liogienė Tatjana

    2016-07-01

    Full Text Available The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion recognition task. Among various feature selection and transformation techniques for one-stage classification, multiple classifier systems were proposed. The main idea of multiple classifiers is to arrange the emotion classification process in stages. Besides parallel and serial cases, the hierarchical arrangement of multi-stage classification is most widely used for speech emotion recognition. In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme. The Sequential Forward Selection (SFS and Sequential Floating Forward Selection (SFFS techniques were employed for every stage of the multi-stage classification scheme. Experimental testing of the proposed scheme was performed using the German and Lithuanian emotional speech datasets. Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets. The multi-stage scheme has shown higher robustness to the growth of emotion set. The decrease in recognition rate with the increase in emotion set for multi-stage scheme was lower by 10–20 % in comparison with the single-stage case. Differences in SFS and SFFS employment for feature selection were negligible.

  14. Feature-Based Digital Modulation Recognition Using Compressive Sampling

    Directory of Open Access Journals (Sweden)

    Zhuo Sun

    2016-01-01

    Full Text Available Compressive sensing theory can be applied to reconstruct the signal with far fewer measurements than what is usually considered necessary, while in many scenarios, such as spectrum detection and modulation recognition, we only expect to acquire useful characteristics rather than the original signals, where selecting the feature with sparsity becomes the main challenge. With the aim of digital modulation recognition, the paper mainly constructs two features which can be recovered directly from compressive samples. The two features are the spectrum of received data and its nonlinear transformation and the compositional feature of multiple high-order moments of the received data; both of them have desired sparsity required for reconstruction from subsamples. Recognition of multiple frequency shift keying, multiple phase shift keying, and multiple quadrature amplitude modulation are considered in our paper and implemented in a unified procedure. Simulation shows that the two identification features can work effectively in the digital modulation recognition, even at a relatively low signal-to-noise ratio.

  15. Affordance-based 3D feature for generic object recognition

    Science.gov (United States)

    Iizuka, M.; Akizuki, S.; Hashimoto, M.

    2017-03-01

    Techniques for generic object recognition, which targets everyday objects such as cups and spoons, and techniques for approach vector estimation (e.g. estimating grasp position), which are needed for carrying out tasks involving everyday objects, are considered necessary for the perceptual system of service robots. In this research, we design feature for generic object recognition so they can also be applied to approach vector estimation. To carry out tasks involving everyday objects, estimating the function of the target object is critical. Also, as the function of holding liquid is found in all cups, so a function is shared in each type (class) of everyday objects. We thus propose a generic object recognition method that can estimate the approach vector by expressing an object's function as feature. In a test of the generic object recognition of everyday objects, we confirmed that our proposed method had a 92% recognition rate. This rate was 11% higher than the mainstream generic object recognition technique of using convolutional neural network (CNN).

  16. Robust kernel representation with statistical local features for face recognition.

    Science.gov (United States)

    Yang, Meng; Zhang, Lei; Shiu, Simon Chi-Keung; Zhang, David

    2013-06-01

    Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.

  17. A bio-inspired feature extraction for robust speech recognition.

    Science.gov (United States)

    Zouhir, Youssef; Ouni, Kaïs

    2014-01-01

    In this paper, a feature extraction method for robust speech recognition in noisy environments is proposed. The proposed method is motivated by a biologically inspired auditory model which simulates the outer/middle ear filtering by a low-pass filter and the spectral behaviour of the cochlea by the Gammachirp auditory filterbank (GcFB). The speech recognition performance of our method is tested on speech signals corrupted by real-world noises. The evaluation results show that the proposed method gives better recognition rates compared to the classic techniques such as Perceptual Linear Prediction (PLP), Linear Predictive Coding (LPC), Linear Prediction Cepstral coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC). The used recognition system is based on the Hidden Markov Models with continuous Gaussian Mixture densities (HMM-GM).

  18. Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

    Science.gov (United States)

    Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

    2017-06-09

    In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

  19. Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

    Directory of Open Access Journals (Sweden)

    Yuanshen Zhao

    2016-01-01

    Full Text Available Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the  a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.

  20. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

    Directory of Open Access Journals (Sweden)

    Pavol Partila

    2015-01-01

    Full Text Available The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

  1. Optimal Features Subset Selection and Classification for Iris Recognition

    Directory of Open Access Journals (Sweden)

    Prabir Bhattacharya

    2008-06-01

    Full Text Available The selection of the optimal features subset and the classification have become an important issue in the field of iris recognition. We propose a feature selection scheme based on the multiobjectives genetic algorithm (MOGA to improve the recognition accuracy and asymmetrical support vector machine for the classification of iris patterns. We also suggest a segmentation scheme based on the collarette area localization. The deterministic feature sequence is extracted from the iris images using the 1D log-Gabor wavelet technique, and the extracted feature sequence is used to train the support vector machine (SVM. The MOGA is applied to optimize the features sequence and to increase the overall performance based on the matching accuracy of the SVM. The parameters of SVM are optimized to improve the overall generalization performance, and the traditional SVM is modified to an asymmetrical SVM to treat the false accept and false reject cases differently and to handle the unbalanced data of a specific class with respect to the other classes. Our experimental results indicate that the performance of SVM as a classifier is better than the performance of the classifiers based on the feedforward neural network, the k-nearest neighbor, and the Hamming and the Mahalanobis distances. The proposed technique is computationally effective with recognition rates of 99.81% and 96.43% on CASIA and ICE datasets, respectively.

  2. RESEARCH ON FOREST FLAME RECOGNITION ALGORITHM BASED ON IMAGE FEATURE

    Directory of Open Access Journals (Sweden)

    Z. Wang

    2017-09-01

    Full Text Available In recent years, fire recognition based on image features has become a hotspot in fire monitoring. However, due to the complexity of forest environment, the accuracy of forest fireworks recognition based on image features is low. Based on this, this paper proposes a feature extraction algorithm based on YCrCb color space and K-means clustering. Firstly, the paper prepares and analyzes the color characteristics of a large number of forest fire image samples. Using the K-means clustering algorithm, the forest flame model is obtained by comparing the two commonly used color spaces, and the suspected flame area is discriminated and extracted. The experimental results show that the extraction accuracy of flame area based on YCrCb color model is higher than that of HSI color model, which can be applied in different scene forest fire identification, and it is feasible in practice.

  3. Research on Forest Flame Recognition Algorithm Based on Image Feature

    Science.gov (United States)

    Wang, Z.; Liu, P.; Cui, T.

    2017-09-01

    In recent years, fire recognition based on image features has become a hotspot in fire monitoring. However, due to the complexity of forest environment, the accuracy of forest fireworks recognition based on image features is low. Based on this, this paper proposes a feature extraction algorithm based on YCrCb color space and K-means clustering. Firstly, the paper prepares and analyzes the color characteristics of a large number of forest fire image samples. Using the K-means clustering algorithm, the forest flame model is obtained by comparing the two commonly used color spaces, and the suspected flame area is discriminated and extracted. The experimental results show that the extraction accuracy of flame area based on YCrCb color model is higher than that of HSI color model, which can be applied in different scene forest fire identification, and it is feasible in practice.

  4. Robust Face Recognition Via Gabor Feature and Sparse Representation

    Directory of Open Access Journals (Sweden)

    Hao Yu-Juan

    2016-01-01

    Full Text Available Sparse representation based on compressed sensing theory has been widely used in the field of face recognition, and has achieved good recognition results. but the face feature extraction based on sparse representation is too simple, and the sparse coefficient is not sparse. In this paper, we improve the classification algorithm based on the fusion of sparse representation and Gabor feature, and then improved algorithm for Gabor feature which overcomes the problem of large dimension of the vector dimension, reduces the computation and storage cost, and enhances the robustness of the algorithm to the changes of the environment.The classification efficiency of sparse representation is determined by the collaborative representation,we simplify the sparse constraint based on L1 norm to the least square constraint, which makes the sparse coefficients both positive and reduce the complexity of the algorithm. Experimental results show that the proposed method is robust to illumination, facial expression and pose variations of face recognition, and the recognition rate of the algorithm is improved.

  5. Haar-like Rectangular Features for Biometric Recognition

    DEFF Research Database (Denmark)

    Nasrollahi, Kamal; Moeslund, Thomas B.; Rashidi, Maryam

    2013-01-01

    , which mostly have been used for detection, for biometric recognition. The proposed system has been tested for three different biometrics: ear, iris, and hand vein patterns and it is shown that it is robust against most of the mentioned degradations and it outperforms state-of-the-art systems......Developing a reliable, fast, and robust biometric recognition system is still a challenging task. This is because the inputs to these systems can be noisy, occluded, poorly illuminated, rotated, and of very low-resolutions. This paper proposes a probabilistic classifier using Haar-like features...

  6. Face Recognition Performance Improvement using a Similarity Score of Feature Vectors based on Probabilistic Histograms

    Directory of Open Access Journals (Sweden)

    SRIKOTE, G.

    2016-08-01

    Full Text Available This paper proposes an improved performance algorithm of face recognition to identify two face mismatch pairs in cases of incorrect decisions. The primary feature of this method is to deploy the similarity score with respect to Gaussian components between two previously unseen faces. Unlike the conventional classical vector distance measurement, our algorithms also consider the plot of summation of the similarity index versus face feature vector distance. A mixture of Gaussian models of labeled faces is also widely applicable to different biometric system parameters. By comparative evaluations, it has been shown that the efficiency of the proposed algorithm is superior to that of the conventional algorithm by an average accuracy of up to 1.15% and 16.87% when compared with 3x3 Multi-Region Histogram (MRH direct-bag-of-features and Principal Component Analysis (PCA-based face recognition systems, respectively. The experimental results show that similarity score consideration is more discriminative for face recognition compared to feature distance. Experimental results of Labeled Face in the Wild (LFW data set demonstrate that our algorithms are suitable for real applications probe-to-gallery identification of face recognition systems. Moreover, this proposed method can also be applied to other recognition systems and therefore additionally improves recognition scores.

  7. The Importance of Visual Features in Generic versus Specialized Object Recognition: A Computational Study

    Directory of Open Access Journals (Sweden)

    Masoud eGhodrati

    2014-08-01

    Full Text Available It is debated whether the representation of objects in inferior temporal (IT cortex is distributed over activities of many neurons or there are restricted islands of neurons responsive to a specific set of objects. There are lines of evidence demonstrating that fusiform face area (FFA-in human processes information related to specialized object recognition (here we say within category object recognition such as face identification. Physiological studies have also discovered several patches in monkey ventral temporal lobe that are responsible for facial processing. Neuronal recording from these patches shows that neurons are highly selective for face images whereas for other objects we do not see such selectivity in IT. However, it is also well-supported that objects are encoded through distributed patterns of neural activities that are distinctive for each object category. It seems that visual cortex utilize different mechanisms for between category object recognition (e.g. face vs. non-face objects versus within category object recognition (e.g. two different faces. In this study, we address this question with computational simulations. We use two biologically inspired object recognition models (one proposed in our group and define two experiments which address these issues. The models have a hierarchical structure of several processing layers that simply simulate visual processing from V1 to aIT. We show, through computational modeling, that the difference between these two mechanisms of recognition can underlie the visual feature and extraction mechanism. It is argued that in order to perform generic and specialized object recognition, visual cortex must separate the mechanisms involved in within category from between categories object recognition. High recognition performance in within category object recognition can be guaranteed when class-specific features with intermediate size and complexity are extracted. However, generic object

  8. Automatic Target Recognition in Synthetic Aperture Sonar Images Based on Geometrical Feature Extraction

    Directory of Open Access Journals (Sweden)

    J. Del Rio Vera

    2009-01-01

    Full Text Available This paper presents a new supervised classification approach for automated target recognition (ATR in SAS images. The recognition procedure starts with a novel segmentation stage based on the Hilbert transform. A number of geometrical features are then extracted and used to classify observed objects against a previously compiled database of target and non-target features. The proposed approach has been tested on a set of 1528 simulated images created by the NURC SIGMAS sonar model, achieving up to 95% classification accuracy.

  9. 3D facial expression recognition using maximum relevance minimum redundancy geometrical features

    Science.gov (United States)

    Rabiu, Habibu; Saripan, M. Iqbal; Mashohor, Syamsiah; Marhaban, Mohd Hamiruce

    2012-12-01

    In recent years, facial expression recognition (FER) has become an attractive research area, which besides the fundamental challenges, it poses, finds application in areas, such as human-computer interaction, clinical psychology, lie detection, pain assessment, and neurology. Generally the approaches to FER consist of three main steps: face detection, feature extraction and expression recognition. The recognition accuracy of FER hinges immensely on the relevance of the selected features in representing the target expressions. In this article, we present a person and gender independent 3D facial expression recognition method, using maximum relevance minimum redundancy geometrical features. The aim is to detect a compact set of features that sufficiently represents the most discriminative features between the target classes. Multi-class one-against-one SVM classifier was employed to recognize the seven facial expressions; neutral, happy, sad, angry, fear, disgust, and surprise. The average recognition accuracy of 92.2% was recorded. Furthermore, inter database homogeneity was investigated between two independent databases the BU-3DFE and UPM-3DFE the results showed a strong homogeneity between the two databases.

  10. Volume Decomposition and Feature Recognition for Hexahedral Mesh Generation

    Energy Technology Data Exchange (ETDEWEB)

    GADH,RAJIT; LU,YONG; TAUTGES,TIMOTHY J.

    1999-09-27

    Considerable progress has been made on automatic hexahedral mesh generation in recent years. Several automatic meshing algorithms have proven to be very reliable on certain classes of geometry. While it is always worth pursuing general algorithms viable on more general geometry, a combination of the well-established algorithms is ready to take on classes of complicated geometry. By partitioning the entire geometry into meshable pieces matched with appropriate meshing algorithm the original geometry becomes meshable and may achieve better mesh quality. Each meshable portion is recognized as a meshing feature. This paper, which is a part of the feature based meshing methodology, presents the work on shape recognition and volume decomposition to automatically decompose a CAD model into meshable volumes. There are four phases in this approach: (1) Feature Determination to extinct decomposition features, (2) Cutting Surfaces Generation to form the ''tailored'' cutting surfaces, (3) Body Decomposition to get the imprinted volumes; and (4) Meshing Algorithm Assignment to match volumes decomposed with appropriate meshing algorithms. The feature determination procedure is based on the CLoop feature recognition algorithm that is extended to be more general. Results are demonstrated over several parts with complicated topology and geometry.

  11. Evaluation of surface EMG features for the recognition of American Sign Language gestures.

    Science.gov (United States)

    Kosmidou, Vasiliki E; Hadjileontiadis, Leontios J; Panas, Stavros M

    2006-01-01

    In this work, analysis of the surface electromyogram (sEMG) signal is proposed for the recognition of American sign language (ASL) gestures. To this purpose, sixteen features are extracted from the sEMG signal acquired from the user's forearm, and evaluated by the Mahalanobis distance criterion. Discriminant analysis is used to reduce the number of features used in the classification of the signed ASL gestures. The proposed features are tested against noise resulting in a further reduced set of features, which are evaluated for their discriminant ability. The classification results reveal that 97.7% of the inspected ASL gestures were correctly recognized using sEMG-based features, providing a promising solution to the automatic ASL gesture recognition problem.

  12. Excavation Equipment Recognition Based on Novel Acoustic Statistical Features.

    Science.gov (United States)

    Cao, Jiuwen; Wang, Wei; Wang, Jianzhong; Wang, Ruirong

    2017-12-01

    Excavation equipment recognition attracts increasing attentions in recent years due to its significance in underground pipeline network protection and civil construction management. In this paper, a novel classification algorithm based on acoustics processing is proposed for four representative excavation equipments. New acoustic statistical features, namely, the short frame energy ratio, concentration of spectrum amplitude ratio, truncated energy range, and interval of pulse are first developed to characterize acoustic signals. Then, probability density distributions of these acoustic features are analyzed and a novel classifier is presented. Experiments on real recorded acoustics of the four excavation devices are conducted to demonstrate the effectiveness of the proposed algorithm. Comparisons with two popular machine learning methods, support vector machine and extreme learning machine, combined with the popular linear prediction cepstral coefficients are provided to show the generalization capability of our method. A real surveillance system using our algorithm is developed and installed in a metro construction site for real-time recognition performance validation.

  13. Appearance-based human gesture recognition using multimodal features for human computer interaction

    Science.gov (United States)

    Luo, Dan; Gao, Hua; Ekenel, Hazim Kemal; Ohya, Jun

    2011-03-01

    The use of gesture as a natural interface plays an utmost important role for achieving intelligent Human Computer Interaction (HCI). Human gestures include different components of visual actions such as motion of hands, facial expression, and torso, to convey meaning. So far, in the field of gesture recognition, most previous works have focused on the manual component of gestures. In this paper, we present an appearance-based multimodal gesture recognition framework, which combines the different groups of features such as facial expression features and hand motion features which are extracted from image frames captured by a single web camera. We refer 12 classes of human gestures with facial expression including neutral, negative and positive meanings from American Sign Languages (ASL). We combine the features in two levels by employing two fusion strategies. At the feature level, an early feature combination can be performed by concatenating and weighting different feature groups, and LDA is used to choose the most discriminative elements by projecting the feature on a discriminative expression space. The second strategy is applied on decision level. Weighted decisions from single modalities are fused in a later stage. A condensation-based algorithm is adopted for classification. We collected a data set with three to seven recording sessions and conducted experiments with the combination techniques. Experimental results showed that facial analysis improve hand gesture recognition, decision level fusion performs better than feature level fusion.

  14. A multi-approach feature extractions for iris recognition

    Science.gov (United States)

    Sanpachai, H.; Settapong, M.

    2014-04-01

    Biometrics is a promising technique that is used to identify individual traits and characteristics. Iris recognition is one of the most reliable biometric methods. As iris texture and color is fully developed within a year of birth, it remains unchanged throughout a person's life. Contrary to fingerprint, which can be altered due to several aspects including accidental damage, dry or oily skin and dust. Although iris recognition has been studied for more than a decade, there are limited commercial products available due to its arduous requirement such as camera resolution, hardware size, expensive equipment and computational complexity. However, at the present time, technology has overcome these obstacles. Iris recognition can be done through several sequential steps which include pre-processing, features extractions, post-processing, and matching stage. In this paper, we adopted the directional high-low pass filter for feature extraction. A box-counting fractal dimension and Iris code have been proposed as feature representations. Our approach has been tested on CASIA Iris Image database and the results are considered successful.

  15. Feature Recognition of Froth Images Based on Energy Distribution Characteristics

    Directory of Open Access Journals (Sweden)

    WU Yanpeng

    2014-09-01

    Full Text Available This paper proposes a determining algorithm for froth image features based on the amplitude spectrum energy statistics by applying Fast Fourier Transformation to analyze the energy distribution of various-sized froth. The proposed algorithm has been used to do a froth feature analysis of the froth images from the alumina flotation processing site, and the results show that the consistency rate reaches 98.1 % and the usability rate 94.2 %; with its good robustness and high efficiency, the algorithm is quite suitable for flotation processing state recognition.

  16. Distance sets for shape filters and shape recognition

    NARCIS (Netherlands)

    Grigorescu, Cosmin; Petkov, Nicolai

    2003-01-01

    We introduce a novel rich local descriptor of an image point, we call the (labeled) distance set, which is determined by the spatial arrangement of image features around that point. We describe a two-dimensional (2-D) visual object by the set of (labeled) distance sets associated with the feature

  17. Object Recognition using Feature- and Color-Based Methods

    Science.gov (United States)

    Duong, Tuan; Duong, Vu; Stubberud, Allen

    2008-01-01

    An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognition system. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power.

  18. Handwritten Chinese character recognition based on supervised competitive learning neural network and block-based relative fuzzy feature extraction

    Science.gov (United States)

    Sun, Limin; Wu, Shuanhu

    2005-02-01

    Offline handwritten chinese character recognition is still a difficult problem because of its large stroke changes, writing anomaly, and the difficulty for obtaining its stroke ranking information. Generally, offline handwritten chinese character can be divided into two procedures: feature extraction for capturing handwritten chinese character information and feature classifying for character recognition. In this paper, we proposed a new Chinese character recognition algorithm. In feature extraction part, we adopted elastic mesh dividing method for extracting the block features and its relative fuzzy features that utilized the relativities between different strokes and distribution probability of a stroke in its neighbor sub-blocks. In recognition part, we constructed a classifier based on a supervised competitive learning algorithm to train competitive learning neural network with the extracted features set. Experimental results show that the performance of our algorithm is encouraging and can be comparable to other algorithms.

  19. Finger-vein image recognition combining modified Hausdorff distance with minutiae feature matching.

    Science.gov (United States)

    Yu, Cheng-Bo; Qin, Hua-Feng; Cui, Yan-Zhe; Hu, Xiao-Qian

    2009-12-01

    In this paper, we propose a novel method for finger-vein recognition. We extract the features of the vein patterns for recognition. Then, the minutiae features included bifurcation points and ending points are extracted from these vein patterns. These feature points are used as a geometric representation of the vein patterns shape. Finally, the modified Hausdorff distance algorithm is provided to evaluate the identification ability among all possible relative positions of the vein patterns shape. This algorithm has been widely used for comparing point sets or edge maps since it does not require point correspondence. Experimental results show that these minutiae feature points can be used to perform personal verification tasks as a geometric representation of the vein patterns shape. Furthermore, by this developed method, we can achieve robust image matching under different lighting conditions.

  20. Feature Extraction and Selection Strategies for Automated Target Recognition

    Science.gov (United States)

    Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

    2010-01-01

    Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

  1. Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

    Science.gov (United States)

    Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

    2013-04-01

    In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

  2. Voice based Speaker Recognition Combining Acoustic and Stylistic Features

    Science.gov (United States)

    2008-01-01

    performance. The score is further norma - lized using T-NORM. SRI’s duration system consists of two separate sets of tokens, one set for the most fre...Pittsburgh, PA. Hermansky, H. and N. Morgan (1984). "RASTA Processing of Speech." IEEE Transac- tions on Speech and Audio 2: 578--589 author H...Learning. Kajarekar, S. (2005). Four Weightings and a Fusion: A Cepstral-SVM System for Speak- er Recognition. ASRU, San Juan, IEEE . Kajarekar, S., et al

  3. Treelets Binary Feature Retrieval for Fast Keypoint Recognition.

    Science.gov (United States)

    Zhu, Jianke; Wu, Chenxia; Chen, Chun; Cai, Deng

    2015-10-01

    Fast keypoint recognition is essential to many vision tasks. In contrast to the classification-based approaches, we directly formulate the keypoint recognition as an image patch retrieval problem, which enjoys the merit of finding the matched keypoint and its pose simultaneously. To effectively extract the binary features from each patch surrounding the keypoint, we make use of treelets transform that can group the highly correlated data together and reduce the noise through the local analysis. Treelets is a multiresolution analysis tool, which provides an orthogonal basis to reflect the geometry of the noise-free data. To facilitate the real-world applications, we have proposed two novel approaches. One is the convolutional treelets that capture the image patch information locally and globally while reducing the computational cost. The other is the higher-order treelets that reflect the relationship between the rows and columns within image patch. An efficient sub-signature-based locality sensitive hashing scheme is employed for fast approximate nearest neighbor search in patch retrieval. Experimental evaluations on both synthetic data and the real-world Oxford dataset have shown that our proposed treelets binary feature retrieval methods outperform the state-of-the-art feature descriptors and classification-based approaches.

  4. Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

    Science.gov (United States)

    Ibrahim, Wisam; Abadeh, Mohammad Saniee

    2017-05-21

    Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Statistical Feature Extraction and Recognition of Beverages Using Electronic Tongue

    Directory of Open Access Journals (Sweden)

    P. C. PANCHARIYA

    2010-01-01

    Full Text Available This paper describes an approach for extraction of features from data generated from an electronic tongue based on large amplitude pulse voltammetry. In this approach statistical features of the meaningful selected variables from current response signals are extracted and used for recognition of beverage samples. The proposed feature extraction approach not only reduces the computational complexity but also reduces the computation time and requirement of storage of data for the development of E-tongue for field applications. With the reduced information, a probabilistic neural network (PNN was trained for qualitative analysis of different beverages. Before the qualitative analysis of the beverages, the methodology has been tested for the basic artificial taste solutions i.e. sweet, sour, salt, bitter, and umami. The proposed procedure was compared with the more conventional and linear feature extraction technique employing principal component analysis combined with PNN. Using the extracted feature vectors, highly correct classification by PNN was achieved for eight types of juices and six types of soft drinks. The results indicated that the electronic tongue based on large amplitude pulse voltammetry with reduced feature was capable of discriminating not only basic artificial taste solutions but also the various sorts of the same type of natural beverages (fruit juices, vegetable juices, soft drinks, etc..

  6. Histogram of Oriented Gradient Based Gist Feature for Building Recognition

    Science.gov (United States)

    Cheng, Kaili; Yu, Zhezhou

    2016-01-01

    We proposed a new method of gist feature extraction for building recognition and named the feature extracted by this method as the histogram of oriented gradient based gist (HOG-gist). The proposed method individually computes the normalized histograms of multiorientation gradients for the same image with four different scales. The traditional approach uses the Gabor filters with four angles and four different scales to extract orientation gist feature vectors from an image. Our method, in contrast, uses the normalized histogram of oriented gradient as orientation gist feature vectors of the same image. These HOG-based orientation gist vectors, combined with intensity and color gist feature vectors, are the proposed HOG-gist vectors. In general, the HOG-gist contains four multiorientation histograms (four orientation gist feature vectors), and its texture description ability is stronger than that of the traditional gist using Gabor filters with four angles. Experimental results using Sheffield Buildings Database verify the feasibility and effectiveness of the proposed HOG-gist. PMID:27872639

  7. Histogram of Oriented Gradient Based Gist Feature for Building Recognition

    Directory of Open Access Journals (Sweden)

    Bin Li

    2016-01-01

    Full Text Available We proposed a new method of gist feature extraction for building recognition and named the feature extracted by this method as the histogram of oriented gradient based gist (HOG-gist. The proposed method individually computes the normalized histograms of multiorientation gradients for the same image with four different scales. The traditional approach uses the Gabor filters with four angles and four different scales to extract orientation gist feature vectors from an image. Our method, in contrast, uses the normalized histogram of oriented gradient as orientation gist feature vectors of the same image. These HOG-based orientation gist vectors, combined with intensity and color gist feature vectors, are the proposed HOG-gist vectors. In general, the HOG-gist contains four multiorientation histograms (four orientation gist feature vectors, and its texture description ability is stronger than that of the traditional gist using Gabor filters with four angles. Experimental results using Sheffield Buildings Database verify the feasibility and effectiveness of the proposed HOG-gist.

  8. Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks

    Directory of Open Access Journals (Sweden)

    Buzhou Tang

    2014-01-01

    Full Text Available Biomedical Named Entity Recognition (BNER, which extracts important entities such as genes and proteins, is a crucial step of natural language processing in the biomedical domain. Various machine learning-based approaches have been applied to BNER tasks and showed good performance. In this paper, we systematically investigated three different types of word representation (WR features for BNER, including clustering-based representation, distributional representation, and word embeddings. We selected one algorithm from each of the three types of WR features and applied them to the JNLPBA and BioCreAtIvE II BNER tasks. Our results showed that all the three WR algorithms were beneficial to machine learning-based BNER systems. Moreover, combining these different types of WR features further improved BNER performance, indicating that they are complementary to each other. By combining all the three types of WR features, the improvements in F-measure on the BioCreAtIvE II GM and JNLPBA corpora were 3.75% and 1.39%, respectively, when compared with the systems using baseline features. To the best of our knowledge, this is the first study to systematically evaluate the effect of three different types of WR features for BNER tasks.

  9. Articulation based admissible wavelet packet feature based on human cochlear frequency response for TIMIT speech recognition

    Directory of Open Access Journals (Sweden)

    Astik Biswas

    2014-12-01

    This paper deals with the speaker-independent Automatic Speech Recognition (ASR system for continuous speech. This Hidden Markov Model (HMM based ASR system was developed for English using recordings of four regions taken from TIMIT database. A new set of features were derived using wavelet packet transform’s multi-resolution capabilities and having an advantage of ERB filter based on the human cochlea. New set of wavelet features have shown significant improvements in the noisy environment, especially at low SNR values.

  10. Static gesture recognition using features extracted from skeletal data

    CSIR Research Space (South Africa)

    Mangera, R

    2013-12-01

    Full Text Available is used to cluster the data and the performance of the feature vectors is evaluated on a collected static dataset. The rest of this paper is ordered as follows: Section II de- scribes current gesture recognition systems. Section III details...: left shoulder (ls), right shoulder (rs), left elbow (le), right elbow (re), left hand (lh), right hand (rh) and head (he) joints. 1) Relative Joint Distances: For each pose, the 3-D dis- tance between each of the 6 arm joints and the head joint...

  11. Action recognition via cumulative histogram of multiple features

    Science.gov (United States)

    Yan, Xunshi; Luo, Yupin

    2011-01-01

    Spatial-temporal interest points (STIPs) are popular in human action recognition. However, they suffer from difficulties in determining size of codebook and losing much information during forming histograms. In this paper, spatial-temporal interest regions (STIRs) are proposed, which are based on STIPs and are capable of marking the locations of the most ``shining'' human body parts. In order to represent human actions, the proposed approach takes great advantages of multiple features, including STIRs, pyramid histogram of oriented gradients and pyramid histogram of oriented optical flows. To achieve this, cumulative histogram is used to integrate dynamic information in sequences and to form feature vectors. Furthermore, the widely used nearest neighbor and AdaBoost methods are employed as classification algorithms. Experiments on public datasets KTH, Weizmann and UCF sports show that the proposed approach achieves effective and robust results.

  12. A DFT-Based Method of Feature Extraction for Palmprint Recognition

    Science.gov (United States)

    Choge, H. Kipsang; Karungaru, Stephen G.; Tsuge, Satoru; Fukumi, Minoru

    Over the last quarter century, research in biometric systems has developed at a breathtaking pace and what started with the focus on the fingerprint has now expanded to include face, voice, iris, and behavioral characteristics such as gait. Palmprint is one of the most recent additions, and is currently the subject of great research interest due to its inherent uniqueness, stability, user-friendliness and ease of acquisition. This paper describes an effective and procedurally simple method of palmprint feature extraction specifically for palmprint recognition, although verification experiments are also conducted. This method takes advantage of the correspondences that exist between prominent palmprint features or objects in the spatial domain with those in the frequency or Fourier domain. Multi-dimensional feature vectors are formed by extracting a GA-optimized set of points from the 2-D Fourier spectrum of the palmprint images. The feature vectors are then used for palmprint recognition, before and after dimensionality reduction via the Karhunen-Loeve Transform (KLT). Experiments performed using palmprint images from the ‘PolyU Palmprint Database’ indicate that using a compact set of DFT coefficients, combined with KLT and data preprocessing, produces a recognition accuracy of more than 98% and can provide a fast and effective technique for personal identification.

  13. Environment Recognition for Digital Audio Forensics Using MPEG-7 and MEL Cepstral Features

    Science.gov (United States)

    Muhammad, Ghulam; Alghathbar, Khalid

    2011-07-01

    Environment recognition from digital audio for forensics application is a growing area of interest. However, compared to other branches of audio forensics, it is a less researched one. Especially less attention has been given to detect environment from files where foreground speech is present, which is a forensics scenario. In this paper, we perform several experiments focusing on the problems of environment recognition from audio particularly for forensics application. Experimental results show that the task is easier when audio files contain only environmental sound than when they contain both foreground speech and background environment. We propose a full set of MPEG-7 audio features combined with mel frequency cepstral coefficients (MFCCs) to improve the accuracy. In the experiments, the proposed approach significantly increases the recognition accuracy of environment sound even in the presence of high amount of foreground human speech.

  14. WORD BASED TAMIL SPEECH RECOGNITION USING TEMPORAL FEATURE BASED SEGMENTATION

    Directory of Open Access Journals (Sweden)

    A. Akila

    2015-05-01

    Full Text Available Speech recognition system requires segmentation of speech waveform into fundamental acoustic units. Segmentation is a process of decomposing the speech signal into smaller units. Speech segmentation could be done using wavelet, fuzzy methods, Artificial Neural Networks and Hidden Markov Model. Speech segmentation is a process of breaking continuous stream of sound into some basic units like words, phonemes or syllable that could be recognized. Segmentation could be used to distinguish different types of audio signals from large amount of audio data, often referred as audio classification. The speech segmentation can be divided into two categories based on whether the algorithm uses previous knowledge of data to process the speech. The categories are blind segmentation and aided segmentation.The major issues with the connected speech recognition algorithms were the vocabulary size will be larger with variation in the combination of words in the connected speech and the complexity of the algorithm is more to find the best match for the given test pattern. To overcome these issues, the connected speech has to be segmented into words using the attributes of speech. A methodology using the temporal feature Short Term Energy was proposed and compared with an existing algorithm called Dynamic Thresholding segmentation algorithm which uses spectrogram image of the connected speech for segmentation.

  15. Computational intelligence in multi-feature visual pattern recognition hand posture and face recognition using biologically inspired approaches

    CERN Document Server

    Pisharady, Pramod Kumar; Poh, Loh Ai

    2014-01-01

    This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds. The book has 3 parts. Part 1 describes various research issues in the field with a survey of the related literature. Part 2 presents computational intelligence based algorithms for feature selection and classification. The algorithms are discriminative and fast. The main application area considered is hand posture recognition. The book also discusses utility of these algorithms in other visual as well as non-visual pattern recognition tasks including face recognition, general object recognition and cancer / tumor classification. Part 3 presents biologically inspired algorithms for feature extraction. The visual cortex model based features discussed have invariance with respect to appearance and size of the hand, and provide good...

  16. Codebook Guided Feature-Preserving for Recognition-Oriented Image Retargeting.

    Science.gov (United States)

    Yan, Bo; Tan, Weimin; Li, Ke; Tian, Qi

    2017-05-01

    Traditional image resizing methods, such as uniform scaling and content-aware image retargeting, are designed to preserve the visually salient contents of an image while resizing it. In this paper, we propose a novel image resizing approach called recognition-oriented image retargeting. Its goal is to preserve the distinctive local features for recognition instead of the traditional visual saliency during resizing. Moreover, we also apply our approach to image matching and image retrieval applications to verify its performance. Meanwhile, using our approach to these applications is able to solve some of the challenging problems in their fields. In image matching application, we find that our approach shows promising preservation of local feature descriptors. In image retrieval task, extensive experiments on Oxford5K, Holidays, Paris, and Flickr100k data sets demonstrate that our approach consistently outperforms other image retargeting methods by large margins in the aspects of retrieval precision and query bits.

  17. Outer packet sets and feature prediction of computer virus

    Science.gov (United States)

    Zhang, Ling

    2014-10-01

    The packet sets model was proposed by Prof. Shi in 2008. A packet sets is a set pair composed of internal and outer packet sets, and it has dynamic characteristic. Using packet sets theory, this paper gives the feature prediction of computer virus based on outer packet sets. The concept of virus screening-filtering is given, furthermore, the virus screening-filtering order theorem, composite virus screening-filtering theorem and virus screening-filtering rule are presented. A prediction method of computer virus feature is given based on the results. The outer packet sets is a new tool in the research of the prediction of dynamic virus feature.

  18. The effects of digital signal processing features on children's speech recognition and loudness perception.

    Science.gov (United States)

    Crukley, Jeffery; Scollie, Susan D

    2014-03-01

    The purpose of this study was to determine the effects of hearing instruments set to Desired Sensation Level version 5 (DSL v5) hearing instrument prescription algorithm targets and equipped with directional microphones and digital noise reduction (DNR) on children's sentence recognition in noise performance and loudness perception in a classroom environment. Ten children (ages 8-17 years) with stable, congenital sensorineural hearing losses participated in the study. Participants were fitted bilaterally with behind-the-ear hearing instruments set to DSL v5 prescriptive targets. Sentence recognition in noise was evaluated using the Bamford-Kowal-Bench Speech in Noise Test (Niquette et al., 2003). Loudness perception was evaluated using a modified version of the Contour Test of Loudness Perception (Cox, Alexander, Taylor, & Gray, 1997). Children's sentence recognition in noise performance was significantly better when using directional microphones alone or in combination with DNR than when using omnidirectional microphones alone or in combination with DNR. Children's loudness ratings for sounds above 72 dB SPL were lowest when fitted with the DSL v5 Noise prescription combined with directional microphones. DNR use showed no effect on loudness ratings. Use of the DSL v5 Noise prescription with a directional microphone improved sentence recognition in noise performance and reduced loudness perception ratings for loud sounds relative to a typical clinical reference fitting with the DSL v5 Quiet prescription with no digital signal processing features enabled. Potential clinical strategies are discussed.

  19. Multi-Input Feature Combination in the Cepstral Domain for Practical Speech Recognition Systems

    Science.gov (United States)

    Obuchi, Yasunari; Hataoka, Nobuo

    In this paper we describe a new framework of feature combination in the cepstral domain for multi-input robust speech recognition. The general framework of working in the cepstral domain has various advantages over working in the time or hypothesis domain. It is stable, easy to maintain, and less expensive because it does not require precise calibration. It is also easy to configure in a complex speech recognition system. However, it is not straightforward to improve the recognition performance by increasing the number of inputs, and we introduce the concept of variance re-scaling to compensate the negative effect of averaging several input features. Finally, we propose to take another advantage of working in the cepstral domain. The speech can be modeled using hidden Markov models, and the model can be used as prior knowledge. This approach is formulated as a new algorithm, referred to as Hypothesis-Based Feature Combination. The effectiveness of various algorithms are evaluated using two sets of speech databases. We also refer to automatic optimization of some parameters in the proposed algorithms.

  20. Discovering highly informative feature set over high dimensions

    KAUST Repository

    Zhang, Chongsheng

    2012-11-01

    For many textual collections, the number of features is often overly large. These features can be very redundant, it is therefore desirable to have a small, succinct, yet highly informative collection of features that describes the key characteristics of a dataset. Information theory is one such tool for us to obtain this feature collection. With this paper, we mainly contribute to the improvement of efficiency for the process of selecting the most informative feature set over high-dimensional unlabeled data. We propose a heuristic theory for informative feature set selection from high dimensional data. Moreover, we design data structures that enable us to compute the entropies of the candidate feature sets efficiently. We also develop a simple pruning strategy that eliminates the hopeless candidates at each forward selection step. We test our method through experiments on real-world data sets, showing that our proposal is very efficient. © 2012 IEEE.

  1. performance evaluation of feature sets of minutiae quadruplets

    African Journals Online (AJOL)

    all datasets. The nineteen different feature sets were tested on three databases, FVC 2000 DB1,. 2002 DB3 and 2004 DB1 [5, 6, 7], to iden- tify which feature sets would give a good performance on all three databases. These three databases, one from each year, were chosen because the datasets of one database.

  2. Performance Evaluation of Feature Sets of Minutiae Quadruplets ...

    African Journals Online (AJOL)

    The features proposed in this paper are derived from minutiae quadruplets and are applicable in matching and indexing ngerprint images. In this work nineteen different possibilities of features were explored for indexing and the performances of some of the feature sets were mixed: some giving good performances on ...

  3. Using features of local densities, statistics and HMM toolkit (HTK for offline Arabic handwriting text recognition

    Directory of Open Access Journals (Sweden)

    El Moubtahij Hicham

    2017-12-01

    Full Text Available This paper presents an analytical approach of an offline handwritten Arabic text recognition system. It is based on the Hidden Markov Models (HMM Toolkit (HTK without explicit segmentation. The first phase is preprocessing, where the data is introduced in the system after quality enhancements. Then, a set of characteristics (features of local densities and features statistics are extracted by using the technique of sliding windows. Subsequently, the resulting feature vectors are injected to the Hidden Markov Model Toolkit (HTK. The simple database “Arabic-Numbers” and IFN/ENIT are used to evaluate the performance of this system. Keywords: Hidden Markov Models (HMM Toolkit (HTK, Sliding windows

  4. Human Skeleton Model Based Dynamic Features for Walking Speed Invariant Gait Recognition

    Directory of Open Access Journals (Sweden)

    Jure Kovač

    2014-01-01

    Full Text Available Humans are able to recognize small number of people they know well by the way they walk. This ability represents basic motivation for using human gait as the means for biometric identification. Such biometrics can be captured at public places from a distance without subject's collaboration, awareness, and even consent. Although current approaches give encouraging results, we are still far from effective use in real-life applications. In general, methods set various constraints to circumvent the influence of covariate factors like changes of walking speed, view, clothing, footwear, and object carrying, that have negative impact on recognition performance. In this paper we propose a skeleton model based gait recognition system focusing on modelling gait dynamics and eliminating the influence of subjects appearance on recognition. Furthermore, we tackle the problem of walking speed variation and propose space transformation and feature fusion that mitigates its influence on recognition performance. With the evaluation on OU-ISIR gait dataset, we demonstrate state of the art performance of proposed methods.

  5. Recognition of Pitman shorthand text using tangent feature values at ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    MS received 6 November 2000; revised 16 December 2002. Abstract. Recognition of text recorded in Pitman shorthand language (PSL) is an interesting research problem. Automatic reading of PSL and generating equivalent. English text is very challenging. The most important task involved here is the accurate recognition ...

  6. Recognition of Pitman shorthand text using tangent feature values at ...

    Indian Academy of Sciences (India)

    Recognition of text recorded in Pitman shorthand language (PSL) is an interesting research problem. Automatic reading of PSL and generating equivalent English text is very challenging. The most important task involved here is the accurate recognition of Pitman stroke patterns, which constitute “text” in PSL. The paper ...

  7. Cardinality as a Highly Descriptive Feature in Myoelectric Pattern Recognition for Decoding Motor Volition

    Directory of Open Access Journals (Sweden)

    Max eOrtiz-Catalan

    2015-10-01

    Full Text Available Accurate descriptors of muscular activity play an important role in clinical practice and rehabilitation research. Such descriptors are features of myoelectric signals extracted from sliding time windows. A wide variety of myoelectric features have been used as inputs to pattern recognition algorithms that aim to decode motor volition. The output of these algorithms can then be used to control limb prostheses, exoskeletons, and rehabilitation therapies. In the present study, cardinality is introduced and compared with traditional time-domain (Hudgins’ set and other recently proposed myoelectric features (for example, rough entropy. Cardinality was found to consistently outperform other features, including those that are more sophisticated and computationally expensive, despite variations in sampling frequency, time window length, contraction dynamics, type and number of movements (single or simultaneous, and classification algorithms. Provided that the signal resolution is kept between 12 and 14 bits, cardinality improves myoelectric pattern recognition for the prediction of motion volition. This technology is instrumental for the rehabilitation of amputees and patients with motor impairments where myoelectric signals are viable. All code and data used in this work is available online within BioPatRec.

  8. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Directory of Open Access Journals (Sweden)

    Liangji Zhou

    2017-01-01

    Full Text Available As a typical deep-learning model, Convolutional Neural Networks (CNNs can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases.

  9. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Science.gov (United States)

    Huo, Guanying

    2017-01-01

    As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

  10. Identity Recognition Algorithm Using Improved Gabor Feature Selection of Gait Energy Image

    Science.gov (United States)

    Chao, LIANG; Ling-yao, JIA; Dong-cheng, SHI

    2017-01-01

    This paper describes an effective gait recognition approach based on Gabor features of gait energy image. In this paper, the kernel Fisher analysis combined with kernel matrix is proposed to select dominant features. The nearest neighbor classifier based on whitened cosine distance is used to discriminate different gait patterns. The approach proposed is tested on the CASIA and USF gait databases. The results show that our approach outperforms other state of gait recognition approaches in terms of recognition accuracy and robustness.

  11. Featuring Old/New Recognition: The Two Faces of the Pseudoword Effect

    Science.gov (United States)

    Joordens, Steve; Ozubko, Jason D.; Niewiadomski, Marty W.

    2008-01-01

    In his analysis of the pseudoword effect, [Greene, R.L. (2004). Recognition memory for pseudowords. "Journal of Memory and Language," 50, 259-267.] suggests nonwords can feel more familiar that words in a recognition context if the orthographic features of the nonword match well with the features of the items presented at study. One possible…

  12. Automatic threshold selection for multi-class open set recognition

    Science.gov (United States)

    Scherreik, Matthew; Rigling, Brian

    2017-05-01

    Multi-class open set recognition is the problem of supervised classification with additional unknown classes encountered after a model has been trained. An open set classifer often has two core components. The first component is a base classifier which estimates the most likely class of a given example. The second component consists of open set logic which estimates if the example is truly a member of the candidate class. Such a system is operated in a feed-forward fashion. That is, a candidate label is first estimated by the base classifier, and the true membership of the example to the candidate class is estimated afterward. Previous works have developed an iterative threshold selection algorithm for rejecting examples from classes which were not present at training time. In those studies, a Platt-calibrated SVM was used as the base classifier, and the thresholds were applied to class posterior probabilities for rejection. In this work, we investigate the effectiveness of other base classifiers when paired with the threshold selection algorithm and compare their performance with the original SVM solution.

  13. A Novel DBN Feature Fusion Model for Cross-Corpus Speech Emotion Recognition

    Directory of Open Access Journals (Sweden)

    Zou Cairong

    2016-01-01

    Full Text Available The feature fusion from separate source is the current technical difficulties of cross-corpus speech emotion recognition. The purpose of this paper is to, based on Deep Belief Nets (DBN in Deep Learning, use the emotional information hiding in speech spectrum diagram (spectrogram as image features and then implement feature fusion with the traditional emotion features. First, based on the spectrogram analysis by STB/Itti model, the new spectrogram features are extracted from the color, the brightness, and the orientation, respectively; then using two alternative DBN models they fuse the traditional and the spectrogram features, which increase the scale of the feature subset and the characterization ability of emotion. Through the experiment on ABC database and Chinese corpora, the new feature subset compared with traditional speech emotion features, the recognition result on cross-corpus, distinctly advances by 8.8%. The method proposed provides a new idea for feature fusion of emotion recognition.

  14. Setting a world record in 3D face recognition

    NARCIS (Netherlands)

    Spreeuwers, Lieuwe Jan

    Biometrics - recognition of persons based on how they look or behave, is the main subject of research at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente. Examples are finger print recognition,

  15. Zernike moments features for shape-based gait recognition

    Science.gov (United States)

    Qin, Huanfeng; Qin, Lan; Liu, Jun; Chao, Jiang

    2011-12-01

    The paper proposes a new spatio-temporal gait representation, called cycles gait Zernike moments (CGZM), to characterize human walking properties for individual recognition. Firstly, Zernike moments as shape descriptors are used to characterize gait silhouette shape. Secondly, we generate CGZM from Zernike moments of silhouette sequences. Finally, the phase and magnitude coefficientsof CGZM are utilized to perform classification by the modified Hausdorff distance (MHD) classifier. Experimental results show that the proposed approach have an encouraging recognition performance.

  16. Feature selection in gene expression data using principal component analysis and rough set theory.

    Science.gov (United States)

    Mishra, Debahuti; Dash, Rajashree; Rath, Amiya Kumar; Acharya, Milu

    2011-01-01

    In many fields such as data mining, machine learning, pattern recognition and signal processing, data sets containing huge number of features are often involved. Feature selection is an essential data preprocessing technique for such high-dimensional data classification tasks. Traditional dimensionality reduction approach falls into two categories: Feature Extraction (FE) and Feature Selection (FS). Principal component analysis is an unsupervised linear FE method for projecting high-dimensional data into a low-dimensional space with minimum loss of information. It discovers the directions of maximal variances in the data. The Rough set approach to feature selection is used to discover the data dependencies and reduction in the number of attributes contained in a data set using the data alone, requiring no additional information. For selecting discriminative features from principal components, the Rough set theory can be applied jointly with PCA, which guarantees that the selected principal components will be the most adequate for classification. We call this method Rough PCA. The proposed method is successfully applied for choosing the principal features and then applying the Upper and Lower Approximations to find the reduced set of features from a gene expression data.

  17. A Reduced Set of Features for Chronic Kidney Disease Prediction.

    Science.gov (United States)

    Misir, Rajesh; Mitra, Malay; Samanta, Ranjit Kumar

    2017-01-01

    Chronic kidney disease (CKD) is one of the life-threatening diseases. Early detection and proper management are solicited for augmenting survivability. As per the UCI data set, there are 24 attributes for predicting CKD or non-CKD. At least there are 16 attributes need pathological investigations involving more resources, money, time, and uncertainties. The objective of this work is to explore whether we can predict CKD or non-CKD with reasonable accuracy using less number of features. An intelligent system development approach has been used in this study. We attempted one important feature selection technique to discover reduced features that explain the data set much better. Two intelligent binary classification techniques have been adopted for the validity of the reduced feature set. Performances were evaluated in terms of four important classification evaluation parameters. As suggested from our results, we may more concentrate on those reduced features for identifying CKD and thereby reduces uncertainty, saves time, and reduces costs.

  18. Emotion recognition based on multiple order features using fractional Fourier transform

    Science.gov (United States)

    Ren, Bo; Liu, Deyin; Qi, Lin

    2017-07-01

    In order to deal with the insufficiency of recently algorithms based on Two Dimensions Fractional Fourier Transform (2D-FrFT), this paper proposes a multiple order features based method for emotion recognition. Most existing methods utilize the feature of single order or a couple of orders of 2D-FrFT. However, different orders of 2D-FrFT have different contributions on the feature extraction of emotion recognition. Combination of these features can enhance the performance of an emotion recognition system. The proposed approach obtains numerous features that extracted in different orders of 2D-FrFT in the directions of x-axis and y-axis, and uses the statistical magnitudes as the final feature vectors for recognition. The Support Vector Machine (SVM) is utilized for the classification and RML Emotion database and Cohn-Kanade (CK) database are used for the experiment. The experimental results demonstrate the effectiveness of the proposed method.

  19. Level Sets and Voronoi based Feature Extraction from any Imagery

    DEFF Research Database (Denmark)

    Sharma, O.; Anton, François; Mioc, Darka

    2012-01-01

    Polygon features are of interest in many GEOProcessing applications like shoreline mapping, boundary delineation, change detection, etc. This paper presents a unique new GPU-based methodology to automate feature extraction combining level sets, or mean shift based segmentation together with Voronoi...

  20. Relevant test set using feature selection algorithm for early detection ...

    African Journals Online (AJOL)

    The objective of feature selection is to find the most relevant features for classification. Thus, the dimensionality of the information will be reduced and may improve classification's accuracy. This paper proposed a minimum set of relevant questions that can be used for early detection of dyslexia. In this research, we ...

  1. Uniform Local Binary Pattern Based Texture-Edge Feature for 3D Human Behavior Recognition

    Science.gov (United States)

    Ming, Yue; Wang, Guangchao; Fan, Chunxiao

    2015-01-01

    With the rapid development of 3D somatosensory technology, human behavior recognition has become an important research field. Human behavior feature analysis has evolved from traditional 2D features to 3D features. In order to improve the performance of human activity recognition, a human behavior recognition method is proposed, which is based on a hybrid texture-edge local pattern coding feature extraction and integration of RGB and depth videos information. The paper mainly focuses on background subtraction on RGB and depth video sequences of behaviors, extracting and integrating historical images of the behavior outlines, feature extraction and classification. The new method of 3D human behavior recognition has achieved the rapid and efficient recognition of behavior videos. A large number of experiments show that the proposed method has faster speed and higher recognition rate. The recognition method has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition. PMID:25942404

  2. Fast and efficient local features detection for building recognition

    DEFF Research Database (Denmark)

    Nguyen, Phuong Giang; Andersen, Hans Jørgen

    2011-01-01

    for invariant features; i.e. image features should have minimal differences under these conditions. Local image features in the form of key points are widely used because of their invariant properties. In this chapter, we analyze different issues relating to existing local feature detectors. Based...

  3. Cross-Subject EEG Feature Selection for Emotion Recognition Using Transfer Recursive Feature Elimination.

    Science.gov (United States)

    Yin, Zhong; Wang, Yongxiong; Liu, Li; Zhang, Wei; Zhang, Jianhua

    2017-01-01

    Using machine-learning methodologies to analyze EEG signals becomes increasingly attractive for recognizing human emotions because of the objectivity of physiological data and the capability of the learning principles on modeling emotion classifiers from heterogeneous features. However, the conventional subject-specific classifiers may induce additional burdens to each subject for preparing multiple-session EEG data as training sets. To this end, we developed a new EEG feature selection approach, transfer recursive feature elimination (T-RFE), to determine a set of the most robust EEG indicators with stable geometrical distribution across a group of training subjects and a specific testing subject. A validating set is introduced to independently determine the optimal hyper-parameter and the feature ranking of the T-RFE model aiming at controlling the overfitting. The effectiveness of the T-RFE algorithm for such cross-subject emotion classification paradigm has been validated by DEAP database. With a linear least square support vector machine classifier implemented, the performance of the T-RFE is compared against several conventional feature selection schemes and the statistical significant improvement has been found. The classification rate and F-score achieve 0.7867, 0.7526, 0.7875, and 0.8077 for arousal and valence dimensions, respectively, and outperform several recent reported works on the same database. In the end, the T-RFE based classifier is compared against two subject-generic classifiers in the literature. The investigation of the computational time for all classifiers indicates the accuracy improvement of the T-RFE is at the cost of the longer training time.

  4. Self-Organizing Neural Integration of Pose-Motion Features for Human Action Recognition

    Directory of Open Access Journals (Sweden)

    German Ignacio Parisi

    2015-06-01

    Full Text Available The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented towards human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR networks that obtain progressively generalized representations of sensory inputs and learn inherent spatiotemporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best 21 results for a public benchmark of domestic daily actions.

  5. An Efficient Multimodal 2D + 3D Feature-based Approach to Automatic Facial Expression Recognition

    KAUST Repository

    Li, Huibin

    2015-07-29

    We present a fully automatic multimodal 2D + 3D feature-based facial expression recognition approach and demonstrate its performance on the BU-3DFE database. Our approach combines multi-order gradient-based local texture and shape descriptors in order to achieve efficiency and robustness. First, a large set of fiducial facial landmarks of 2D face images along with their 3D face scans are localized using a novel algorithm namely incremental Parallel Cascade of Linear Regression (iPar-CLR). Then, a novel Histogram of Second Order Gradients (HSOG) based local image descriptor in conjunction with the widely used first-order gradient based SIFT descriptor are used to describe the local texture around each 2D landmark. Similarly, the local geometry around each 3D landmark is described by two novel local shape descriptors constructed using the first-order and the second-order surface differential geometry quantities, i.e., Histogram of mesh Gradients (meshHOG) and Histogram of mesh Shape index (curvature quantization, meshHOS). Finally, the Support Vector Machine (SVM) based recognition results of all 2D and 3D descriptors are fused at both feature-level and score-level to further improve the accuracy. Comprehensive experimental results demonstrate that there exist impressive complementary characteristics between the 2D and 3D descriptors. We use the BU-3DFE benchmark to compare our approach to the state-of-the-art ones. Our multimodal feature-based approach outperforms the others by achieving an average recognition accuracy of 86.32%. Moreover, a good generalization ability is shown on the Bosphorus database.

  6. Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature

    Directory of Open Access Journals (Sweden)

    Shouyi Yin

    2015-01-01

    Full Text Available Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.

  7. Fast traffic sign recognition with a rotation invariant binary pattern based feature.

    Science.gov (United States)

    Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun

    2015-01-19

    Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.

  8. Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

    Directory of Open Access Journals (Sweden)

    S. Mala

    2014-01-01

    Full Text Available Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE, a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG. Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

  9. Multimodal Approach for Face Recognition using 3D-2D Face Feature Fusion

    OpenAIRE

    Naveen S; Dr. R. S. MONI

    2014-01-01

    3D Face recognition has been an area of interest among researchers for the past few decades especially in pattern recognition. The main advantage of 3D Face recognition is the availability of geometrical information of the face structure which is more or less unique for a subject. This paper focuses on the problems of person identification using 3D Face data. Use of unregistered 3D Face data for feature extraction significantly increases the operational speed of the system with huge database ...

  10. Human activity recognition based on feature selection in smart home using back-propagation algorithm.

    Science.gov (United States)

    Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei

    2014-09-01

    In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  11. Characteristics of Mandarin Open-set Word Recognition Development among Chinese Children with Cochlear Implants

    Directory of Open Access Journals (Sweden)

    Ying Kong

    2017-01-01

    Conclusions: Mandarin open-set word recognition increased with time after CI implantation, and the age at implantation had a significant effect on long-term speech recognition. Chinese children with CIs had delayed but similar development of recognition, compared to normal children. Early CI implantation can shorten the gap between children with CIs and normal children.

  12. A Feature Set for Cytometry on Digitized Microscopic Images

    Directory of Open Access Journals (Sweden)

    Karsten Rodenacker

    2003-01-01

    Full Text Available Feature extraction is a crucial step in most cytometry studies. In this paper a systematic approach to feature extraction is presented. The feature sets that have been developed and used for quantitative cytology at the Laboratory for Biomedical Image Analysis of the GSF as well as at the Center for Image Analysis in Uppsala over the last 25 years are described and illustrated. The feature sets described are divided into morphometric, densitometric, textural and structural features. The latter group is used to describe the eu‐ and hetero‐chromatin in a way complementing the textural methods. The main goal of the paper is to bring attention to the need of a common and well defined description of features used in cyto‐ and histometrical studies. The application of the sets of features is shown in an overview of projects from different fields. Finally some rules of thumb for the design of studies in this field are proposed. Colour figures can be viewed on http://www.esacp.org/acp/2003/25‐1/rodenacker.htm.

  13. Open set recognition of aircraft in aerial imagery using synthetic template models

    Science.gov (United States)

    Bapst, Aleksander B.; Tran, Jonathan; Koch, Mark W.; Moya, Mary M.; Swahn, Robert

    2017-05-01

    Fast, accurate and robust automatic target recognition (ATR) in optical aerial imagery can provide game-changing advantages to military commanders and personnel. ATR algorithms must reject non-targets with a high degree of confidence in a world with an infinite number of possible input images. Furthermore, they must learn to recognize new targets without requiring massive data collections. Whereas most machine learning algorithms classify data in a closed set manner by mapping inputs to a fixed set of training classes, open set recognizers incorporate constraints that allow for inputs to be labelled as unknown. We have adapted two template-based open set recognizers to use computer generated synthetic images of military aircraft as training data, to provide a baseline for military-grade ATR: (1) a frequentist approach based on probabilistic fusion of extracted image features, and (2) an open set extension to the one-class support vector machine (SVM). These algorithms both use histograms of oriented gradients (HOG) as features as well as artificial augmentation of both real and synthetic image chips to take advantage of minimal training data. Our results show that open set recognizers trained with synthetic data and tested with real data can successfully discriminate real target inputs from non-targets. However, there is still a requirement for some knowledge of the real target in order to calibrate the relationship between synthetic template and target score distributions. We conclude by proposing algorithm modifications that may improve the ability of synthetic data to represent real data.

  14. Shape features for recognition of Pap smear cells

    Science.gov (United States)

    Goggin, Shelly D. D.; Janson, Scott D.

    1996-10-01

    Automated cytology relies on the use of features extracted form cell images to classify cells. This paper examines the classification capability of a number of shape features on a database of normal, abnormal and endocervical cell nuclei images. The features include the chain code, the directed Hausdorff distance, measured of the length of the radii of the cell and measures of ellipticity. The area under the receiver operating characteristic curve is used as a figure of merit. For the calculation of the directed Hausdorff distance, the images are filtered using the Sorbel gradient and erosion. The feature in the image with the largest chain code is considered to be the nucleus. The other features use images threshold at a percentage of the maximum intensity in the image. The best feature for the discrimination between normal cells and either abnormal or endocervical cells was the directed Hausdorff distance, but this feature is computationally expensive. The minimum diameter as determined by the chain code was the second best feature for recognizing abnormal cells and is less computationally expensive. Ellipticity was the second best feature for recognizing endocervical cells, which is also less computationally expensive than the directed Hausdorff distance. An optical design for the calculation of directed Hausdorff distance feature is included, which could reduce the computational expense.

  15. Canonicalization of Feature Parameters for Robust Speech Recognition Based on Distinctive Phonetic Feature (DPF) Vectors

    Science.gov (United States)

    Huda, Mohammad Nurul; Ghulam, Muhammad; Fukuda, Takashi; Katsurada, Kouichi; Nitta, Tsuneo

    This paper describes a robust automatic speech recognition (ASR) system with less computation. Acoustic models of a hidden Markov model (HMM)-based classifier include various types of hidden factors such as speaker-specific characteristics, coarticulation, and an acoustic environment, etc. If there exists a canonicalization process that can recover the degraded margin of acoustic likelihoods between correct phonemes and other ones caused by hidden factors, the robustness of ASR systems can be improved. In this paper, we introduce a canonicalization method that is composed of multiple distinctive phonetic feature (DPF) extractors corresponding to each hidden factor canonicalization, and a DPF selector which selects an optimum DPF vector as an input of the HMM-based classifier. The proposed method resolves gender factors and speaker variability, and eliminates noise factors by applying the canonicalzation based on the DPF extractors and two-stage Wiener filtering. In the experiment on AURORA-2J, the proposed method provides higher word accuracy under clean training and significant improvement of word accuracy in low signal-to-noise ratio (SNR) under multi-condition training compared to a standard ASR system with mel frequency ceptral coeffient (MFCC) parameters. Moreover, the proposed method requires a reduced, two-fifth, Gaussian mixture components and less memory to achieve accurate ASR.

  16. Study on Feature Subspace of Archetypal Emotions for Speech Emotion Recognition

    OpenAIRE

    Ma, Xi; Wu, Zhiyong; Jia, Jia; Xu, Mingxing; Meng, Helen; Cai, Lianhong

    2016-01-01

    Feature subspace selection is an important part in speech emotion recognition. Most of the studies are devoted to finding a feature subspace for representing all emotions. However, some studies have indicated that the features associated with different emotions are not exactly the same. Hence, traditional methods may fail to distinguish some of the emotions with just one global feature subspace. In this work, we propose a new divide and conquer idea to solve the problem. First, the feature su...

  17. Four-Channel Biosignal Analysis and Feature Extraction for Automatic Emotion Recognition

    Science.gov (United States)

    Kim, Jonghwa; André, Elisabeth

    This paper investigates the potential of physiological signals as a reliable channel for automatic recognition of user's emotial state. For the emotion recognition, little attention has been paid so far to physiological signals compared to audio-visual emotion channels such as facial expression or speech. All essential stages of automatic recognition system using biosignals are discussed, from recording physiological dataset up to feature-based multiclass classification. Four-channel biosensors are used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to search the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by emotion recognition results.

  18. Feature Acquisition and Analysis for Facial Expression Recognition Using Convolutional Neural Networks

    National Research Council Canada - National Science Library

    Taiki Nishime; Satoshi Endo; Naruaki Toma; Koji Yamada; Yuhei Akamine

    2017-01-01

    .... Therefore, it is difficult to evaluate the reliability of the result from recognition accuracy alone, and the analysis for explaining the result and feature learned by Convolutional Neural Networks (CNN...

  19. Recognition during recall failure: Semantic feature matching as a mechanism for recognition of semantic cues when recall fails.

    Science.gov (United States)

    Cleary, Anne M; Ryals, Anthony J; Wagner, Samantha R

    2016-01-01

    Research suggests that a feature-matching process underlies cue familiarity-detection when cued recall with graphemic cues fails. When a test cue (e.g., potchbork) overlaps in graphemic features with multiple unrecalled studied items (e.g., patchwork, pitchfork, pocketbook, pullcork), higher cue familiarity ratings are given during recall failure of all of the targets than when the cue overlaps in graphemic features with only one studied target and that target fails to be recalled (e.g., patchwork). The present study used semantic feature production norms (McRae et al., Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005) to examine whether the same holds true when the cues are semantic in nature (e.g., jaguar is used to cue cheetah). Indeed, test cues (e.g., cedar) that overlapped in semantic features (e.g., a_tree, has_bark, etc.) with four unretrieved studied items (e.g., birch, oak, pine, willow) received higher cue familiarity ratings during recall failure than test cues that overlapped in semantic features with only two (also unretrieved) studied items (e.g., birch, oak), which in turn received higher familiarity ratings during recall failure than cues that did not overlap in semantic features with any studied items. These findings suggest that the feature-matching theory of recognition during recall failure can accommodate recognition of semantic cues during recall failure, providing a potential mechanism for conceptually-based forms of cue recognition during target retrieval failure. They also provide converging evidence for the existence of the semantic features envisaged in feature-based models of semantic knowledge representation and for those more concretely specified by the production norms of McRae et al. (Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005).

  20. Comparing Shape and Texture Features for Pattern Recognition in Simulation Data

    Energy Technology Data Exchange (ETDEWEB)

    Newsam, S; Kamath, C

    2004-12-10

    Shape and texture features have been used for some time for pattern recognition in datasets such as remote sensed imagery, medical imagery, photographs, etc. In this paper, we investigate shape and texture features for pattern recognition in simulation data. In particular, we explore which features are suitable for characterizing regions of interest in images resulting from fluid mixing simulations. Three texture features--gray level co-occurrence matrices, wavelets, and Gabor filters--and two shape features--geometric moments and the angular radial transform--are compared. The features are evaluated using a similarity retrieval framework. Our preliminary results indicate that Gabor filters perform the best among the texture features and the angular radial transform performs the best among the shape features. The feature which performs the best overall is dependent on how the groundtruth dataset is created.

  1. Freeform feature recognition and manipulation to support shape design

    NARCIS (Netherlands)

    Langerak, T.R.

    2008-01-01

    Freeform features are parameterizable shape parts that are used in the design of industrial products. The parametric nature of the feature allows a designer to quickly manipulate shape without having to precisely configure the geometry of the shape. However, in many cases, designers want to use

  2. Defects' geometric feature recognition based on infrared image edge detection

    Science.gov (United States)

    Junyan, Liu; Qingju, Tang; Yang, Wang; Yumei, Lu; Zhiping, Zhang

    2014-11-01

    Edge detection is an important technology in image segmentation, feature extraction and other digital image processing areas. Boundary contains a wealth of information in the image, so to extract defects' edges in infrared images effectively enables the identification of defects' geometric features. This paper analyzed the detection effect of classic edge detection operators, and proposed fuzzy C-means (FCM) clustering-Canny operator algorithm to achieve defects' edges in the infrared images. Results show that the proposed algorithm has better effect than the classic edge detection operators, which can identify the defects' geometric feature much more completely and clearly. The defects' diameters have been calculated based on the image edge detection results.

  3. Image processing tool for automatic feature recognition and quantification

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Xing; Stoddard, Ryan J.

    2017-05-02

    A system for defining structures within an image is described. The system includes reading of an input file, preprocessing the input file while preserving metadata such as scale information and then detecting features of the input file. In one version the detection first uses an edge detector followed by identification of features using a Hough transform. The output of the process is identified elements within the image.

  4. FAST DISCRETE CURVELET TRANSFORM BASED ANISOTROPIC FEATURE EXTRACTION FOR IRIS RECOGNITION

    Directory of Open Access Journals (Sweden)

    Amol D. Rahulkar

    2010-11-01

    Full Text Available The feature extraction plays a very important role in iris recognition. Recent researches on multiscale analysis provide good opportunity to extract more accurate information for iris recognition. In this work, a new directional iris texture features based on 2-D Fast Discrete Curvelet Transform (FDCT is proposed. The proposed approach divides the normalized iris image into six sub-images and the curvelet transform is applied independently on each sub-image. The anisotropic feature vector for each sub-image is derived using the directional energies of the curvelet coefficients. These six feature vectors are combined to create the resultant feature vector. During recognition, the nearest neighbor classifier based on Euclidean distance has been used for authentication. The effectiveness of the proposed approach has been tested on two different databases namely UBIRIS and MMU1. Experimental results show the superiority of the proposed approach.

  5. Feature Extraction for Facial Expression Recognition based on Hybrid Face Regions

    Directory of Open Access Journals (Sweden)

    LAJEVARDI, S.M.

    2009-10-01

    Full Text Available Facial expression recognition has numerous applications, including psychological research, improved human computer interaction, and sign language translation. A novel facial expression recognition system based on hybrid face regions (HFR is investigated. The expression recognition system is fully automatic, and consists of the following modules: face detection, facial detection, feature extraction, optimal features selection, and classification. The features are extracted from both whole face image and face regions (eyes and mouth using log Gabor filters. Then, the most discriminate features are selected based on mutual information criteria. The system can automatically recognize six expressions: anger, disgust, fear, happiness, sadness and surprise. The selected features are classified using the Naive Bayesian (NB classifier. The proposed method has been extensively assessed using Cohn-Kanade database and JAFFE database. The experiments have highlighted the efficiency of the proposed HFR method in enhancing the classification rate.

  6. Feature Encodings and Poolings for Action and Event Recognition: A Comprehensive Survey

    Directory of Open Access Journals (Sweden)

    Changyu Liu

    2017-10-01

    Full Text Available Action and event recognition in multimedia collections is relevant to progress in cross-disciplinary research areas including computer vision, computational optimization, statistical learning, and nonlinear dynamics. Over the past two decades, action and event recognition has evolved from earlier intervening strategies under controlled environments to recent automatic solutions under dynamic environments, resulting in an imperative requirement to effectively organize spatiotemporal deep features. Consequently, resorting to feature encodings and poolings for action and event recognition in complex multimedia collections is an inevitable trend. The purpose of this paper is to offer a comprehensive survey on the most popular feature encoding and pooling approaches in action and event recognition in recent years by summarizing systematically both underlying theoretical principles and original experimental conclusions of those approaches based on an approach-based taxonomy, so as to provide impetus for future relevant studies.

  7. Combining Semantic and Acoustic Features for Valence and Arousal Recognition in Speech

    DEFF Research Database (Denmark)

    Karadogan, Seliz; Larsen, Jan

    2012-01-01

    The recognition of affect in speech has attracted a lot of interest recently; especially in the area of cognitive and computer sciences. Most of the previous studies focused on the recognition of basic emotions (such as happiness, sadness and anger) using categorical approach. Recently, the focus...... has been shifting towards dimensional affect recognition based on the idea that emotional states are not independent from one another but related in a systematic manner. In this paper, we design a continuous dimensional speech affect recognition model that combines acoustic and semantic features. We...... design our own corpus that consists of 59 short movie clips with audio and text in subtitle format, rated by human subjects in arousal and valence (A-V) dimensions. For the acoustic part, we combine many features and use correlation based feature selection and apply support vector regression...

  8. Structural features of glycan recognition among viral pathogens.

    Science.gov (United States)

    Shanker, Sreejesh; Hu, Liya; Ramani, Sasirekha; Atmar, Robert L; Estes, Mary K; Venkataram Prasad, B V

    2017-06-01

    Recognition and binding to host glycans present on cellular surfaces is an initial and critical step in viral entry. Diverse families of host glycans such as histo-blood group antigens, sialoglycans and glycosaminoglycans are recognized by viruses. Glycan binding determines virus-host specificity, tissue tropism, pathogenesis and potential for interspecies transmission. Viruses including noroviruses, rotaviruses, enteroviruses, influenza, and papillomaviruses have evolved novel strategies to bind specific glycans often in a strain-specific manner. Structural studies have been instrumental in elucidating the molecular determinants of these virus-glycan interactions, aiding in developing vaccines and antivirals targeting this key interaction. Our review focuses on these key structural aspects of virus-glycan interactions, particularly highlighting the different strain-specific strategies employed by viruses to bind host glycans. Copyright © 2017. Published by Elsevier Ltd.

  9. Video Anomaly Detection with Compact Feature Sets for Online Performance.

    Science.gov (United States)

    Leyva, Roberto; Sanchez, Victor; Li, Chang-Tsun

    2017-04-18

    Over the past decade, video anomaly detection has been explored with remarkable results. However, research on methodologies suitable for online performance is still very limited. In this paper, we present an online framework for video anomaly detection. The key aspect of our framework is a compact set of highly descriptive features, which is extracted from a novel cell structure that helps to define support regions in a coarse-to-fine fashion. Based on the scene's activity, only a limited number of support regions are processed, thus limiting the size of the feature set. Specifically, we use foreground occupancy and optical flow features. The framework uses an inference mechanism that evaluates the compact feature set via Gaussian Mixture Models, Markov Chains and Bag-of-Words in order to detect abnormal events. Our framework also considers the joint response of the models in the local spatio-temporal neighborhood to increase detection accuracy. We test our framework on popular existing datasets and on a new dataset comprising a wide variety of realistic videos captured by surveillance cameras. This particular dataset includes surveillance videos depicting criminal activities, car accidents and other dangerous situations. Evaluation results show that our framework outperforms other online methods and attains a very competitive detection performance compared to state-of-the-art non-online methods.

  10. Configural and Featural Face Processing Influences on Emotion Recognition in Schizophrenia and Bipolar Disorder.

    Science.gov (United States)

    Van Rheenen, Tamsyn E; Joshua, Nicole; Castle, David J; Rossell, Susan L

    2017-03-01

    Emotion recognition impairments have been demonstrated in schizophrenia (Sz), but are less consistent and lesser in magnitude in bipolar disorder (BD). This may be related to the extent to which different face processing strategies are engaged during emotion recognition in each of these disorders. We recently showed that Sz patients had impairments in the use of both featural and configural face processing strategies, whereas BD patients were impaired only in the use of the latter. Here we examine the influence that these impairments have on facial emotion recognition in these cohorts. Twenty-eight individuals with Sz, 28 individuals with BD, and 28 healthy controls completed a facial emotion labeling task with two conditions designed to separate the use of featural and configural face processing strategies; part-based and whole-face emotion recognition. Sz patients performed worse than controls on both conditions, and worse than BD patients on the whole-face condition. BD patients performed worse than controls on the whole-face condition only. Configural processing deficits appear to influence the recognition of facial emotions in BD, whereas both configural and featural processing abnormalities impair emotion recognition in Sz. This may explain discrepancies in the profiles of emotion recognition between the disorders. (JINS, 2017, 23, 287-291).

  11. SAR Target Recognition Based on Multi-feature Multiple Representation Classifier Fusion

    Directory of Open Access Journals (Sweden)

    Zhang Xinzheng

    2017-10-01

    Full Text Available In this paper, we present a Synthetic Aperture Radar (SAR image target recognition algorithm based on multi-feature multiple representation learning classifier fusion. First, it extracts three features from the SAR images, namely principal component analysis, wavelet transform, and Two-Dimensional Slice Zernike Moments (2DSZM features. Second, we harness the sparse representation classifier and the cooperative representation classifier with the above-mentioned features to get six predictive labels. Finally, we adopt classifier fusion to obtain the final recognition decision. We researched three different classifier fusion algorithms in our experiments, and the results demonstrate thatusing Bayesian decision fusion gives thebest recognition performance. The method based on multi-feature multiple representation learning classifier fusion integrates the discrimination of multi-features and combines the sparse and cooperative representation classification performance to gain complementary advantages and to improve recognition accuracy. The experiments are based on the Moving and Stationary Target Acquisition and Recognition (MSTAR database,and they demonstrate the effectiveness of the proposed approach.

  12. Improving Protein Fold Recognition by Extracting Fold-specific Features from Predicted Residue-residue Contacts.

    Science.gov (United States)

    Zhu, Jianwei; Zhang, Haicang; Li, Shuai Cheng; Wang, Chao; Kong, Lupeng; Sun, Shiwei; Zheng, Wei-Mou; Bu, Dongbo

    2017-08-16

    Accurate recognition of protein fold types is a key step for template-based prediction of protein structures. The existing approaches to fold recognition mainly exploit the features derived from alignments of query protein against templates. These approaches have been shown to be successful for fold recognition at family level, but usually failed at superfamily/fold levels. To overcome this limitation, one of the key points is to explore more structurally-informative features of proteins. Although residue-residue contacts carry abundant structural information, how to thoroughly exploit these information for fold recognition still remains a challenge. In this study, we present an approach (called DeepFR) to improve fold recognition at superfamily/fold levels. The basic idea of our approach is to extract fold-specific features from predicted residue-residue contacts of proteins using deep convolutional neural network (DCNN) technique. Based on these fold-specific features, we calculated similarity between query protein and templates, and then assigned query protein with fold type of the most similar template. DCNN has showed excellent performance in image feature extraction and image recognition; the rational underlying the application of DCNN for fold recognition is that contact likelihood maps are essentially analogy to images, as they both display compositional hierarchy. Experimental results on the LINDAHL dataset suggest that even using the extracted fold-specific features alone, our approach achieved success rate comparable to the state-of-the-art approaches. When further combining these features with traditional alignment-related features, the success rate of our approach increased to 92.3%, 82.5%, and 78.8% at family, superfamily, and fold levels, respectively, which is about 18% higher than the state-of-the-art approach at fold level, 6% higher at superfamily level, and 1% higher at family level. An independent assessment on SCOP_TEST dataset showed

  13. An Offline Fuzzy Based Approach for Iris Recognition with Enhanced Feature Detection

    Science.gov (United States)

    Kodituwakku, S. R.; Fazeen, M. I. M.

    Among many biometric identification methods iris recognition is more attractive due to the unique features of the human eye [1]. There are many proposed algorithms for iris recognition. Although all these methods are based on the properties of the iris, they are subject to some limitations. In this research we attempt to develop an algorithm for iris recognition based on Fuzzy logic incorporated with not only the visible properties of the human iris but also considering the iris function. Visible features of the human iris such as pigment related features, features controlling the size of the pupil, visible rare anomalies, pigment frill and Collarette are considered [2]. This paper presents the algorithm we developed to recognize iris. A prototype system developed is also discussed.

  14. Evaluation of Different Features for Face Recognition in Video

    Science.gov (United States)

    2014-09-01

    15 4 Graph presents the performance comparison among different algorithms implemented in OpenCV (Fisherfaces, Eigenfaces and LBPH)- all use...for face recog- nition in video, in particular those available in the OpenCV library [13]. Comparative performance analysis of these algorithms is...videos. The first one used a generic class that exists in OpenCV (version 2.4.1), called FeatureDetector, which allowed the automatic extraction of

  15. Optical character recognition of camera-captured images based on phase features

    Science.gov (United States)

    Diaz-Escobar, Julia; Kober, Vitaly

    2015-09-01

    Nowadays most of digital information is obtained using mobile devices specially smartphones. In particular, it brings the opportunity for optical character recognition in camera-captured images. For this reason many recognition applications have been recently developed such as recognition of license plates, business cards, receipts and street signal; document classification, augmented reality, language translator and so on. Camera-captured images are usually affected by geometric distortions, nonuniform illumination, shadow, noise, which make difficult the recognition task with existing systems. It is well known that the Fourier phase contains a lot of important information regardless of the Fourier magnitude. So, in this work we propose a phase-based recognition system exploiting phase-congruency features for illumination/scale invariance. The performance of the proposed system is tested in terms of miss classifications and false alarms with the help of computer simulation.

  16. Multiple levels of linguistic and paralinguistic features contribute to voice recognition.

    Science.gov (United States)

    Zarate, Jean Mary; Tian, Xing; Woods, Kevin J P; Poeppel, David

    2015-06-19

    Voice or speaker recognition is critical in a wide variety of social contexts. In this study, we investigated the contributions of acoustic, phonological, lexical, and semantic information toward voice recognition. Native English speaking participants were trained to recognize five speakers in five conditions: non-speech, Mandarin, German, pseudo-English, and English. We showed that voice recognition significantly improved as more information became available, from purely acoustic features in non-speech to additional phonological information varying in familiarity. Moreover, we found that the recognition performance is transferable between training and testing in phonologically familiar conditions (German, pseudo-English, and English), but not in unfamiliar (Mandarin) or non-speech conditions. These results provide evidence suggesting that bottom-up acoustic analysis and top-down influence from phonological processing collaboratively govern voice recognition.

  17. Haar-like Features for Robust Real-Time Face Recognition

    DEFF Research Database (Denmark)

    Nasrollahi, Kamal; Moeslund, Thomas B.

    2013-01-01

    Face recognition is still a very challenging task when the input face image is noisy, occluded by some obstacles, of very low-resolution, not facing the camera, and not properly illuminated. These problems make the feature extraction and consequently the face recognition system unstable....... The proposed system in this paper introduces the novel idea of using Haar-like features, which have commonly been used for object detection, along with a probabilistic classifier for face recognition. The proposed system is simple, real-time, effective and robust against most of the mentioned problems....... Experimental results on public databases show that the proposed system indeed outperforms the state-of-the-art face recognition systems....

  18. A decision forest based feature selection framework for action recognition from RGB-Depth cameras

    OpenAIRE

    Negin, Farhood; Özdemir, Fırat; Ozdemir, Firat; Yüksel, Kamer Ali; Yuksel, Kamer Ali; Akgül, Ceyhun Burak; Akgul, Ceyhun Burak; Erçil, Aytül; Ercil, Aytul

    2013-01-01

    In this paper, we present an action recognition framework leveraging data mining capabilities of random decision forests trained on kinematic features. We describe human motion via a rich collection of kinematic feature time-series computed from the skeletal representation of the body in motion. We discriminatively optimize a random decision forest model over this collection to identify the most effective subset of features, localized both in time and space. Later, we ...

  19. Enhanced LBP-Based Face Recognition System Using a Heuristic Approach for Searching Weight Set

    OpenAIRE

    Nguyen, Nhat-Quan,; Le, Thai

    2014-01-01

    Part 8: Pattern Recognition and Image Processing; International audience; Local Binary Patterns is one of the most effective approaches for pattern recognition in general and face recognition in particular. There have been many studies on improving this method such as changing the input values or using another kind of histogram. Although weight set is also an important key leading to the success of this method, it does not seem to get much attention. A majority of LBP-based approaches are sti...

  20. Selecting Informative Features of the Helicopter and Aircraft Acoustic Signals in the Adaptive Autonomous Information Systems for Recognition

    Directory of Open Access Journals (Sweden)

    V. K. Hohlov

    2017-01-01

    Full Text Available The article forms the rationale for selecting the informative features of the helicopter and aircraft acoustic signals to solve a problem of their recognition and shows that the most informative ones are the counts of extrema in the energy spectra of the input signals, which represent non-centered random variables. An apparatus of the multiple initial regression coefficients was selected as a mathematical tool of research. The application of digital re-circulators with positive and negative feedbacks, which have the comb-like frequency characteristics, solves the problem of selecting informative features. A distinguishing feature of such an approach is easy agility of the comb frequency characteristics just through the agility of a delay value of digital signal in the feedback circuit. Adding an adaptation block to the selection block of the informative features enables us to ensure the invariance of used informative feature and counts of local extrema of the spectral power density to the airspeed of a helicopter. The paper gives reasons for the principle of adaptation and the structure of the adaptation block. To form the discriminator characteristics are used the cross-correlation statistical characteristics of the quadrature components of acoustic signal realizations, obtained by Hilbert transform. The paper proposes to provide signal recognition using a regression algorithm that allows handling the non-centered informative features and using a priori information about coefficients of initial regression of signal and noise.The simulation in Matlab Simulink has shown that selected informative features of signals in regressive processing of signal realizations of 0.5 s duration have good separability, and based on a set of 100 acoustic signal realizations in each class in full-scale conditions, has proved ensuring a correct recognition probability of 0.975, at least. The considered principles of informative features selection and adaptation can

  1. Dynamic Arm Gesture Recognition Using Spherical Angle Features and Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Hyesuk Kim

    2015-01-01

    Full Text Available We introduce a vision-based arm gesture recognition (AGR system using Kinect. The AGR system learns the discrete Hidden Markov Model (HMM, an effective probabilistic graph model for gesture recognition, from the dynamic pose of the arm joints provided by the Kinect API. Because Kinect’s viewpoint and the subject’s arm length can substantially affect the estimated 3D pose of each joint, it is difficult to recognize gestures reliably with these features. The proposed system performs the feature transformation that changes the 3D Cartesian coordinates of each joint into the 2D spherical angles of the corresponding arm part to obtain view-invariant and more discriminative features. We confirmed high recognition performance of the proposed AGR system through experiments with two different datasets.

  2. Probabilistic neural network with homogeneity testing in recognition of discrete patterns set.

    Science.gov (United States)

    Savchenko, A V

    2013-10-01

    The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n-grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%-7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. Copyright © 2013 Elsevier Ltd. All rights reserved.

  3. Measures for the characterisation of pattern-recognition data sets

    CSIR Research Space (South Africa)

    Van der Walt, Christiaan M

    2007-11-01

    Full Text Available have shown the importance of the relationship between data characteristics and classifier performance; they have, however, failed to de- scribe this relationship in detail. A detailed discussion of each of these approaches is given in [8]. 3. Data... in feature standard deviations (SDs) in each class by calculating the SD of the feature SDs; we use the maximum-likelihood equations given in [8] to calculate these SDs. We denote this SD of the feature SDs as measure T3. 3.6. Inter-class scale variation...

  4. A natural approach to convey numerical digits using hand activity recognition based on hand shape features

    Science.gov (United States)

    Chidananda, H.; Reddy, T. Hanumantha

    2017-06-01

    This paper presents a natural representation of numerical digit(s) using hand activity analysis based on number of fingers out stretched for each numerical digit in sequence extracted from a video. The analysis is based on determining a set of six features from a hand image. The most important features used from each frame in a video are the first fingertip from top, palm-line, palm-center, valley points between the fingers exists above the palm-line. Using this work user can convey any number of numerical digits using right or left or both the hands naturally in a video. Each numerical digit ranges from 0 to9. Hands (right/left/both) used to convey digits can be recognized accurately using the valley points and with this recognition whether the user is a right / left handed person in practice can be analyzed. In this work, first the hand(s) and face parts are detected by using YCbCr color space and face part is removed by using ellipse based method. Then, the hand(s) are analyzed to recognize the activity that represents a series of numerical digits in a video. This work uses pixel continuity algorithm using 2D coordinate geometry system and does not use regular use of calculus, contours, convex hull and datasets.

  5. An open-set detection evaluation methodology for automatic emotion recognition in speech

    NARCIS (Netherlands)

    Truong, K.P.; Leeuwen, D.A. van

    2007-01-01

    In this paper, we present a detection approach and an ‘open-set’ detection evaluation methodology for automatic emotion recognition in speech. The traditional classification approach does not seem to be suitable and flexible enough for typical emotion recognition tasks. For example, classification

  6. A Method of SAR Target Recognition Based on Gabor Filter and Local Texture Feature Extraction

    Directory of Open Access Journals (Sweden)

    Wang Lu

    2015-12-01

    Full Text Available This paper presents a novel texture feature extraction method based on a Gabor filter and Three-Patch Local Binary Patterns (TPLBP for Synthetic Aperture Rader (SAR target recognition. First, SAR images are processed by a Gabor filter in different directions to enhance the significant features of the targets and their shadows. Then, the effective local texture features based on the Gabor filtered images are extracted by TPLBP. This not only overcomes the shortcoming of Local Binary Patterns (LBP, which cannot describe texture features for large scale neighborhoods, but also maintains the rotation invariant characteristic which alleviates the impact of the direction variations of SAR targets on recognition performance. Finally, we use an Extreme Learning Machine (ELM classifier and extract the texture features. The experimental results of MSTAR database demonstrate the effectiveness of the proposed method.

  7. Genome display tool: visualizing features in complex data sets

    Directory of Open Access Journals (Sweden)

    Lu Yue

    2007-02-01

    Full Text Available Abstract Background The enormity of the information contained in large data sets makes it difficult to develop intuitive understanding. It would be useful to have software that allows visualization of possible correlations between properties that can be associated with a core data set. In the case of bacterial genomes, existing visualization tools focus on either global properties such as variations in composition or detailed local displays of the features that comprise the annotation. It is not easy to visualize other information in the context of this core information. Results A Java based software known as the Genome Display Tool (GDT, allows the user to simultaneously view the distribution of multiple attributes pertaining to genes and intragenic regions in a single bacterial genome using different colours and shapes on a single screen. The display represents each gene by small boxes that correlate with physical position in the genome. The size of the boxes is dynamically allocated based on the number of genes and a zoom feature allows close-up inspection of regions of interest. The display is interfaced with a MS-Access relational database and can display any feature in the database that can be represented by discrete values. Data is readily added to the database from an MS-Excel spread sheet. The functionality of GDT is demonstrated by comparing the results of two predictions of recent horizontal transfer events in the genome of Synechocystis PCC-6803. The resulting display allows the user to immediately see how much agreement exists between the two methods and also visualize how genes in various categories (e.g. predicted in both methods, one method etc are distributed in the genome. Conclusion The GDT software provides the user with a powerful tool that allows development of an intuitive understanding of the relative distribution of features in a large data set. As additional features are added to the data set, the number of possible

  8. Using genetic algorithm feature selection in neural classification systems for image pattern recognition

    Directory of Open Access Journals (Sweden)

    Margarita R. Gamarra A.

    2012-09-01

    Full Text Available Pattern recognition performance depends on variations during extraction, selection and classification stages. This paper presents an approach to feature selection by using genetic algorithms with regard to digital image recognition and quality control. Error rate and kappa coefficient were used for evaluating the genetic algorithm approach Neural networks were used for classification, involving the features selected by the genetic algorithms. The neural network approach was compared to a K-nearest neighbor classifier. The proposed approach performed better than the other methods.

  9. Binary pattern flavored feature extractors for Facial Expression Recognition: An overview

    DEFF Research Database (Denmark)

    Kristensen, Rasmus Lyngby; Tan, Zheng-Hua; Ma, Zhanyu

    2015-01-01

    This paper conducts a survey of modern binary pattern flavored feature extractors applied to the Facial Expression Recognition (FER) problem. In total, 26 different feature extractors are included, of which six are selected for in depth description. In addition, the paper unifies important FER...... terminology, describes open challenges, and provides recommendations to scientific evaluation of FER systems. Lastly, it studies the facial expression recognition accuracy and blur invariance of the Local Frequency Descriptor. The paper seeks to bring together disjointed studies, and the main contribution...

  10. A Feature-Based Structural Measure: An Image Similarity Measure for Face Recognition

    Directory of Open Access Journals (Sweden)

    Noor Abdalrazak Shnain

    2017-08-01

    Full Text Available Facial recognition is one of the most challenging and interesting problems within the field of computer vision and pattern recognition. During the last few years, it has gained special attention due to its importance in relation to current issues such as security, surveillance systems and forensics analysis. Despite this high level of attention to facial recognition, the success is still limited by certain conditions; there is no method which gives reliable results in all situations. In this paper, we propose an efficient similarity index that resolves the shortcomings of the existing measures of feature and structural similarity. This measure, called the Feature-Based Structural Measure (FSM, combines the best features of the well-known SSIM (structural similarity index measure and FSIM (feature similarity index measure approaches, striking a balance between performance for similar and dissimilar images of human faces. In addition to the statistical structural properties provided by SSIM, edge detection is incorporated in FSM as a distinctive structural feature. Its performance is tested for a wide range of PSNR (peak signal-to-noise ratio, using ORL (Olivetti Research Laboratory, now AT&T Laboratory Cambridge and FEI (Faculty of Industrial Engineering, São Bernardo do Campo, São Paulo, Brazil databases. The proposed measure is tested under conditions of Gaussian noise; simulation results show that the proposed FSM outperforms the well-known SSIM and FSIM approaches in its efficiency of similarity detection and recognition of human faces.

  11. Recognition-induced forgetting is not due to category-based set size

    National Research Council Canada - National Science Library

    Maxcey, Ashleigh M

    2016-01-01

    ... for objects from that group that were not practiced. An alternative explanation of this effect is that category-based set size is inducing forgetting, not recognition practice as claimed by some researchers...

  12. Feature selection for speech emotion recognition in Spanish and Basque: on the use of machine learning to improve human-computer interaction.

    Directory of Open Access Journals (Sweden)

    Andoni Arruti

    Full Text Available Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

  13. Audio-Visual Speech Recognition Using MPEG-4 Compliant Visual Features

    Directory of Open Access Journals (Sweden)

    Petar S. Aleksic

    2002-11-01

    Full Text Available We describe an audio-visual automatic continuous speech recognition system, which significantly improves speech recognition performance over a wide range of acoustic noise levels, as well as under clean audio conditions. The system utilizes facial animation parameters (FAPs supported by the MPEG-4 standard for the visual representation of speech. We also describe a robust and automatic algorithm we have developed to extract FAPs from visual data, which does not require hand labeling or extensive training procedures. The principal component analysis (PCA was performed on the FAPs in order to decrease the dimensionality of the visual feature vectors, and the derived projection weights were used as visual features in the audio-visual automatic speech recognition (ASR experiments. Both single-stream and multistream hidden Markov models (HMMs were used to model the ASR system, integrate audio and visual information, and perform a relatively large vocabulary (approximately 1000 words speech recognition experiments. The experiments performed use clean audio data and audio data corrupted by stationary white Gaussian noise at various SNRs. The proposed system reduces the word error rate (WER by 20% to 23% relatively to audio-only speech recognition WERs, at various SNRs (0–30 dB with additive white Gaussian noise, and by 19% relatively to audio-only speech recognition WER under clean audio conditions.

  14. An approach to EEG-based emotion recognition using combined feature extraction method.

    Science.gov (United States)

    Zhang, Yong; Ji, Xiaomin; Zhang, Suhua

    2016-10-28

    EEG signal has been widely used in emotion recognition. However, too many channels and extracted features are used in the current EEG-based emotion recognition methods, which lead to the complexity of these methods This paper studies on feature extraction on EEG-based emotion recognition model to overcome those disadvantages, and proposes an emotion recognition method based on empirical mode decomposition (EMD) and sample entropy. The proposed method first employs EMD strategy to decompose EEG signals only containing two channels into a series of intrinsic mode functions (IMFs). The first 4 IMFs are selected to calculate corresponding sample entropies and then to form feature vectors. These vectors are fed into support vector machine classifier for training and testing. The average accuracy of the proposed method is 94.98% for binary-class tasks and the best accuracy achieves 93.20% for the multi-class task on DEAP database, respectively. The results indicate that the proposed method is more suitable for emotion recognition than several methods of comparison. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  15. Gesture Recognition using Latent-Dynamic based Conditional Random Fields and Scalar Features

    Science.gov (United States)

    Yulita, I. N.; Fanany, M. I.; Arymurthy, A. M.

    2017-02-01

    The need for segmentation and labeling of sequence data appears in several fields. The use of the conditional models such as Conditional Random Fields is widely used to solve this problem. In the pattern recognition, Conditional Random Fields specify the possibilities of a sequence label. This method constructs its full label sequence to be a probabilistic graphical model based on its observation. However, Conditional Random Fields can not capture the internal structure so that Latent-based Dynamic Conditional Random Fields is developed without leaving external dynamics of inter-label. This study proposes the use of Latent-Dynamic Conditional Random Fields for Gesture Recognition and comparison between both methods. Besides, this study also proposes the use of a scalar features to gesture recognition. The results show that performance of Latent-dynamic based Conditional Random Fields is not better than the Conditional Random Fields, and scalar features are effective for both methods are in gesture recognition. Therefore, it recommends implementing Conditional Random Fields and scalar features in gesture recognition for better performance

  16. Blurred face recognition by fusing blur-invariant texture and structure features

    Science.gov (United States)

    Zhu, Mengyu; Cao, Zhiguo; Xiao, Yang; Xie, Xiaokang

    2015-10-01

    Blurred face recognition is still remaining as a challenge task, but with wide applications. Image blur can largely affect recognition performance. The local phase quantization (LPQ) was proposed to extract the blur-invariant texture information. It was used for blurred face recognition and achieved good performance. However, LPQ considers only the phase blur-invariant texture information, which is not sufficient. In addition, LPQ is extracted holistically, which cannot fully explore its discriminative power on local spatial properties. In this paper, we propose a novel method for blurred face recognition. The texture and structure blur-invariant features are extracted and fused to generate a more complete description on blurred image. For texture blur-invariant feature, LPQ is extracted in a densely sampled way and vector of locally aggregated descriptors (VLAD) is employed to enhance its performance. For structure blur-invariant feature, the histogram of oriented gradient (HOG) is used. To further enhance its blur invariance, we improve HOG by eliminating weak gradient magnitude which is more sensitive to image blur than the strong gradient. The improved HOG is then fused with the original HOG by canonical correlation analysis (CCA). At last, we fuse them together by CCA to form the final blur-invariant representation of the face image. The experiments are performed on three face datasets. The results demonstrate that our improvements and our proposition can have a good performance in blurred face recognition.

  17. A High Precision Feature Based on LBP and Gabor Theory for Face Recognition

    Directory of Open Access Journals (Sweden)

    Peng Ouyang

    2013-04-01

    Full Text Available How to describe an image accurately with the most useful information but at the same time the least useless information is a basic problem in the recognition field. In this paper, a novel and high precision feature called BG2D2LRP is proposed, accompanied with a corresponding face recognition system. The feature contains both static texture differences and dynamic contour trends. It is based on Gabor and LBP theory, operated by various kinds of transformations such as block, second derivative, direct orientation, layer and finally fusion in a particular way. Seven well-known face databases such as FRGC, AR, FERET and so on are used to evaluate the veracity and robustness of the proposed feature. A maximum improvement of 29.41% is achieved comparing with other methods. Besides, the ROC curve provides a satisfactory figure. Those experimental results strongly demonstrate the feasibility and superiority of the new feature and method.

  18. An overlapping-feature-based phonological model incorporating linguistic constraints: applications to speech recognition.

    Science.gov (United States)

    Sun, Jiping; Deng, Li

    2002-02-01

    Modeling phonological units of speech is a critical issue in speech recognition. In this paper, our recent development of an overlapping-feature-based phonological model that represents long-span contextual dependency in speech acoustics is reported. In this model, high-level linguistic constraints are incorporated in automatic construction of the patterns of feature-overlapping and of the hidden Markov model (HMM) states induced by such patterns. The main linguistic information explored includes word and phrase boundaries, morpheme, syllable, syllable constituent categories, and word stress. A consistent computational framework developed for the construction of the feature-based model and the major components of the model are described. Experimental results on the use of the overlapping-feature model in an HMM-based system for speech recognition show improvements over the conventional triphone-based phonological model.

  19. Optimising chemical named entity recognition with pre-processing analytics, knowledge-rich features and heuristics

    Science.gov (United States)

    2015-01-01

    Background The development of robust methods for chemical named entity recognition, a challenging natural language processing task, was previously hindered by the lack of publicly available, large-scale, gold standard corpora. The recent public release of a large chemical entity-annotated corpus as a resource for the CHEMDNER track of the Fourth BioCreative Challenge Evaluation (BioCreative IV) workshop greatly alleviated this problem and allowed us to develop a conditional random fields-based chemical entity recogniser. In order to optimise its performance, we introduced customisations in various aspects of our solution. These include the selection of specialised pre-processing analytics, the incorporation of chemistry knowledge-rich features in the training and application of the statistical model, and the addition of post-processing rules. Results Our evaluation shows that optimal performance is obtained when our customisations are integrated into the chemical entity recogniser. When its performance is compared with that of state-of-the-art methods, under comparable experimental settings, our solution achieves competitive advantage. We also show that our recogniser that uses a model trained on the CHEMDNER corpus is suitable for recognising names in a wide range of corpora, consistently outperforming two popular chemical NER tools. Conclusion The contributions resulting from this work are two-fold. Firstly, we present the details of a chemical entity recognition methodology that has demonstrated performance at a competitive, if not superior, level as that of state-of-the-art methods. Secondly, the developed suite of solutions has been made publicly available as a configurable workflow in the interoperable text mining workbench Argo. This allows interested users to conveniently apply and evaluate our solutions in the context of other chemical text mining tasks. PMID:25810777

  20. SSVEP recognition using common feature analysis in brain-computer interface.

    Science.gov (United States)

    Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

    2015-04-15

    Canonical correlation analysis (CCA) has been successfully applied to steady-state visual evoked potential (SSVEP) recognition for brain-computer interface (BCI) application. Although the CCA method outperforms the traditional power spectral density analysis through multi-channel detection, it requires additionally pre-constructed reference signals of sine-cosine waves. It is likely to encounter overfitting in using a short time window since the reference signals include no features from training data. We consider that a group of electroencephalogram (EEG) data trials recorded at a certain stimulus frequency on a same subject should share some common features that may bear the real SSVEP characteristics. This study therefore proposes a common feature analysis (CFA)-based method to exploit the latent common features as natural reference signals in using correlation analysis for SSVEP recognition. Good performance of the CFA method for SSVEP recognition is validated with EEG data recorded from ten healthy subjects, in contrast to CCA and a multiway extension of CCA (MCCA). Experimental results indicate that the CFA method significantly outperformed the CCA and the MCCA methods for SSVEP recognition in using a short time window (i.e., less than 1s). The superiority of the proposed CFA method suggests it is promising for the development of a real-time SSVEP-based BCI. Copyright © 2014 Elsevier B.V. All rights reserved.

  1. Comparing sociocultural features of cholera in three endemic African settings

    Science.gov (United States)

    2013-01-01

    Background Cholera mainly affects developing countries where safe water supply and sanitation infrastructure are often rudimentary. Sub-Saharan Africa is a cholera hotspot. Effective cholera control requires not only a professional assessment, but also consideration of community-based priorities. The present work compares local sociocultural features of endemic cholera in urban and rural sites from three field studies in southeastern Democratic Republic of Congo (SE-DRC), western Kenya and Zanzibar. Methods A vignette-based semistructured interview was used in 2008 in Zanzibar to study sociocultural features of cholera-related illness among 356 men and women from urban and rural communities. Similar cross-sectional surveys were performed in western Kenya (n = 379) and in SE-DRC (n = 360) in 2010. Systematic comparison across all settings considered the following domains: illness identification; perceived seriousness, potential fatality and past household episodes; illness-related experience; meaning; knowledge of prevention; help-seeking behavior; and perceived vulnerability. Results Cholera is well known in all three settings and is understood to have a significant impact on people’s lives. Its social impact was mainly characterized by financial concerns. Problems with unsafe water, sanitation and dirty environments were the most common perceived causes across settings; nonetheless, non-biomedical explanations were widespread in rural areas of SE-DRC and Zanzibar. Safe food and water and vaccines were prioritized for prevention in SE-DRC. Safe water was prioritized in western Kenya along with sanitation and health education. The latter two were also prioritized in Zanzibar. Use of oral rehydration solutions and rehydration was a top priority everywhere; healthcare facilities were universally reported as a primary source of help. Respondents in SE-DRC and Zanzibar reported cholera as affecting almost everybody without differentiating much for gender, age

  2. Comparison of Image Transform-Based Features for Visual Speech Recognition in Clean and Corrupted Videos

    Directory of Open Access Journals (Sweden)

    Ji Ming

    2008-03-01

    Full Text Available We present results of a study into the performance of a variety of different image transform-based feature types for speaker-independent visual speech recognition of isolated digits. This includes the first reported use of features extracted using a discrete curvelet transform. The study will show a comparison of some methods for selecting features of each feature type and show the relative benefits of both static and dynamic visual features. The performance of the features will be tested on both clean video data and also video data corrupted in a variety of ways to assess each feature type's robustness to potential real-world conditions. One of the test conditions involves a novel form of video corruption we call jitter which simulates camera and/or head movement during recording.

  3. Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition.

    Science.gov (United States)

    Gutta, Sandeep; Cheng, Qi

    2016-03-01

    Traditional biometric recognition systems often utilize physiological traits such as fingerprint, face, iris, etc. Recent years have seen a growing interest in electrocardiogram (ECG)-based biometric recognition techniques, especially in the field of clinical medicine. In existing ECG-based biometric recognition methods, feature extraction and classifier design are usually performed separately. In this paper, a multitask learning approach is proposed, in which feature extraction and classifier design are carried out simultaneously. Weights are assigned to the features within the kernel of each task. We decompose the matrix consisting of all the feature weights into sparse and low-rank components. The sparse component determines the features that are relevant to identify each individual, and the low-rank component determines the common feature subspace that is relevant to identify all the subjects. A fast optimization algorithm is developed, which requires only the first-order information. The performance of the proposed approach is demonstrated through experiments using the MIT-BIH Normal Sinus Rhythm database.

  4. Object Recognition in Mental Representations: Directions for Exploring Diagnostic Features through Visual Mental Imagery

    Science.gov (United States)

    Roldan, Stephanie M.

    2017-01-01

    One of the fundamental goals of object recognition research is to understand how a cognitive representation produced from the output of filtered and transformed sensory information facilitates efficient viewer behavior. Given that mental imagery strongly resembles perceptual processes in both cortical regions and subjective visual qualities, it is reasonable to question whether mental imagery facilitates cognition in a manner similar to that of perceptual viewing: via the detection and recognition of distinguishing features. Categorizing the feature content of mental imagery holds potential as a reverse pathway by which to identify the components of a visual stimulus which are most critical for the creation and retrieval of a visual representation. This review will examine the likelihood that the information represented in visual mental imagery reflects distinctive object features thought to facilitate efficient object categorization and recognition during perceptual viewing. If it is the case that these representational features resemble their sensory counterparts in both spatial and semantic qualities, they may well be accessible through mental imagery as evaluated through current investigative techniques. In this review, methods applied to mental imagery research and their findings are reviewed and evaluated for their efficiency in accessing internal representations, and implications for identifying diagnostic features are discussed. An argument is made for the benefits of combining mental imagery assessment methods with diagnostic feature research to advance the understanding of visual perceptive processes, with suggestions for avenues of future investigation. PMID:28588538

  5. A de-illumination scheme for face recognition based on fast decomposition and detail feature fusion.

    Science.gov (United States)

    Zhou, Yi; Zhou, Sheng-Tong; Zhong, Zuo-Yang; Li, Hong-Guang

    2013-05-06

    Almost all the face recognition algorithms are unsatisfied due to illumination variation. Feature with high frequency represents the face intrinsic structure according to the common assumption that illumination varies slowly and the face intrinsic feature varies rapidly. In this paper, we will propose an adaptive scheme based on FBEEMD and detail feature fusion. FBEEMD is a fast version of BEEMD without time-consuming surface interpolation and iteration computation. It can decompose an image into sub-images with high frequency matching detail feature and sub-images with low frequency corresponding to contour feature. However, it is difficult to determine by quantitative analysis that which sub-images with high frequency can be used for reconstructing an illumination-invariant face. Thus, two measurements are proposed to calculate weights for quantifying the detail feature. With this fusion technique, one can reconstruct a more illumination-neutral facial image to improve face recognition rate. Verification experiments using classical recognition algorithms are tested with Yale B, PIE and FERET databases. The encouraging results show that the proposed scheme is very effective when dealing with face images under variable lighting condition.

  6. Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors.

    Science.gov (United States)

    Xi, Xugang; Tang, Minyan; Miran, Seyed M; Luo, Zhizeng

    2017-05-27

    As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short

  7. Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors

    Directory of Open Access Journals (Sweden)

    Xugang Xi

    2017-05-01

    Full Text Available As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG, with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL. A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM with Permutation Entropy (PE or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA with the feature WAMP guarantees a high sensitivity (98.70% and specificity (98.59% with a

  8. Selecting an informative features vocabulary for recognition algorithms based on Fourier-descriptors

    Directory of Open Access Journals (Sweden)

    V. Ya. Kolyuchkin

    2014-01-01

    Full Text Available Working vocabulary of features include most informative features of objects to be recognized. The aim is to develop a method of forming a working vocabulary of features for recognition algorithms based on Fourier-descriptors of the object image contours.To solve this problem the paper offers to use the method of functional maximization that is the ratio of the distance between the classes to the spread of objects within each of the classes represented in the feature space, which is formed on the basis of Fourier-descriptors.To check the effectiveness of the proposed method to form a working vocabulary of features the numerical experiments have been carried out. The experiments used two databases of reference images consisting of 10 and 13 reference images. Test images obtained by rotating the reference images, by zooming, as well as by adding the noise using the normal law of distribution have been created from these images. The proposed by the author algorithm, which uses the Prewitt operator, threshold segmentation, and morphological processing has marked the contours of images. The original vocabulary of features derived from the Fourier-descriptors has dimension of 98. The vocabularies of working features having the dimensions, respectively, 3 and 4 have been formed on the basis of functional maximization for both reference images. In the course of numerical experiments the frequency of correct decisions to recognise the features of reference bases of images for the original and working vocabularies has been evaluated. It has been proved that the algorithm of recognition with the formed working vocabularies of features provides a great efficiency of automatic recognition of objects.There are known publications, which use a similar method to form a working vocabulary of features in algorithms of human recognition by the image. But there are no publications on choosing the vocabulary of features for recognition algorithms based on the

  9. Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data

    Science.gov (United States)

    Yang, Bisheng; Dong, Zhen; Liu, Yuan; Liang, Fuxun; Wang, Yongjun

    2017-04-01

    In recent years, updating the inventory of road infrastructures based on field work is labor intensive, time consuming, and costly. Fortunately, vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. However, robust recognition of road facilities from huge volumes of 3D point clouds is still a challenging issue because of complicated and incomplete structures, occlusions and varied point densities. Most existing methods utilize point or object based features to recognize object candidates, and can only extract limited types of objects with a relatively low recognition rate, especially for incomplete and small objects. To overcome these drawbacks, this paper proposes a semantic labeling framework by combing multiple aggregation levels (point-segment-object) of features and contextual features to recognize road facilities, such as road surfaces, road boundaries, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, and cars, for highway infrastructure inventory. The proposed method first identifies ground and non-ground points, and extracts road surfaces facilities from ground points. Non-ground points are segmented into individual candidate objects based on the proposed multi-rule region growing method. Then, the multiple aggregation levels of features and the contextual features (relative positions, relative directions, and spatial patterns) associated with each candidate object are calculated and fed into a SVM classifier to label the corresponding candidate object. The recognition performance of combining multiple aggregation levels and contextual features was compared with single level (point, segment, or object) based features using large-scale highway scene point clouds. Comparative studies demonstrated that the proposed semantic labeling framework significantly improves road facilities recognition

  10. Noise-robust speech recognition through auditory feature detection and spike sequence decoding.

    Science.gov (United States)

    Schafer, Phillip B; Jin, Dezhe Z

    2014-03-01

    Speech recognition in noisy conditions is a major challenge for computer systems, but the human brain performs it routinely and accurately. Automatic speech recognition (ASR) systems that are inspired by neuroscience can potentially bridge the performance gap between humans and machines. We present a system for noise-robust isolated word recognition that works by decoding sequences of spikes from a population of simulated auditory feature-detecting neurons. Each neuron is trained to respond selectively to a brief spectrotemporal pattern, or feature, drawn from the simulated auditory nerve response to speech. The neural population conveys the time-dependent structure of a sound by its sequence of spikes. We compare two methods for decoding the spike sequences--one using a hidden Markov model-based recognizer, the other using a novel template-based recognition scheme. In the latter case, words are recognized by comparing their spike sequences to template sequences obtained from clean training data, using a similarity measure based on the length of the longest common sub-sequence. Using isolated spoken digits from the AURORA-2 database, we show that our combined system outperforms a state-of-the-art robust speech recognizer at low signal-to-noise ratios. Both the spike-based encoding scheme and the template-based decoding offer gains in noise robustness over traditional speech recognition methods. Our system highlights potential advantages of spike-based acoustic coding and provides a biologically motivated framework for robust ASR development.

  11. Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition

    Directory of Open Access Journals (Sweden)

    Mohammad Subhi Al-batah

    2014-01-01

    Full Text Available To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL and high-grade squamous intraepithelial lesion (HSIL. The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.

  12. Multiple adaptive neuro-fuzzy inference system with automatic features extraction algorithm for cervical cancer recognition.

    Science.gov (United States)

    Al-batah, Mohammad Subhi; Isa, Nor Ashidi Mat; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi

    2014-01-01

    To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.

  13. Pose-robust face recognition using shape-adapted texture features

    Science.gov (United States)

    Gernoth, Thorsten; Goossen, André; Grigat, Rolf-Rainer

    2011-03-01

    Unconstrained environments with variable ambient illumination and changes of head pose are still challenging for many face recognition systems. To recognize a person independent of pose, we first fit an active appearance model to a given facial image. Shape information is used to transform the face into a pose-normalized representation. We decompose the transformed face into local regions and extract texture features from these not necessarily rectangular regions using a shape-adapted discrete cosine transform. We show that these features contain sufficient discriminative information to recognize persons across changes in pose. Furthermore, our experimental results show a significant improvement in face recognition performance on faces with pose variations when compared with a block-DCT based feature extraction technique in an access control scenario.

  14. Specific features of goal setting in road traffic safety

    Science.gov (United States)

    Kolesov, V. I.; Danilov, O. F.; Petrov, A. I.

    2017-10-01

    Road traffic safety (RTS) management is inherently a branch of cybernetics and therefore requires clear formalization of the task. The paper aims at identification of the specific features of goal setting in RTS management under the system approach. The paper presents the results of cybernetic modeling of the cause-to-effect mechanism of a road traffic accident (RTA); in here, the mechanism itself is viewed as a complex system. A designed management goal function is focused on minimizing the difficulty in achieving the target goal. Optimization of the target goal has been performed using the Lagrange principle. The created working algorithms have passed the soft testing. The key role of the obtained solution in the tactical and strategic RTS management is considered. The dynamics of the management effectiveness indicator has been analyzed based on the ten-year statistics for Russia.

  15. Acoustic features involved in the neighbour-stranger vocal recognition process in male Australian fur seals.

    Science.gov (United States)

    Tripovich, J S; Charrier, I; Rogers, T L; Canfield, R; Arnould, J P Y

    2008-09-01

    Many territorial species have the ability to recognise neighbours from stranger individuals. If the neighbouring individual is assumed to pose less of a threat, the territorial individual responds less and avoids unnecessary confrontations with familiar individuals at established boundaries, thus avoiding the costly energy expenditure associated with fighting. Territorial male Australian fur seals respond more to strangers than to neighbouring males. The present study evaluated which acoustic features were important in the neighbour-stranger recognition process in male Australian fur seals. The results reveal that there was an increase in response strength or intensity from males when they heard more bark units, indicating the importance of repetition to detect a caller. However, lengthening and shortening the inter-unit spaces, (i.e. changing the rhythm of the call) did not appear to significantly affect an animal's response. In addition, the whole frequency spectrum was considered important to recognition with results suggesting that they may vary in their importance. A call containing the dominant and surrounding harmonics was considered important to a male's ability to recognise its neighbour. Furthermore, recognition occurs even with a partial bark, but males need to hear between 25 and 75% of each bark unit from neighbouring seals. Our study highlights which acoustic features induce stronger or weaker responses from territorial males, decoding the important features in neighbour-stranger recognition.

  16. Single-Word Recognition Need Not Depend on Single-Word Features: Narrative Coherence Counteracts Effects of Single-Word Features that Lexical Decision Emphasizes.

    Science.gov (United States)

    Teng, Dan W; Wallot, Sebastian; Kelty-Stephen, Damian G

    2016-12-01

    Research on reading comprehension of connected text emphasizes reliance on single-word features that organize a stable, mental lexicon of words and that speed or slow the recognition of each new word. However, the time needed to recognize a word might not actually be as fixed as previous research indicates, and the stability of the mental lexicon may change with task demands. The present study explores the effects of narrative coherence in self-paced story reading to single-word feature effects in lexical decision. We presented single strings of letters to 24 participants, in both lexical decision and self-paced story reading. Both tasks included the same words composing a set of adjective-noun pairs. Reading times revealed that the tasks, and the order of the presentation of the tasks, changed and/or eliminated familiar effects of single-word features. Specifically, experiencing the lexical-decision task first gradually emphasized the role of single-word features, and experiencing the self-paced story-reading task afterwards counteracted the effect of single-word features. We discuss the implications that task-dependence and narrative coherence might have for the organization of the mental lexicon. Future work will need to consider what architectures suit the apparent flexibility with which task can accentuate or diminish effects of single-word features.

  17. Recognition of children on age-different images: Facial morphology and age-stable features.

    Science.gov (United States)

    Caplova, Zuzana; Compassi, Valentina; Giancola, Silvio; Gibelli, Daniele M; Obertová, Zuzana; Poppa, Pasquale; Sala, Remo; Sforza, Chiarella; Cattaneo, Cristina

    2017-07-01

    The situation of missing children is one of the most emotional social issues worldwide. The search for and identification of missing children is often hampered, among others, by the fact that the facial morphology of long-term missing children changes as they grow. Nowadays, the wide coverage by surveillance systems potentially provides image material for comparisons with images of missing children that may facilitate identification. The aim of study was to identify whether facial features are stable in time and can be utilized for facial recognition by comparing facial images of children at different ages as well as to test the possible use of moles in recognition. The study was divided into two phases (1) morphological classification of facial features using an Anthropological Atlas; (2) algorithm developed in MATLAB® R2014b for assessing the use of moles as age-stable features. The assessment of facial features by Anthropological Atlases showed high mismatch percentages among observers. On average, the mismatch percentages were lower for features describing shape than for those describing size. The nose tip cleft and the chin dimple showed the best agreement between observers regarding both categorization and stability over time. Using the position of moles as a reference point for recognition of the same person on age-different images seems to be a useful method in terms of objectivity and it can be concluded that moles represent age-stable facial features that may be considered for preliminary recognition. Copyright © 2017 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.

  18. Set of Frequent Word Item sets as Feature Representation for Text with Indonesian Slang

    Science.gov (United States)

    Sa'adillah Maylawati, Dian; Putri Saptawati, G. A.

    2017-01-01

    Indonesian slang are commonly used in social media. Due to their unstructured syntax, it is difficult to extract their features based on Indonesian grammar for text mining. To do so, we propose Set of Frequent Word Item sets (SFWI) as text representation which is considered match for Indonesian slang. Besides, SFWI is able to keep the meaning of Indonesian slang with regard to the order of appearance sentence. We use FP-Growth algorithm with adding separation sentence function into the algorithm to extract the feature of SFWI. The experiments is done with text data from social media such as Facebook, Twitter, and personal website. The result of experiments shows that Indonesian slang were more correctly interpreted based on SFWI.

  19. Task-specific codes for face recognition: how they shape the neural representation of features for detection and individuation.

    Science.gov (United States)

    Nestor, Adrian; Vettel, Jean M; Tarr, Michael J

    2008-01-01

    The variety of ways in which faces are categorized makes face recognition challenging for both synthetic and biological vision systems. Here we focus on two face processing tasks, detection and individuation, and explore whether differences in task demands lead to differences both in the features most effective for automatic recognition and in the featural codes recruited by neural processing. Our study appeals to a computational framework characterizing the features representing object categories as sets of overlapping image fragments. Within this framework, we assess the extent to which task-relevant information differs across image fragments. Based on objective differences we find among task-specific representations, we test the sensitivity of the human visual system to these different face descriptions independently of one another. Both behavior and functional magnetic resonance imaging reveal effects elicited by objective task-specific levels of information. Behaviorally, recognition performance with image fragments improves with increasing task-specific information carried by different face fragments. Neurally, this sensitivity to the two tasks manifests as differential localization of neural responses across the ventral visual pathway. Fragments diagnostic for detection evoke larger neural responses than non-diagnostic ones in the right posterior fusiform gyrus and bilaterally in the inferior occipital gyrus. In contrast, fragments diagnostic for individuation evoke larger responses than non-diagnostic ones in the anterior inferior temporal gyrus. Finally, for individuation only, pattern analysis reveals sensitivity to task-specific information within the right "fusiform face area". OUR RESULTS DEMONSTRATE: 1) information diagnostic for face detection and individuation is roughly separable; 2) the human visual system is independently sensitive to both types of information; 3) neural responses differ according to the type of task-relevant information

  20. A probabilistic framework for landmark detection based on phonetic features for automatic speech recognition.

    Science.gov (United States)

    Juneja, Amit; Espy-Wilson, Carol

    2008-02-01

    A probabilistic framework for a landmark-based approach to speech recognition is presented for obtaining multiple landmark sequences in continuous speech. The landmark detection module uses as input acoustic parameters (APs) that capture the acoustic correlates of some of the manner-based phonetic features. The landmarks include stop bursts, vowel onsets, syllabic peaks and dips, fricative onsets and offsets, and sonorant consonant onsets and offsets. Binary classifiers of the manner phonetic features-syllabic, sonorant and continuant-are used for probabilistic detection of these landmarks. The probabilistic framework exploits two properties of the acoustic cues of phonetic features-(1) sufficiency of acoustic cues of a phonetic feature for a probabilistic decision on that feature and (2) invariance of the acoustic cues of a phonetic feature with respect to other phonetic features. Probabilistic landmark sequences are constrained using manner class pronunciation models for isolated word recognition with known vocabulary. The performance of the system is compared with (1) the same probabilistic system but with mel-frequency cepstral coefficients (MFCCs), (2) a hidden Markov model (HMM) based system using APs and (3) a HMM based system using MFCCs.

  1. Modeling the temporal dynamics of distinctive feature landmark detectors for speech recognition.

    Science.gov (United States)

    Jansen, Aren; Niyogi, Partha

    2008-09-01

    This paper elaborates on a computational model for speech recognition that is inspired by several interrelated strands of research in phonology, acoustic phonetics, speech perception, and neuroscience. The goals are twofold: (i) to explore frameworks for recognition that may provide a viable alternative to the current hidden Markov model (HMM) based speech recognition systems and (ii) to provide a computational platform that will facilitate engaging, quantifying, and testing various theories in the scientific traditions in phonetics, psychology, and neuroscience. This motivation leads to an approach that constructs a hierarchically structured point process representation based on distinctive feature landmark detectors and probabilistically integrates the firing patterns of these detectors to decode a phonological sequence. The accuracy of a broad class recognizer based on this framework is competitive with equivalent HMM-based systems. Various avenues for future development of the presented methodology are outlined.

  2. Human gait recognition by pyramid of HOG feature on silhouette images

    Science.gov (United States)

    Yang, Guang; Yin, Yafeng; Park, Jeanrok; Man, Hong

    2013-03-01

    As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.

  3. Language Recognition Using Latent Dynamic Conditional Random Field Model with Phonological Features

    Directory of Open Access Journals (Sweden)

    Sirinoot Boonsuk

    2014-01-01

    Full Text Available Spoken language recognition (SLR has been of increasing interest in multilingual speech recognition for identifying the languages of speech utterances. Most existing SLR approaches apply statistical modeling techniques with acoustic and phonotactic features. Among the popular approaches, the acoustic approach has become of greater interest than others because it does not require any prior language-specific knowledge. Previous research on the acoustic approach has shown less interest in applying linguistic knowledge; it was only used as supplementary features, while the current state-of-the-art system assumes independency among features. This paper proposes an SLR system based on the latent-dynamic conditional random field (LDCRF model using phonological features (PFs. We use PFs to represent acoustic characteristics and linguistic knowledge. The LDCRF model was employed to capture the dynamics of the PFs sequences for language classification. Baseline systems were conducted to evaluate the features and methods including Gaussian mixture model (GMM based systems using PFs, GMM using cepstral features, and the CRF model using PFs. Evaluated on the NIST LRE 2007 corpus, the proposed method showed an improvement over the baseline systems. Additionally, it showed comparable result with the acoustic system based on i-vector. This research demonstrates that utilizing PFs can enhance the performance.

  4. Facial Expression Recognition with Fusion Features Extracted from Salient Facial Areas.

    Science.gov (United States)

    Liu, Yanpeng; Li, Yibin; Ma, Xin; Song, Rui

    2017-03-29

    In the pattern recognition domain, deep architectures are currently widely used and they have achieved fine results. However, these deep architectures make particular demands, especially in terms of their requirement for big datasets and GPU. Aiming to gain better results without deep networks, we propose a simplified algorithm framework using fusion features extracted from the salient areas of faces. Furthermore, the proposed algorithm has achieved a better result than some deep architectures. For extracting more effective features, this paper firstly defines the salient areas on the faces. This paper normalizes the salient areas of the same location in the faces to the same size; therefore, it can extracts more similar features from different subjects. LBP and HOG features are extracted from the salient areas, fusion features' dimensions are reduced by Principal Component Analysis (PCA) and we apply several classifiers to classify the six basic expressions at once. This paper proposes a salient areas definitude method which uses peak expressions frames compared with neutral faces. This paper also proposes and applies the idea of normalizing the salient areas to align the specific areas which express the different expressions. As a result, the salient areas found from different subjects are the same size. In addition, the gamma correction method is firstly applied on LBP features in our algorithm framework which improves our recognition rates significantly. By applying this algorithm framework, our research has gained state-of-the-art performances on CK+ database and JAFFE database.

  5. LOCAL LINE BINARY PATTERN FOR FEATURE EXTRACTION ON PALM VEIN RECOGNITION

    Directory of Open Access Journals (Sweden)

    Jayanti Yusmah Sari

    2015-08-01

    Full Text Available In recent years, palm vein recognition has been studied to overcome problems in conventional systems in biometrics technology (finger print, face, and iris. Those problems in biometrics includes convenience and performance. However, due to the clarity of the palm vein image, the veins could not be segmented properly. To overcome this problem, we propose a palm vein recognition system using Local Line Binary Pattern (LLBP method that can extract robust features from the palm vein images that has unclear veins. LLBP is an advanced method of Local Binary Pattern (LBP, a texture descriptor based on the gray level comparison of a neighborhood of pixels. There are four major steps in this paper, Region of Interest (ROI detection, image preprocessing, features extraction using LLBP method, and matching using Fuzzy k-NN classifier. The proposed method was applied on the CASIA Multi-Spectral Image Database. Experimental results showed that the proposed method using LLBP has a good performance with recognition accuracy of 97.3%. In the future, experiments will be conducted to observe which parameter that could affect processing time and recognition accuracy of LLBP is needed

  6. Robust and Effective Component-based Banknote Recognition by SURF Features.

    Science.gov (United States)

    Hasanuzzaman, Faiz M; Yang, Xiaodong; Tian, YingLi

    2011-01-01

    Camera-based computer vision technology is able to assist visually impaired people to automatically recognize banknotes. A good banknote recognition algorithm for blind or visually impaired people should have the following features: 1) 100% accuracy, and 2) robustness to various conditions in different environments and occlusions. Most existing algorithms of banknote recognition are limited to work for restricted conditions. In this paper we propose a component-based framework for banknote recognition by using Speeded Up Robust Features (SURF). The component-based framework is effective in collecting more class-specific information and robust in dealing with partial occlusion and viewpoint changes. Furthermore, the evaluation of SURF demonstrates its effectiveness in handling background noise, image rotation, scale, and illumination changes. To authenticate the robustness and generalizability of the proposed approach, we have collected a large dataset of banknotes from a variety of conditions including occlusion, cluttered background, rotation, and changes of illumination, scaling, and viewpoints. The proposed algorithm achieves 100% recognition rate on our challenging dataset.

  7. Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

    Science.gov (United States)

    Mimura, Masato; Sakai, Shinsuke; Kawahara, Tatsuya

    2015-12-01

    We propose an approach to reverberant speech recognition adopting deep learning in the front-end as well as b a c k-e n d o f a r e v e r b e r a n t s p e e c h r e c o g n i t i o n s y s t e m, a n d a n o v e l m e t h o d t o i m p r o v e t h e d e r e v e r b e r a t i o n p e r f o r m a n c e of the front-end network using phone-class information. At the front-end, we adopt a deep autoencoder (DAE) for enhancing the speech feature parameters, and speech recognition is performed in the back-end using DNN-HMM acoustic models trained on multi-condition data. The system was evaluated through the ASR task in the Reverb Challenge 2014. The DNN-HMM system trained on the multi-condition training set achieved a conspicuously higher word accuracy compared to the MLLR-adapted GMM-HMM system trained on the same data. Furthermore, feature enhancement with the deep autoencoder contributed to the improvement of recognition accuracy especially in the more adverse conditions. While the mapping between reverberant and clean speech in DAE-based dereverberation is conventionally conducted only with the acoustic information, we presume the mapping is also dependent on the phone information. Therefore, we propose a new scheme (pDAE), which augments a phone-class feature to the standard acoustic features as input. Two types of the phone-class feature are investigated. One is the hard recognition result of monophones, and the other is a soft representation derived from the posterior outputs of monophone DNN. The augmented feature in either type results in a significant improvement (7-8 % relative) from the standard DAE.

  8. Behavioral features recognition and oestrus detection based on fast approximate clustering algorithm in dairy cows

    Science.gov (United States)

    Tian, Fuyang; Cao, Dong; Dong, Xiaoning; Zhao, Xinqiang; Li, Fade; Wang, Zhonghua

    2017-06-01

    Behavioral features recognition was an important effect to detect oestrus and sickness in dairy herds and there is a need for heat detection aid. The detection method was based on the measure of the individual behavioural activity, standing time, and temperature of dairy using vibrational sensor and temperature sensor in this paper. The data of behavioural activity index, standing time, lying time and walking time were sent to computer by lower power consumption wireless communication system. The fast approximate K-means algorithm (FAKM) was proposed to deal the data of the sensor for behavioral features recognition. As a result of technical progress in monitoring cows using computers, automatic oestrus detection has become possible.

  9. Fusion of EEG and Musical Features in Continuous Music-emotion Recognition

    OpenAIRE

    Thammasan, Nattapong; Fukui, Ken-ichi; Numao, Masayuki

    2016-01-01

    Emotion estimation in music listening is confronting challenges to capture the emotion variation of listeners. Recent years have witnessed attempts to exploit multimodality fusing information from musical contents and physiological signals captured from listeners to improve the performance of emotion recognition. In this paper, we present a study of fusion of signals of electroencephalogram (EEG), a tool to capture brainwaves at a high-temporal resolution, and musical features at decision lev...

  10. Multimodal emotional state recognition using sequence-dependent deep hierarchical features.

    Science.gov (United States)

    Barros, Pablo; Jirak, Doreen; Weber, Cornelius; Wermter, Stefan

    2015-12-01

    Emotional state recognition has become an important topic for human-robot interaction in the past years. By determining emotion expressions, robots can identify important variables of human behavior and use these to communicate in a more human-like fashion and thereby extend the interaction possibilities. Human emotions are multimodal and spontaneous, which makes them hard to be recognized by robots. Each modality has its own restrictions and constraints which, together with the non-structured behavior of spontaneous expressions, create several difficulties for the approaches present in the literature, which are based on several explicit feature extraction techniques and manual modality fusion. Our model uses a hierarchical feature representation to deal with spontaneous emotions, and learns how to integrate multiple modalities for non-verbal emotion recognition, making it suitable to be used in an HRI scenario. Our experiments show that a significant improvement of recognition accuracy is achieved when we use hierarchical features and multimodal information, and our model improves the accuracy of state-of-the-art approaches from 82.5% reported in the literature to 91.3% for a benchmark dataset on spontaneous emotion expressions. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  11. Feature and Score Fusion Based Multiple Classifier Selection for Iris Recognition

    Directory of Open Access Journals (Sweden)

    Md. Rabiul Islam

    2014-01-01

    Full Text Available The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existing N hamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al.

  12. A Kinect based sign language recognition system using spatio-temporal features

    Science.gov (United States)

    Memiş, Abbas; Albayrak, Songül

    2013-12-01

    This paper presents a sign language recognition system that uses spatio-temporal features on RGB video images and depth maps for dynamic gestures of Turkish Sign Language. Proposed system uses motion differences and accumulation approach for temporal gesture analysis. Motion accumulation method, which is an effective method for temporal domain analysis of gestures, produces an accumulated motion image by combining differences of successive video frames. Then, 2D Discrete Cosine Transform (DCT) is applied to accumulated motion images and temporal domain features transformed into spatial domain. These processes are performed on both RGB images and depth maps separately. DCT coefficients that represent sign gestures are picked up via zigzag scanning and feature vectors are generated. In order to recognize sign gestures, K-Nearest Neighbor classifier with Manhattan distance is performed. Performance of the proposed sign language recognition system is evaluated on a sign database that contains 1002 isolated dynamic signs belongs to 111 words of Turkish Sign Language (TSL) in three different categories. Proposed sign language recognition system has promising success rates.

  13. An alternative to scale-space representation for extracting local features in image recognition

    DEFF Research Database (Denmark)

    Andersen, Hans Jørgen; Nguyen, Phuong Giang

    2012-01-01

    In image recognition, the common approach for extracting local features using a scale-space representation has usually three main steps; first interest points are extracted at different scales, next from a patch around each interest point the rotation is calculated with corresponding orientation...... and compensation, and finally a descriptor is computed for the derived patch (i.e. feature of the patch). To avoid the memory and computational intensive process of constructing the scale-space, we use a method where no scale-space is required This is done by dividing the given image into a number of triangles...

  14. Facial Expression Recognition with Fusion Features Extracted from Salient Facial Areas

    Science.gov (United States)

    Liu, Yanpeng; Li, Yibin; Ma, Xin; Song, Rui

    2017-01-01

    In the pattern recognition domain, deep architectures are currently widely used and they have achieved fine results. However, these deep architectures make particular demands, especially in terms of their requirement for big datasets and GPU. Aiming to gain better results without deep networks, we propose a simplified algorithm framework using fusion features extracted from the salient areas of faces. Furthermore, the proposed algorithm has achieved a better result than some deep architectures. For extracting more effective features, this paper firstly defines the salient areas on the faces. This paper normalizes the salient areas of the same location in the faces to the same size; therefore, it can extracts more similar features from different subjects. LBP and HOG features are extracted from the salient areas, fusion features’ dimensions are reduced by Principal Component Analysis (PCA) and we apply several classifiers to classify the six basic expressions at once. This paper proposes a salient areas definitude method which uses peak expressions frames compared with neutral faces. This paper also proposes and applies the idea of normalizing the salient areas to align the specific areas which express the different expressions. As a result, the salient areas found from different subjects are the same size. In addition, the gamma correction method is firstly applied on LBP features in our algorithm framework which improves our recognition rates significantly. By applying this algorithm framework, our research has gained state-of-the-art performances on CK+ database and JAFFE database. PMID:28353671

  15. Dorsal hand vein recognition based on Gabor multi-orientation fusion and multi-scale HOG features

    Science.gov (United States)

    Han, Tuo; Wang, Zhiyong; Yang, Xiaoping

    2016-10-01

    Kinds of factors such as illumination and hand gestures would reduce the accuracy of dorsal hand vein recognition. Aiming at single hand vein image with low contrast and simple structure, an algorithm combining Gabor multi-orientation features fusion with Multi-scale Histogram of Oriented Gradient (MS-HOG) is proposed in this paper. With this method, more features will be extracted to improve the recognition accuracy. Firstly, diagrams of multi-scale and multi-orientation are acquired using Gabor transformation, then the Gabor features of the same scale and multi-orientation will be fused, and the features of the correspondent fusion diagrams will be extracted with a HOG operator of a certain scale. Finally the multi-scale cascaded histograms will be obtained for hand vein recognition. The experimental results show that our method not only improve the recognition accuracy but has good robustness in dorsal hand vein recognition.

  16. Extraction Of Audio Features For Emotion Recognition System Based On Music

    Directory of Open Access Journals (Sweden)

    Kee Moe Han

    2015-08-01

    Full Text Available Music is the combination of melody linguistic information and the vocalists emotion. Since music is a work of art analyzing emotion in music by computer is a difficult task. Many approaches have been developed to detect the emotions included in music but the results are not satisfactory because emotion is very complex. In this paper the evaluations of audio features from the music files are presented. The extracted features are used to classify the different emotion classes of the vocalists. Musical features extraction is done by using Music Information Retrieval MIR tool box in this paper. The database of 100 music clips are used to classify the emotions perceived in music clips. Music may contain many emotions according to the vocalists mood such as happy sad nervous bored peace etc. In this paper the audio features related to the emotions of the vocalists are extracted to use in emotion recognition system based on music.

  17. Several Similarity Measures of Interval Valued Neutrosophic Soft Sets and Their Application in Pattern Recognition Problems

    Directory of Open Access Journals (Sweden)

    Anjan Mukherjee

    2014-12-01

    Full Text Available Interval valued neutrosophic soft set introduced by Irfan Deli in 2014[8] is a generalization of neutrosophic set introduced by F. Smarandache in 1995[19], which can be used in real scientific and engineering applications. In this paper the Hamming and Euclidean distances between two interval valued neutrosophic soft sets (IVNS sets are defined and similarity measures based on distances between two interval valued neutrosophic soft sets are proposed. Similarity measure based on set theoretic approach is also proposed. Some basic properties of similarity measures between two interval valued neutrosophic soft sets is also studied. A decision making method is established for interval valued neutrosophic soft set setting using similarity measures between IVNS sets. Finally an example is given to demonstrate the possible application of similarity measures in pattern recognition problems.

  18. Contributions of feature shapes and surface cues to the recognition and neural representation of facial identity.

    Science.gov (United States)

    Andrews, Timothy J; Baseler, Heidi; Jenkins, Rob; Burton, A Mike; Young, Andrew W

    2016-10-01

    A full understanding of face recognition will involve identifying the visual information that is used to discriminate different identities and how this is represented in the brain. The aim of this study was to explore the importance of shape and surface properties in the recognition and neural representation of familiar faces. We used image morphing techniques to generate hybrid faces that mixed shape properties (more specifically, second order spatial configural information as defined by feature positions in the 2D-image) from one identity and surface properties from a different identity. Behavioural responses showed that recognition and matching of these hybrid faces was primarily based on their surface properties. These behavioural findings contrasted with neural responses recorded using a block design fMRI adaptation paradigm to test the sensitivity of Haxby et al.'s (2000) core face-selective regions in the human brain to the shape or surface properties of the face. The fusiform face area (FFA) and occipital face area (OFA) showed a lower response (adaptation) to repeated images of the same face (same shape, same surface) compared to different faces (different shapes, different surfaces). From the behavioural data indicating the critical contribution of surface properties to the recognition of identity, we predicted that brain regions responsible for familiar face recognition should continue to adapt to faces that vary in shape but not surface properties, but show a release from adaptation to faces that vary in surface properties but not shape. However, we found that the FFA and OFA showed an equivalent release from adaptation to changes in both shape and surface properties. The dissociation between the neural and perceptual responses suggests that, although they may play a role in the process, these core face regions are not solely responsible for the recognition of facial identity. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Using Mutual Information Criterion to Design an Efficient Phoneme Set for Chinese Speech Recognition

    Science.gov (United States)

    Zhang, Jin-Song; Hu, Xin-Hui; Nakamura, Satoshi

    Chinese is a representative tonal language, and it has been an attractive topic of how to process tone information in the state-of-the-art large vocabulary speech recognition system. This paper presents a novel way to derive an efficient phoneme set of tone-dependent units to build a recognition system, by iteratively merging a pair of tone-dependent units according to the principle of minimal loss of the Mutual Information (MI). The mutual information is measured between the word tokens and their phoneme transcriptions in a training text corpus, based on the system lexical and language model. The approach has a capability to keep discriminative tonal (and phoneme) contrasts that are most helpful for disambiguating homophone words due to lack of tones, and merge those tonal (and phoneme) contrasts that are not important for word disambiguation for the recognition task. This enables a flexible selection of phoneme set according to a balance between the MI information amount and the number of phonemes. We applied the method to traditional phoneme set of Initial/Finals, and derived several phoneme sets with different number of units. Speech recognition experiments using the derived sets showed its effectiveness.

  20. A Dynamic Interval-Valued Intuitionistic Fuzzy Sets Applied to Pattern Recognition

    Directory of Open Access Journals (Sweden)

    Zhenhua Zhang

    2013-01-01

    Full Text Available We present dynamic interval-valued intuitionistic fuzzy sets (DIVIFS, which can improve the recognition accuracy when they are applied to pattern recognition. By analyzing the degree of hesitancy, we propose some DIVIFS models from intuitionistic fuzzy sets (IFS and interval-valued IFS (IVIFS. And then we present a novel ranking condition on the distance of IFS and IVIFS and introduce some distance measures of DIVIFS satisfying the ranking condition. Finally, a pattern recognition example applied to medical diagnosis decision making is given to demonstrate the application of DIVIFS and its distances. The simulation results show that the DIVIFS method is more comprehensive and flexible than the IFS method and the IVIFS method.

  1. The Role of External Features in Face Recognition with Central Vision Loss.

    Science.gov (United States)

    Bernard, Jean-Baptiste; Chung, Susana T L

    2016-05-01

    We evaluated how the performance of recognizing familiar face images depends on the internal (eyebrows, eyes, nose, mouth) and external face features (chin, outline of face, hairline) in individuals with central vision loss. In experiment 1, we measured eye movements for four observers with central vision loss to determine whether they fixated more often on the internal or the external features of face images while attempting to recognize the images. We then measured the accuracy for recognizing face images that contained only the internal, only the external, or both internal and external features (experiment 2) and for hybrid images where the internal and external features came from two different source images (experiment 3) for five observers with central vision loss and four age-matched control observers. When recognizing familiar face images, approximately 40% of the fixations of observers with central vision loss was centered on the external features of faces. The recognition accuracy was higher for images containing only external features (66.8 ± 3.3% correct) than for images containing only internal features (35.8 ± 15.0%), a finding contradicting that of control observers. For hybrid face images, observers with central vision loss responded more accurately to the external features (50.4 ± 17.8%) than to the internal features (9.3 ± 4.9%), whereas control observers did not show the same bias toward responding to the external features. Contrary to people with normal vision who rely more on the internal features of face images for recognizing familiar faces, individuals with central vision loss show a higher dependence on using external features of face images.

  2. Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

    Directory of Open Access Journals (Sweden)

    Hongqiang Li

    2016-10-01

    Full Text Available Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

  3. Faces and facets: The variability of emotion recognition in psychopathy reflects its affective and antisocial features.

    Science.gov (United States)

    Igoumenou, Artemis; Harmer, Catherine J; Yang, Min; Coid, Jeremy W; Rogers, Robert D

    2017-11-01

    Psychopathy consists of a constellation of affective-interpersonal features including lack of empathy, callousness, manipulativeness and interpersonal charm, impulsiveness and irresponsibility. Despite its theoretical and predictive value in forensic contexts, the relationships between the psychometric dimensions of psychopathy, including its antisocial features, and the construct's neuropsychological characteristics remain uncertain. In this study, 685 personality-disordered prisoners with histories of serious violent or sexual offenses were assessed for psychopathy before completing a computerized and well-validated assessment of the ability to recognize emotional expressions in the face. Prisoners with more of the affective features of psychopathy, and prisoners with more of its antisocial manifestations, showed relatively poor recognition accuracy of fearfulness and disgust. These relationships were independent and modest but were still evident following correction for demographic features (e.g., ethnicity and socioeconomic status), mental illness (e.g., substance and alcohol misuse), personality disorders (other than antisocial personality disorder) and treatment status. By contrast, the associations between these dimensions of psychopathy and emotion recognition were diminished by controlling for cognitive ability. These findings demonstrate that variability in the ability of high-risk personality-disordered prisoners to recognize emotional expressions in the face-in particular, fear and disgust-reflects both the affective and antisocial aspects of psychopathy, and is moderated by cognitive ability. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  4. Contributions of feature shapes and surface cues to the recognition of facial expressions.

    Science.gov (United States)

    Sormaz, Mladen; Young, Andrew W; Andrews, Timothy J

    2016-10-01

    Theoretical accounts of face processing often emphasise feature shapes as the primary visual cue to the recognition of facial expressions. However, changes in facial expression also affect the surface properties of the face. In this study, we investigated whether this surface information can also be used in the recognition of facial expression. First, participants identified facial expressions (fear, anger, disgust, sadness, happiness) from images that were manipulated such that they varied mainly in shape or mainly in surface properties. We found that the categorization of facial expression is possible in either type of image, but that different expressions are relatively dependent on surface or shape properties. Next, we investigated the relative contributions of shape and surface information to the categorization of facial expressions. This employed a complementary method that involved combining the surface properties of one expression with the shape properties from a different expression. Our results showed that the categorization of facial expressions in these hybrid images was equally dependent on the surface and shape properties of the image. Together, these findings provide a direct demonstration that both feature shape and surface information make significant contributions to the recognition of facial expressions. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. Comparative Study on Feature Selection and Fusion Schemes for Emotion Recognition from Speech

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    Santiago Planet

    2012-09-01

    Full Text Available The automatic analysis of speech to detect affective states may improve the way users interact with electronic devices. However, the analysis only at the acoustic level could be not enough to determine the emotion of a user in a realistic scenario. In this paper we analyzed the spontaneous speech recordings of the FAU Aibo Corpus at the acoustic and linguistic levels to extract two sets of features. The acoustic set was reduced by a greedy procedure selecting the most relevant features to optimize the learning stage. We compared two versions of this greedy selection algorithm by performing the search of the relevant features forwards and backwards. We experimented with three classification approaches: Naïve-Bayes, a support vector machine and a logistic model tree, and two fusion schemes: decision-level fusion, merging the hard-decisions of the acoustic and linguistic classifiers by means of a decision tree; and feature-level fusion, concatenating both sets of features before the learning stage. Despite the low performance achieved by the linguistic data, a dramatic improvement was achieved after its combination with the acoustic information, improving the results achieved by this second modality on its own. The results achieved by the classifiers using the parameters merged at feature level outperformed the classification results of the decision-level fusion scheme, despite the simplicity of the scheme. Moreover, the extremely reduced set of acoustic features obtained by the greedy forward search selection algorithm improved the results provided by the full set.

  6. Sparse Contribution Feature Selection and Classifiers Optimized by Concave-Convex Variation for HCC Image Recognition.

    Science.gov (United States)

    Pang, Wenbo; Jiang, Huiyan; Li, Siqi

    2017-01-01

    Accurate classification of hepatocellular carcinoma (HCC) image is of great importance in pathology diagnosis and treatment. This paper proposes a concave-convex variation (CCV) method to optimize three classifiers (random forest, support vector machine, and extreme learning machine) for the more accurate HCC image classification results. First, in preprocessing stage, hematoxylin-eosin (H&E) pathological images are enhanced using bilateral filter and each HCC image patch is obtained under the guidance of pathologists. Then, after extracting the complete features of each patch, a new sparse contribution (SC) feature selection model is established to select the beneficial features for each classifier. Finally, a concave-convex variation method is developed to improve the performance of classifiers. Experiments using 1260 HCC image patches demonstrate that our proposed CCV classifiers have improved greatly compared to each original classifier and CCV-random forest (CCV-RF) performs the best for HCC image recognition.

  7. Noise Robust Feature Scheme for Automatic Speech Recognition Based on Auditory Perceptual Mechanisms

    Science.gov (United States)

    Cai, Shang; Xiao, Yeming; Pan, Jielin; Zhao, Qingwei; Yan, Yonghong

    Mel Frequency Cepstral Coefficients (MFCC) are the most popular acoustic features used in automatic speech recognition (ASR), mainly because the coefficients capture the most useful information of the speech and fit well with the assumptions used in hidden Markov models. As is well known, MFCCs already employ several principles which have known counterparts in the peripheral properties of human hearing: decoupling across frequency, mel-warping of the frequency axis, log-compression of energy, etc. It is natural to introduce more mechanisms in the auditory periphery to improve the noise robustness of MFCC. In this paper, a k-nearest neighbors based frequency masking filter is proposed to reduce the audibility of spectra valleys which are sensitive to noise. Besides, Moore and Glasberg's critical band equivalent rectangular bandwidth (ERB) expression is utilized to determine the filter bandwidth. Furthermore, a new bandpass infinite impulse response (IIR) filter is proposed to imitate the temporal masking phenomenon of the human auditory system. These three auditory perceptual mechanisms are combined with the standard MFCC algorithm in order to investigate their effects on ASR performance, and a revised MFCC extraction scheme is presented. Recognition performances with the standard MFCC, RASTA perceptual linear prediction (RASTA-PLP) and the proposed feature extraction scheme are evaluated on a medium-vocabulary isolated-word recognition task and a more complex large vocabulary continuous speech recognition (LVCSR) task. Experimental results show that consistent robustness against background noise is achieved on these two tasks, and the proposed method outperforms both the standard MFCC and RASTA-PLP.

  8. Face recognition via edge-based Gabor feature representation for plastic surgery-altered images

    Science.gov (United States)

    Chude-Olisah, Chollette C.; Sulong, Ghazali; Chude-Okonkwo, Uche A. K.; Hashim, Siti Z. M.

    2014-12-01

    Plastic surgery procedures on the face introduce skin texture variations between images of the same person (intra-subject), thereby making the task of face recognition more difficult than in normal scenario. Usually, in contemporary face recognition systems, the original gray-level face image is used as input to the Gabor descriptor, which translates to encoding some texture properties of the face image. The texture-encoding process significantly degrades the performance of such systems in the case of plastic surgery due to the presence of surgically induced intra-subject variations. Based on the proposition that the shape of significant facial components such as eyes, nose, eyebrow, and mouth remains unchanged after plastic surgery, this paper employs an edge-based Gabor feature representation approach for the recognition of surgically altered face images. We use the edge information, which is dependent on the shapes of the significant facial components, to address the plastic surgery-induced texture variation problems. To ensure that the significant facial components represent useful edge information with little or no false edges, a simple illumination normalization technique is proposed for preprocessing. Gabor wavelet is applied to the edge image to accentuate on the uniqueness of the significant facial components for discriminating among different subjects. The performance of the proposed method is evaluated on the Georgia Tech (GT) and the Labeled Faces in the Wild (LFW) databases with illumination and expression problems, and the plastic surgery database with texture changes. Results show that the proposed edge-based Gabor feature representation approach is robust against plastic surgery-induced face variations amidst expression and illumination problems and outperforms the existing plastic surgery face recognition methods reported in the literature.

  9. Own- and Other-Race Face Identity Recognition in Children: The Effects of Pose and Feature Composition

    Science.gov (United States)

    Anzures, Gizelle; Kelly, David J.; Pascalis, Olivier; Quinn, Paul C.; Slater, Alan M.; de Viviés, Xavier; Lee, Kang

    2014-01-01

    We used a matching-to-sample task and manipulated facial pose and feature composition to examine the other-race effect (ORE) in face identity recognition between 5 and 10 years of age. Overall, the present findings provide a genuine measure of own- and other-race face identity recognition in children that is independent of photographic and image…

  10. Robust Automatic Speech Recognition Features using Complex Wavelet Packet Transform Coefficients

    Directory of Open Access Journals (Sweden)

    TjongWan Sen

    2009-11-01

    Full Text Available To improve the performance of phoneme based Automatic Speech Recognition (ASR in noisy environment; we developed a new technique that could add robustness to clean phonemes features. These robust features are obtained from Complex Wavelet Packet Transform (CWPT coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would also cover all frequencies involved in recognition process. For time overlapping signals with different frequency contents, e. g. phoneme signal with noises, its CWPT coefficients are the combination of CWPT coefficients of phoneme signal and CWPT coefficients of noises. The CWPT coefficients of phonemes signal would be changed according to frequency components contained in noises. Since the numbers of phonemes in every language are relatively small (limited and already well known, one could easily derive principal component vectors from clean training dataset using Principal Component Analysis (PCA. These principal component vectors could be used then to add robustness and minimize noises effects in testing phase. Simulation results, using Alpha Numeric 4 (AN4 from Carnegie Mellon University and NOISEX-92 examples from Rice University, showed that this new technique could be used as features extractor that improves the robustness of phoneme based ASR systems in various adverse noisy conditions and still preserves the performance in clean environments.

  11. Robust Automatic Speech Recognition Features using Complex Wavelet Packet Transform Coefficients

    Directory of Open Access Journals (Sweden)

    Tjong Wan Sen

    2013-09-01

    Full Text Available To improve the performance of phoneme based Automatic Speech Recognition (ASR in noisy environment; we developed a new technique that could add robustness to clean phonemes features. These robust features are obtained from Complex Wavelet Packet Transform (CWPT coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would also cover all frequencies involved in recognition process. For time overlapping signals with different frequency contents, e. g. phoneme signal with noises, its CWPT coefficients are the combination of CWPT coefficients of phoneme signal and CWPT coefficients of noises. The CWPT coefficients of phonemes signal would be changed according to frequency components contained in noises. Since the numbers of phonemes in every language are relatively small (limited and already well known, one could easily derive principal component vectors from clean training dataset using Principal Component Analysis (PCA. These principal component vectors could be used then to add robustness and minimize noises effects in testing phase. Simulation results, using Alpha Numeric 4 (AN4 from Carnegie Mellon University and NOISEX-92 examples from Rice University, showed that this new technique could be used as features extractor that improves the robustness of phoneme based ASR systems in various adverse noisy conditions and still preserves the performance in clean environments.

  12. Personal recognition using finger knuckle shape oriented features and texture analysis

    Directory of Open Access Journals (Sweden)

    K. Usha

    2016-10-01

    Full Text Available Finger knuckle print is considered as one of the emerging hand biometric traits due to its potentiality toward the identification of individuals. This paper contributes a new method for personal recognition using finger knuckle print based on two approaches namely, geometric and texture analyses. In the first approach, the shape oriented features of the finger knuckle print are extracted by means of angular geometric analysis and then integrated to achieve better precision rate. Whereas, the knuckle texture feature analysis is carried out by means of multi-resolution transform known as Curvelet transform. This Curvelet transform has the ability to approximate curved singularities with minimum number of Curvelet coefficients. Since, finger knuckle patterns mainly consist of lines and curves, Curvelet transform is highly suitable for its representation. Further, the Curvelet transform decomposes the finger knuckle image into Curvelet sub-bands which are termed as ‘Curvelet knuckle’. Finally, principle component analysis is applied on each Curvelet knuckle for extracting its feature vector through the covariance matrix derived from their Curvelet coefficients. Extensive experiments were conducted using PolyU database and IIT finger knuckle database. The experimental results confirm that, our proposed method shows a high recognition rate of 98.72% with lower false acceptance rate of 0.06%.

  13. A food recognition system for diabetic patients based on an optimized bag-of-features model.

    Science.gov (United States)

    Anthimopoulos, Marios M; Gianola, Lauro; Scarnato, Luca; Diem, Peter; Mougiakakou, Stavroula G

    2014-07-01

    Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

  14. Deep Visual Attributes vs. Hand-Crafted Audio Features on Multidomain Speech Emotion Recognition

    Directory of Open Access Journals (Sweden)

    Michalis Papakostas

    2017-06-01

    Full Text Available Emotion recognition from speech may play a crucial role in many applications related to human–computer interaction or understanding the affective state of users in certain tasks, where other modalities such as video or physiological parameters are unavailable. In general, a human’s emotions may be recognized using several modalities such as analyzing facial expressions, speech, physiological parameters (e.g., electroencephalograms, electrocardiograms etc. However, measuring of these modalities may be difficult, obtrusive or require expensive hardware. In that context, speech may be the best alternative modality in many practical applications. In this work we present an approach that uses a Convolutional Neural Network (CNN functioning as a visual feature extractor and trained using raw speech information. In contrast to traditional machine learning approaches, CNNs are responsible for identifying the important features of the input thus, making the need of hand-crafted feature engineering optional in many tasks. In this paper no extra features are required other than the spectrogram representations and hand-crafted features were only extracted for validation purposes of our method. Moreover, it does not require any linguistic model and is not specific to any particular language. We compare the proposed approach using cross-language datasets and demonstrate that it is able to provide superior results vs. traditional ones that use hand-crafted features.

  15. Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors

    Directory of Open Access Journals (Sweden)

    Frédéric Li

    2018-02-01

    Full Text Available Getting a good feature representation of data is paramount for Human Activity Recognition (HAR using wearable sensors. An increasing number of feature learning approaches—in particular deep-learning based—have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM to obtain features characterising both short- and long-term time dependencies in the data.

  16. Feature activation during word recognition: action, visual, and associative-semantic priming effects

    Directory of Open Access Journals (Sweden)

    Kevin J.Y. Lam

    2015-05-01

    Full Text Available Embodied theories of language postulate that language meaning is stored in modality-specific brain areas generally involved in perception and action in the real world. However, the temporal dynamics of the interaction between modality-specific information and lexical-semantic processing remain unclear. We investigated the relative timing at which two types of modality-specific information (action-based and visual-form information contribute to lexical-semantic comprehension. To this end, we applied a behavioral priming paradigm in which prime and target words were related with respect to (1 action features, (2 visual features, or (3 semantically associative information. Using a Go/No-Go lexical decision task, priming effects were measured across four different inter-stimulus intervals (ISI = 100 ms, 250 ms, 400 ms, and 1,000 ms to determine the relative time course of the different features . Notably, action priming effects were found in ISIs of 100 ms, 250 ms, and 1,000 ms whereas a visual priming effect was seen only in the ISI of 1,000 ms. Importantly, our data suggest that features follow different time courses of activation during word recognition. In this regard, feature activation is dynamic, measurable in specific time windows but not in others. Thus the current study (1 demonstrates how multiple ISIs can be used within an experiment to help chart the time course of feature activation and (2 provides new evidence for embodied theories of language.

  17. Feature activation during word recognition: action, visual, and associative-semantic priming effects.

    Science.gov (United States)

    Lam, Kevin J Y; Dijkstra, Ton; Rueschemeyer, Shirley-Ann

    2015-01-01

    Embodied theories of language postulate that language meaning is stored in modality-specific brain areas generally involved in perception and action in the real world. However, the temporal dynamics of the interaction between modality-specific information and lexical-semantic processing remain unclear. We investigated the relative timing at which two types of modality-specific information (action-based and visual-form information) contribute to lexical-semantic comprehension. To this end, we applied a behavioral priming paradigm in which prime and target words were related with respect to (1) action features, (2) visual features, or (3) semantically associative information. Using a Go/No-Go lexical decision task, priming effects were measured across four different inter-stimulus intervals (ISI = 100, 250, 400, and 1000 ms) to determine the relative time course of the different features. Notably, action priming effects were found in ISIs of 100, 250, and 1000 ms whereas a visual priming effect was seen only in the ISI of 1000 ms. Importantly, our data suggest that features follow different time courses of activation during word recognition. In this regard, feature activation is dynamic, measurable in specific time windows but not in others. Thus the current study (1) demonstrates how multiple ISIs can be used within an experiment to help chart the time course of feature activation and (2) provides new evidence for embodied theories of language.

  18. Robust speech recognition based on joint model and feature space optimization of hidden Markov models.

    Science.gov (United States)

    Moon, S; Hwang, J N

    1997-01-01

    The hidden Markov model (HMM) inversion algorithm, based on either the gradient search or the Baum-Welch reestimation of input speech features, is proposed and applied to the robust speech recognition tasks under general types of mismatch conditions. This algorithm stems from the gradient-based inversion algorithm of an artificial neural network (ANN) by viewing an HMM as a special type of ANN. Given input speech features s, the forward training of an HMM finds the model parameters lambda subject to an optimization criterion. On the other hand, the inversion of an HMM finds speech features, s, subject to an optimization criterion with given model parameters lambda. The gradient-based HMM inversion and the Baum-Welch HMM inversion algorithms can be successfully integrated with the model space optimization techniques, such as the robust MINIMAX technique, to compensate the mismatch in the joint model and feature space. The joint space mismatch compensation technique achieves better performance than the single space, i.e. either the model space or the feature space alone, mismatch compensation techniques. It is also demonstrated that approximately 10-dB signal-to-noise ratio (SNR) gain is obtained in the low SNR environments when the joint model and feature space mismatch compensation technique is used.

  19. An Application of Discriminant Analysis to Pattern Recognition of Selected Contaminated Soil Features in Thin Sections

    DEFF Research Database (Denmark)

    Ribeiro, Alexandra B.; Nielsen, Allan Aasbjerg

    1997-01-01

    qualitative microprobe results: present elements Al, Si, Cr, Fe, As (associated with others). Selected groups of calibrated images (same light conditions and magnification) submitted to discriminant analysis, in order to find a pattern of recognition in the soil features corresponding to contamination already...... concentrations of contaminants are indicated by chemical wet analysis, these contaminants must occur directly in the solid phase. Thin sections of soil aggregates were scanned for Cu, Cr and As using an electron microprobe, and qualitative analysis was made on selected areas. Microphotographs of thin sections...

  20. Is the emotion recognition deficit associated with frontotemporal dementia caused by selective inattention to diagnostic facial features?

    Science.gov (United States)

    Oliver, Lindsay D; Virani, Karim; Finger, Elizabeth C; Mitchell, Derek G V

    2014-07-01

    Frontotemporal dementia (FTD) is a debilitating neurodegenerative disorder characterized by severely impaired social and emotional behaviour, including emotion recognition deficits. Though fear recognition impairments seen in particular neurological and developmental disorders can be ameliorated by reallocating attention to critical facial features, the possibility that similar benefits can be conferred to patients with FTD has yet to be explored. In the current study, we examined the impact of presenting distinct regions of the face (whole face, eyes-only, and eyes-removed) on the ability to recognize expressions of anger, fear, disgust, and happiness in 24 patients with FTD and 24 healthy controls. A recognition deficit was demonstrated across emotions by patients with FTD relative to controls. Crucially, removal of diagnostic facial features resulted in an appropriate decline in performance for both groups; furthermore, patients with FTD demonstrated a lack of disproportionate improvement in emotion recognition accuracy as a result of isolating critical facial features relative to controls. Thus, unlike some neurological and developmental disorders featuring amygdala dysfunction, the emotion recognition deficit observed in FTD is not likely driven by selective inattention to critical facial features. Patients with FTD also mislabelled negative facial expressions as happy more often than controls, providing further evidence for abnormalities in the representation of positive affect in FTD. This work suggests that the emotional expression recognition deficit associated with FTD is unlikely to be rectified by adjusting selective attention to diagnostic features, as has proven useful in other select disorders. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. Enlarge the training set based on inter-class relationship for face recognition from one image per person.

    Directory of Open Access Journals (Sweden)

    Qin Li

    Full Text Available In some large-scale face recognition task, such as driver license identification and law enforcement, the training set only contains one image per person. This situation is referred to as one sample problem. Because many face recognition techniques implicitly assume that several (at least two images per person are available for training, they cannot deal with the one sample problem. This paper investigates principal component analysis (PCA, Fisher linear discriminant analysis (LDA, and locality preserving projections (LPP and shows why they cannot perform well in one sample problem. After that, this paper presents four reasons that make one sample problem itself difficult: the small sample size problem; the lack of representative samples; the underestimated intra-class variation; and the overestimated inter-class variation. Based on the analysis, this paper proposes to enlarge the training set based on the inter-class relationship. This paper also extends LDA and LPP to extract features from the enlarged training set. The experimental results show the effectiveness of the proposed method.

  2. Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition.

    Science.gov (United States)

    Fong, Simon; Song, Wei; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

    2017-02-27

    In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called 'shadow features' are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

  3. [The comparison between mental image manipulation and distinctive feature scan on recognition memory of faces].

    Science.gov (United States)

    Kihara, K; Yoshikawa, S

    2001-08-01

    The authors proposed "mental image manipulation of expression" as processing strategy for faces, and investigated whether this strategy facilitates memory for faces or not. In the Experiment, four groups of subjects were assigned to a combination of a task (mental image manipulation of expression or distinctive feature scan) and a retention interval (short-term latency or long-term latency). Each task was followed by an unexpected yes-no recognition test in which identical pictures of the target faces or the same person's expression-changed faces were randomly presented with distractor faces. The mental image manipulation group was better than distinctive feature scan group in long-term storage. This result is considered as a long-term effect of imagery encoding and a configurational encoding by mental image manipulation.

  4. Data-driven spatio-temporal RGBD feature encoding for action recognition in operating rooms.

    Science.gov (United States)

    Twinanda, Andru P; Alkan, Emre O; Gangi, Afshin; de Mathelin, Michel; Padoy, Nicolas

    2015-06-01

    Context-aware systems for the operating room (OR) provide the possibility to significantly improve surgical workflow through various applications such as efficient OR scheduling, context-sensitive user interfaces, and automatic transcription of medical procedures. Being an essential element of such a system, surgical action recognition is thus an important research area. In this paper, we tackle the problem of classifying surgical actions from video clips that capture the activities taking place in the OR. We acquire recordings using a multi-view RGBD camera system mounted on the ceiling of a hybrid OR dedicated to X-ray-based procedures and annotate clips of the recordings with the corresponding actions. To recognize the surgical actions from the video clips, we use a classification pipeline based on the bag-of-words (BoW) approach. We propose a novel feature encoding method that extends the classical BoW approach. Instead of using the typical rigid grid layout to divide the space of the feature locations, we propose to learn the layout from the actual 4D spatio-temporal locations of the visual features. This results in a data-driven and non-rigid layout which retains more spatio-temporal information compared to the rigid counterpart. We classify multi-view video clips from a new dataset generated from 11-day recordings of real operations. This dataset is composed of 1734 video clips of 15 actions. These include generic actions (e.g., moving patient to the OR bed) and actions specific to the vertebroplasty procedure (e.g., hammering). The experiments show that the proposed non-rigid feature encoding method performs better than the rigid encoding one. The classifier's accuracy is increased by over 4 %, from 81.08 to 85.53 %. The combination of both intensity and depth information from the RGBD data provides more discriminative power in carrying out the surgical action recognition task as compared to using either one of them alone. Furthermore, the proposed non

  5. New Missing Features Mask Estimation Method for Speaker Recognition in Noisy Environments

    Directory of Open Access Journals (Sweden)

    José Ramón Calvo de Lara

    2012-06-01

    Full Text Available Normal 0 21 false false false ES X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} Currently, many speaker recognition applications must handle speech corrupted by environmental additive noise without having a priori knowledge about the characteristics of noise. Some previous works in speaker recognition have used Missing Feature (MF approach to compensate for noise. In most of those applications the spectral reliability decision step is done using the Signal to Noise Ratio (SNR criterion. This has the goal of enhancing signal power rather than noise power, which could be dangerous in speaker recognition tasks, because useful speaker information could be removed. This work proposes a new mask estimation method based on Speaker Discriminative Information (SDI for determining spectral reliability in speaker recognition applications based on the MF approach. The proposal was evaluated through speaker verification experiments in speech corrupted by additive noise. Experiments demonstrated that this new criterion has a promising performance in speaker verification tasks.

  6. New features using robust MVDR spectrum of filtered autocorrelation sequence for robust speech recognition.

    Science.gov (United States)

    Seyedin, Sanaz; Ahadi, Seyed Mohammad; Gazor, Saeed

    2013-01-01

    This paper presents a novel noise-robust feature extraction method for speech recognition using the robust perceptual minimum variance distortionless response (MVDR) spectrum of temporally filtered autocorrelation sequence. The perceptual MVDR spectrum of the filtered short-time autocorrelation sequence can reduce the effects of residue of the nonstationary additive noise which remains after filtering the autocorrelation. To achieve a more robust front-end, we also modify the robust distortionless constraint of the MVDR spectral estimation method via revised weighting of the subband power spectrum values based on the sub-band signal to noise ratios (SNRs), which adjusts it to the new proposed approach. This new function allows the components of the input signal at the frequencies least affected by noise to pass with larger weights and attenuates more effectively the noisy and undesired components. This modification results in reduction of the noise residuals of the estimated spectrum from the filtered autocorrelation sequence, thereby leading to a more robust algorithm. Our proposed method, when evaluated on Aurora 2 task for recognition purposes, outperformed all Mel frequency cepstral coefficients (MFCC) as the baseline, relative autocorrelation sequence MFCC (RAS-MFCC), and the MVDR-based features in several different noisy conditions.

  7. Text and Language-Independent Speaker Recognition Using Suprasegmental Features and Support Vector Machines

    Science.gov (United States)

    Bajpai, Anvita; Pathangay, Vinod

    In this paper, presence of the speaker-specific suprasegmental information in the Linear Prediction (LP) residual signal is demonstrated. The LP residual signal is obtained after removing the predictable part of the speech signal. This information, if added to existing speaker recognition systems based on segmental and subsegmental features, can result in better performing combined system. The speaker-specific suprasegmental information can not only be perceived by listening to the residual, but can also be seen in the form of excitation peaks in the residual waveform. However, the challenge lies in capturing this information from the residual signal. Higher order correlations among samples of the residual are not known to be captured using standard signal processing and statistical techniques. The Hilbert envelope of residual is shown to further enhance the excitation peaks present in the residual signal. A speaker-specific pattern is also observed in the autocorrelation sequence of the Hilbert envelope, and further in the statistics of this autocorrelation sequence. This indicates the presence of the speaker-specific suprasegmental information in the residual signal. In this work, no distinction between voiced and unvoiced sounds is done for extracting these features. Support Vector Machine (SVM) is used to classify the patterns in the variance of the autocorrelation sequence for the speaker recognition task.

  8. Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning.

    Science.gov (United States)

    Zhang, Yaoyun; Xu, Jun; Chen, Hui; Wang, Jingqi; Wu, Yonghui; Prakasam, Manu; Xu, Hua

    2016-01-01

    Medicinal chemistry patents contain rich information about chemical compounds. Although much effort has been devoted to extracting chemical entities from scientific literature, limited numbers of patent mining systems are publically available, probably due to the lack of large manually annotated corpora. To accelerate the development of information extraction systems for medicinal chemistry patents, the 2015 BioCreative V challenge organized a track on Chemical and Drug Named Entity Recognition from patent text (CHEMDNER patents). This track included three individual subtasks: (i) Chemical Entity Mention Recognition in Patents (CEMP), (ii) Chemical Passage Detection (CPD) and (iii) Gene and Protein Related Object task (GPRO). We participated in the two subtasks of CEMP and CPD using machine learning-based systems. Our machine learning-based systems employed the algorithms of conditional random fields (CRF) and structured support vector machines (SSVMs), respectively. To improve the performance of the NER systems, two strategies were proposed for feature engineering: (i) domain knowledge features of dictionaries, chemical structural patterns and semantic type information present in the context of the candidate chemical and (ii) unsupervised feature learning algorithms to generate word representation features by Brown clustering and a novel binarized Word embedding to enhance the generalizability of the system. Further, the system output for the CPD task was yielded based on the patent titles and abstracts with chemicals recognized in the CEMP task.The effects of the proposed feature strategies on both the machine learning-based systems were investigated. Our best system achieved the second best performance among 21 participating teams in CEMP with a precision of 87.18%, a recall of 90.78% and aF-measure of 88.94% and was the top performing system among nine participating teams in CPD with a sensitivity of 98.60%, a specificity of 87.21%, an accuracy of 94.75%, a

  9. Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning

    Science.gov (United States)

    Zhang, Yaoyun; Xu, Jun; Chen, Hui; Wang, Jingqi; Wu, Yonghui; Prakasam, Manu; Xu, Hua

    2016-01-01

    Medicinal chemistry patents contain rich information about chemical compounds. Although much effort has been devoted to extracting chemical entities from scientific literature, limited numbers of patent mining systems are publically available, probably due to the lack of large manually annotated corpora. To accelerate the development of information extraction systems for medicinal chemistry patents, the 2015 BioCreative V challenge organized a track on Chemical and Drug Named Entity Recognition from patent text (CHEMDNER patents). This track included three individual subtasks: (i) Chemical Entity Mention Recognition in Patents (CEMP), (ii) Chemical Passage Detection (CPD) and (iii) Gene and Protein Related Object task (GPRO). We participated in the two subtasks of CEMP and CPD using machine learning-based systems. Our machine learning-based systems employed the algorithms of conditional random fields (CRF) and structured support vector machines (SSVMs), respectively. To improve the performance of the NER systems, two strategies were proposed for feature engineering: (i) domain knowledge features of dictionaries, chemical structural patterns and semantic type information present in the context of the candidate chemical and (ii) unsupervised feature learning algorithms to generate word representation features by Brown clustering and a novel binarized Word embedding to enhance the generalizability of the system. Further, the system output for the CPD task was yielded based on the patent titles and abstracts with chemicals recognized in the CEMP task. The effects of the proposed feature strategies on both the machine learning-based systems were investigated. Our best system achieved the second best performance among 21 participating teams in CEMP with a precision of 87.18%, a recall of 90.78% and a F-measure of 88.94% and was the top performing system among nine participating teams in CPD with a sensitivity of 98.60%, a specificity of 87.21%, an accuracy of 94.75%, a

  10. Face Recognition System for Set-Top Box-Based Intelligent TV

    OpenAIRE

    Lee, Won Oh; Kim, Yeong Gon; Hong, Hyung Gil; Park, Kang Ryoung

    2014-01-01

    Despite the prevalence of smart TVs, many consumers continue to use conventional TVs with supplementary set-top boxes (STBs) because of the high cost of smart TVs. However, because the processing power of a STB is quite low, the smart TV functionalities that can be implemented in a STB are very limited. Because of this, negligible research has been conducted regarding face recognition for conventional TVs with supplementary STBs, even though many such studies have been conducted with smart TV...

  11. A Simple Set of Rules for Characters and Place Recognition in French Novels

    OpenAIRE

    Bornet, Cyril; Kaplan, Frédéric

    2017-01-01

    This article describes a simple unsupervised system for automatic extraction and classification of named entities in French novels. The solution presented combines a set of different standalone classifiers within a meta-recognition system. The system is tested on 35 classic French novels, representing 5 million words and 3,700 names of people and places. The results demonstrate that although none of the standalone methods clearly outperforms the others, their combined classification offers a ...

  12. Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Simon Fong

    2017-02-01

    Full Text Available In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called ‘shadow features’ are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

  13. IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK FOR FACE RECOGNITION USING GABOR FEATURE EXTRACTION

    Directory of Open Access Journals (Sweden)

    Muthukannan K

    2013-11-01

    Full Text Available Face detection and recognition is the first step for many applications in various fields such as identification and is used as a key to enter into the various electronic devices, video surveillance, and human computer interface and image database management. This paper focuses on feature extraction in an image using Gabor filter and the extracted image feature vector is then given as an input to the neural network. The neural network is trained with the input data. The Gabor wavelet concentrates on the important components of the face including eye, mouth, nose, cheeks. The main requirement of this technique is the threshold, which gives privileged sensitivity. The threshold values are the feature vectors taken from the faces. These feature vectors are given into the feed forward neural network to train the network. Using the feed forward neural network as a classifier, the recognized and unrecognized faces are classified. This classifier attains a higher face deduction rate. By training more input vectors the system proves to be effective. The effectiveness of the proposed method is demonstrated by the experimental results.

  14. Sequential Filtering Processes Shape Feature Detection in Crickets: A Framework for Song Pattern Recognition.

    Science.gov (United States)

    Hedwig, Berthold G

    2016-01-01

    Intraspecific acoustic communication requires filtering processes and feature detectors in the auditory pathway of the receiver for the recognition of species-specific signals. Insects like acoustically communicating crickets allow describing and analysing the mechanisms underlying auditory processing at the behavioral and neural level. Female crickets approach male calling song, their phonotactic behavior is tuned to the characteristic features of the song, such as the carrier frequency and the temporal pattern of sound pulses. Data from behavioral experiments and from neural recordings at different stages of processing in the auditory pathway lead to a concept of serially arranged filtering mechanisms. These encompass a filter for the carrier frequency at the level of the hearing organ, and the pulse duration through phasic onset responses of afferents and reciprocal inhibition of thoracic interneurons. Further, processing by a delay line and coincidence detector circuit in the brain leads to feature detecting neurons that specifically respond to the species-specific pulse rate, and match the characteristics of the phonotactic response. This same circuit may also control the response to the species-specific chirp pattern. Based on these serial filters and the feature detecting mechanism, female phonotactic behavior is shaped and tuned to the characteristic properties of male calling song.

  15. FEATURE RECOGNITION BERBASIS CORNER DETECTION DENGAN METODE FAST, SURF DAN FLANN TREE UNTUK IDENTIFIKASI LOGO PADA AUGMENTED REALITY MOBILE SYSTEM

    Directory of Open Access Journals (Sweden)

    Rastri Prathivi

    2014-01-01

    Full Text Available Logo is a graphical symbol that is the identity of an organization, institution, or company. Logo is generally used to introduce to the public the existence of an organization, institution, or company. Through the existence of an agency logo can be seen by the public. Feature recognition is one of the processes that exist within an augmented reality system. One of uses augmented reality is able to recognize the identity of the logo through a camera.The first step to make a process of feature recognition is through the corner detection. Incorporation of several method such as FAST, SURF, and FLANN TREE for the feature detection process based corner detection feature matching up process, will have the better ability to detect the presence of a logo. Additionally when running the feature extraction process there are several issues that arise as scale invariant feature and rotation invariant feature. In this study the research object in the form of logo to the priority to make the process of feature recognition. FAST, SURF, and FLANN TREE method will detection logo with scale invariant feature and rotation invariant feature conditions. Obtained from this study will demonstration the accuracy from FAST, SURF, and FLANN TREE methods to solve the scale invariant and rotation invariant feature problems.

  16. Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique

    Directory of Open Access Journals (Sweden)

    Sudha Mohankumar

    2016-06-01

    Full Text Available Precise rainfall forecasting is a common challenge across the globe in meteorological predictions. As rainfall forecasting involves rather complex dynamic parameters, an increasing demand for novel approaches to improve the forecasting accuracy has heightened. Recently, Rough Set Theory (RST has attracted a wide variety of scientific applications and is extensively adopted in decision support systems. Although there are several weather prediction techniques in the existing literature, identifying significant input for modelling effective rainfall prediction is not addressed in the present mechanisms. Therefore, this investigation has examined the feasibility of using rough set based feature selection and data mining methods, namely Naïve Bayes (NB, Bayesian Logistic Regression (BLR, Multi-Layer Perceptron (MLP, J48, Classification and Regression Tree (CART, Random Forest (RF, and Support Vector Machine (SVM, to forecast rainfall. Feature selection or reduction process is a process of identifying a significant feature subset, in which the generated subset must characterize the information system as a complete feature set. This paper introduces a novel rough set based Maximum Frequency Weighted (MFW feature reduction technique for finding an effective feature subset for modelling an efficient rainfall forecast system. The experimental analysis and the results indicate substantial improvements of prediction models when trained using the selected feature subset. CART and J48 classifiers have achieved an improved accuracy of 83.42% and 89.72%, respectively. From the experimental study, relative humidity2 (a4 and solar radiation (a6 have been identified as the effective parameters for modelling rainfall prediction.

  17. Word Recognition and Learning: Effects of Hearing Loss and Amplification Feature

    Science.gov (United States)

    Stewart, Elizabeth C.; Willman, Amanda P.; Odgear, Ian S.

    2017-01-01

    Two amplification features were examined using auditory tasks that varied in stimulus familiarity. It was expected that the benefits of certain amplification features would increase as the familiarity with the stimuli decreased. A total of 20 children and 15 adults with normal hearing as well as 21 children and 17 adults with mild to severe hearing loss participated. Three models of ear-level devices were selected based on the quality of the high-frequency amplification or the digital noise reduction (DNR) they provided. The devices were fitted to each participant and used during testing only. Participants completed three tasks: (a) word recognition, (b) repetition and lexical decision of real and nonsense words, and (c) novel word learning. Performance improved significantly with amplification for both the children and the adults with hearing loss. Performance improved further with wideband amplification for the children more than for the adults. In steady-state noise and multitalker babble, performance decreased for both groups with little to no benefit from amplification or from the use of DNR. When compared with the listeners with normal hearing, significantly poorer performance was observed for both the children and adults with hearing loss on all tasks with few exceptions. Finally, analysis of across-task performance confirmed the hypothesis that benefit increased as the familiarity of the stimuli decreased for wideband amplification but not for DNR. However, users who prefer DNR for listening comfort are not likely to jeopardize their ability to detect and learn new information when using this feature.

  18. Emotion recognition based on EEG features in movie clips with channel selection.

    Science.gov (United States)

    Özerdem, Mehmet Siraç; Polat, Hasan

    2017-07-15

    Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain-computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems. In this study, EEG signals related to positive and negative emotions have been classified with preprocessing of channel selection. Self-Assessment Manikins was used to determine emotional states. We have employed discrete wavelet transform and machine learning techniques such as multilayer perceptron neural network (MLPNN) and k-nearest neighborhood (kNN) algorithm to classify EEG signals. The classifier algorithms were initially used for channel selection. EEG channels for each participant were evaluated separately, and five EEG channels that offered the best classification performance were determined. Thus, final feature vectors were obtained by combining the features of EEG segments belonging to these channels. The final feature vectors with related positive and negative emotions were classified separately using MLPNN and kNN algorithms. The classification performance obtained with both the algorithms are computed and compared. The average overall accuracies were obtained as 77.14 and 72.92% by using MLPNN and kNN, respectively.

  19. A quick scan and lane recognition algorithm based on positional distribution and edge features

    Science.gov (United States)

    Wang, Jian; Zhang, Yuan; Chen, Xiaomin; Shi, Xiaoying

    2010-08-01

    With the growing number of vehicles on the road, the automatic guided vehicles (AGV) vision system for intelligent vehicles has been given more and more attention. Lane recognition is an important component in the automatic guided vehicles (AGV) vision system for intelligent vehicles. To improve the speed and accuracy of lane recognition, this paper proposed an image segmentation algorithm based on the normalized histogram matching and a specific image scan algorithm based on positional distribution of lanes to reduce runtime. The purpose of image segmentation is extracting useful road information and the algorithm is segmenting the image by calculating the similarity of Cumulative Distribution Function (CDF) of normalization histogram. The main idea of image scan algorithm proposed in this paper is regarding the lanes that have been found as starting points and looking for the new lanes. Then we use a novel lane screen algorithm based on the left and right edges of lanes' geometric feature to remove invalid information and improve the accuracy and promote efficiency effectively. At last, a lane prediction algorithm is proposed to predict the farther lanes which may be lost due to treating as noises. After our tests, this algorithm has better robustness and higher efficiency.

  20. Iris image recognition wavelet filter-banks based iris feature extraction schemes

    CERN Document Server

    Rahulkar, Amol D

    2014-01-01

    This book provides the new results in wavelet filter banks based feature extraction, and the classifier in the field of iris image recognition. It provides the broad treatment on the design of separable, non-separable wavelets filter banks, and the classifier. The design techniques presented in the book are applied on iris image analysis for person authentication. This book also brings together the three strands of research (wavelets, iris image analysis, and classifier). It compares the performance of the presented techniques with state-of-the-art available schemes. This book contains the compilation of basic material on the design of wavelets that avoids reading many different books. Therefore, it provide an easier path for the new-comers, researchers to master the contents. In addition, the designed filter banks and classifier can also be effectively used than existing filter-banks in many signal processing applications like pattern classification, data-compression, watermarking, denoising etc.  that will...

  1. Feature extraction and fusion techniques for patch-based face recognition

    OpenAIRE

    Topçu, Berkay; Topcu, Berkay

    2009-01-01

    Face recognition is one of the most addressed pattern recognition problems in recent studies due to its importance in security applications and human computer interfaces. After decades of research in the face recognition problem, feasible technologies are becoming available. However, there is still room for improvement for challenging cases. As such, face recognition problem still attracts researchers from image processing, pattern recognition and computer vision disciplines. Although there e...

  2. Enhanced Local Gradient Order Features and Discriminant Analysis for Face Recognition.

    Science.gov (United States)

    Ren, Chuan-Xian; Lei, Zhen; Dai, Dao-Qing; Li, Stan Z

    2016-11-01

    Robust descriptor-based subspace learning with complex data is an active topic in pattern analysis and machine intelligence. A few researches concentrate the optimal design on feature representation and metric learning. However, traditionally used features of single-type, e.g., image gradient orientations (IGOs), are deficient to characterize the complete variations in robust and discriminant subspace learning. Meanwhile, discontinuity in edge alignment and feature match are not been carefully treated in the literature. In this paper, local order constrained IGOs are exploited to generate robust features. As the difference-based filters explicitly consider the local contrasts within neighboring pixel points, the proposed features enhance the local textures and the order-based coding ability, thus discover intrinsic structure of facial images further. The multimodal features are automatically fused in the most discriminant subspace. The utilization of adaptive interaction function suppresses outliers in each dimension for robust similarity measurement and discriminant analysis. The sparsity-driven regression model is modified to adapt the classification issue of the compact feature representation. Extensive experiments are conducted by using some benchmark face data sets, e.g., of controlled and uncontrolled environments, to evaluate our new algorithm.

  3. Salient Feature Identification and Analysis using Kernel-Based Classification Techniques for Synthetic Aperture Radar Automatic Target Recognition

    Science.gov (United States)

    2014-03-27

    that has made me the person I am today. v Acknowledgments I would first like to thank my advisor, Dr. Julie Jackson, without whom this thesis would not...and machine learning for a range of research including such topics as medical imaging [10] and handwriting recognition [11]. The type of feature...1989. [11] C. Bahlmann, B. Haasdonk, and H. Burkhardt, “Online handwriting recognition with support vector machines-a kernel approach,” in Eighth

  4. The research of edge extraction and target recognition based on inherent feature of objects

    Science.gov (United States)

    Xie, Yu-chan; Lin, Yu-chi; Huang, Yin-guo

    2008-03-01

    Current research on computer vision often needs specific techniques for particular problems. Little use has been made of high-level aspects of computer vision, such as three-dimensional (3D) object recognition, that are appropriate for large classes of problems and situations. In particular, high-level vision often focuses mainly on the extraction of symbolic descriptions, and pays little attention to the speed of processing. In order to extract and recognize target intelligently and rapidly, in this paper we developed a new 3D target recognition method based on inherent feature of objects in which cuboid was taken as model. On the basis of analysis cuboid nature contour and greyhound distributing characteristics, overall fuzzy evaluating technique was utilized to recognize and segment the target. Then Hough transform was used to extract and match model's main edges, we reconstruct aim edges by stereo technology in the end. There are three major contributions in this paper. Firstly, the corresponding relations between the parameters of cuboid model's straight edges lines in an image field and in the transform field were summed up. By those, the aimless computations and searches in Hough transform processing can be reduced greatly and the efficiency is improved. Secondly, as the priori knowledge about cuboids contour's geometry character known already, the intersections of the component extracted edges are taken, and assess the geometry of candidate edges matches based on the intersections, rather than the extracted edges. Therefore the outlines are enhanced and the noise is depressed. Finally, a 3-D target recognition method is proposed. Compared with other recognition methods, this new method has a quick response time and can be achieved with high-level computer vision. The method present here can be used widely in vision-guide techniques to strengthen its intelligence and generalization, which can also play an important role in object tracking, port AGV, robots

  5. A Set of Handwriting Features for Use in Automated Writer Identification().

    Science.gov (United States)

    Miller, John J; Patterson, Robert Bradley; Gantz, Donald T; Saunders, Christopher P; Walch, Mark A; Buscaglia, JoAnn

    2017-05-01

    A writer's biometric identity can be characterized through the distribution of physical feature measurements ("writer's profile"); a graph-based system that facilitates the quantification of these features is described. To accomplish this quantification, handwriting is segmented into basic graphical forms ("graphemes"), which are "skeletonized" to yield the graphical topology of the handwritten segment. The graph-based matching algorithm compares the graphemes first by their graphical topology and then by their geometric features. Graphs derived from known writers can be compared against graphs extracted from unknown writings. The process is computationally intensive and relies heavily upon statistical pattern recognition algorithms. This article focuses on the quantification of these physical features and the construction of the associated pattern recognition methods for using the features to discriminate among writers. The graph-based system described in this article has been implemented in a highly accurate and approximately language-independent biometric recognition system of writers of cursive documents. © 2017 American Academy of Forensic Sciences.

  6. EMG Feature Assessment for Myoelectric Pattern Recognition and Channel Selection: A Study with Incomplete Spinal Cord Injury

    Science.gov (United States)

    Liu, Jie; Li, Xiaoyan; Li, Guanglin; Zhou, Ping

    2014-01-01

    Myoelectric pattern recognition with a large number of electromyogram (EMG) channels provides an approach to assessing motor control information available from the recorded muscles. In order to develop a practical myoelectric control system, a feature dependent channel reduction method was developed in this study to determine a small number of EMG channels for myoelectric pattern recognition analysis. The method selects appropriate raw EMG features for classification of different movements, using the minimum Redundancy Maximum Relevance (mRMR) and the Markov random field (MRF) methods to rank a large number of EMG features, respectively. A k-nearest neighbor (KNN) classifier was used to evaluate the performance of the selected features in terms of classification accuracy. The method was tested using 57 channels’ surface EMG signals recorded from forearm and hand muscles of individuals with incomplete spinal cord injury (SCI). Our results demonstrate that appropriate selection of a small number of raw EMG features from different recording channels resulted in similar high classification accuracies as achieved by using all the EMG channels or features. Compared with the conventional sequential forward selection (SFS) method, the feature dependent method does not require repeated classifier implementation. It can effectively reduce redundant information not only cross different channels, but also cross different features in the same channel. Such hybrid feature-channel selection from a large number of EMG recording channels can reduce computational cost for implementation of a myoelectric pattern recognition based control system. PMID:24844608

  7. EMG feature assessment for myoelectric pattern recognition and channel selection: a study with incomplete spinal cord injury.

    Science.gov (United States)

    Liu, Jie; Li, Xiaoyan; Li, Guanglin; Zhou, Ping

    2014-07-01

    Myoelectric pattern recognition with a large number of electromyogram (EMG) channels provides an approach to assessing motor control information available from the recorded muscles. In order to develop a practical myoelectric control system, a feature dependent channel reduction method was developed in this study to determine a small number of EMG channels for myoelectric pattern recognition analysis. The method selects appropriate raw EMG features for classification of different movements, using the minimum Redundancy Maximum Relevance (mRMR) and the Markov random field (MRF) methods to rank a large number of EMG features, respectively. A k-nearest neighbor (KNN) classifier was used to evaluate the performance of the selected features in terms of classification accuracy. The method was tested using 57 channels' surface EMG signals recorded from forearm and hand muscles of individuals with incomplete spinal cord injury (SCI). Our results demonstrate that appropriate selection of a small number of raw EMG features from different recording channels resulted in similar high classification accuracies as achieved by using all the EMG channels or features. Compared with the conventional sequential forward selection (SFS) method, the feature dependent method does not require repeated classifier implementation. It can effectively reduce redundant information not only cross different channels, but also cross different features in the same channel. Such hybrid feature-channel selection from a large number of EMG recording channels can reduce computational cost for implementation of a myoelectric pattern recognition based control system. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

  8. Object class recognition based on compressive sensing with sparse features inspired by hierarchical model in visual cortex

    Science.gov (United States)

    Lu, Pei; Xu, Zhiyong; Yu, Huapeng; Chang, Yongxin; Fu, Chengyu; Shao, Jianxin

    2012-11-01

    According to models of object recognition in cortex, the brain uses a hierarchical approach in which simple, low-level features having high position and scale specificity are pooled and combined into more complex, higher-level features having greater location invariance. At higher levels, spatial structure becomes implicitly encoded into the features themselves, which may overlap, while explicit spatial information is coded more coarsely. In this paper, the importance of sparsity and localized patch features in a hierarchical model inspired by visual cortex is investigated. As in the model of Serre, Wolf, and Poggio, we first apply Gabor filters at all positions and scales; feature complexity and position/scale invariance are then built up by alternating template matching and max pooling operations. In order to improve generalization performance, the sparsity is proposed and data dimension is reduced by means of compressive sensing theory and sparse representation algorithm. Similarly, within computational neuroscience, adding the sparsity on the number of feature inputs and feature selection is critical for learning biologically model from the statistics of natural images. Then, a redundancy dictionary of patch-based features that could distinguish object class from other categories is designed and then object recognition is implemented by the process of iterative optimization. The method is test on the UIUC car database. The success of this approach suggests a proof for the object class recognition in visual cortex.

  9. Vehicle Color Recognition with Vehicle-Color Saliency Detection and Dual-Orientational Dimensionality Reduction of CNN Deep Features

    Science.gov (United States)

    Zhang, Qiang; Li, Jiafeng; Zhuo, Li; Zhang, Hui; Li, Xiaoguang

    2017-12-01

    Color is one of the most stable attributes of vehicles and often used as a valuable cue in some important applications. Various complex environmental factors, such as illumination, weather, noise and etc., result in the visual characteristics of the vehicle color being obvious diversity. Vehicle color recognition in complex environments has been a challenging task. The state-of-the-arts methods roughly take the whole image for color recognition, but many parts of the images such as car windows; wheels and background contain no color information, which will have negative impact on the recognition accuracy. In this paper, a novel vehicle color recognition method using local vehicle-color saliency detection and dual-orientational dimensionality reduction of convolutional neural network (CNN) deep features has been proposed. The novelty of the proposed method includes two parts: (1) a local vehicle-color saliency detection method has been proposed to determine the vehicle color region of the vehicle image and exclude the influence of non-color regions on the recognition accuracy; (2) dual-orientational dimensionality reduction strategy has been designed to greatly reduce the dimensionality of deep features that are learnt from CNN, which will greatly mitigate the storage and computational burden of the subsequent processing, while improving the recognition accuracy. Furthermore, linear support vector machine is adopted as the classifier to train the dimensionality reduced features to obtain the recognition model. The experimental results on public dataset demonstrate that the proposed method can achieve superior recognition performance over the state-of-the-arts methods.

  10. Feature-specific imaging: Extensions to adaptive object recognition and active illumination based scene reconstruction

    Science.gov (United States)

    Baheti, Pawan K.

    Computational imaging (CI) systems are hybrid imagers in which the optical and post-processing sub-systems are jointly optimized to maximize the task-specific performance. In this dissertation we consider a form of CI system that measures the linear projections (i.e., features) of the scene optically, and it is commonly referred to as feature-specific imaging (FSI). Most of the previous work on FSI has been concerned with image reconstruction. Previous FSI techniques have also been non-adaptive and restricted to the use of ambient illumination. We consider two novel extensions of the FSI system in this work. We first present an adaptive feature-specific imaging (AFSI) system and consider its application to a face-recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. We present both statistical and information-theoretic adaptation mechanisms for the AFSI system. The sequential hypothesis testing framework is used to determine the number of measurements required for achieving a specified misclassification probability. We demonstrate that AFSI system requires significantly fewer measurements than static-FSI (SFSI) and conventional imaging at low signal-to-noise ratio (SNR). We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage. Experimental results validating the AFSI system are presented. Next we present a FSI system based on the use of structured light. Feature measurements are obtained by projecting spatially structured illumination onto an object and collecting all of the reflected light onto a single photodetector. We refer to this system as feature-specific structured imaging (FSSI). Principal component features are used to define the illumination patterns. The optimal LMMSE operator is used to generate object estimates from the measurements. We demonstrate that this new imaging approach reduces imager complexity and provides improved image

  11. Multiple levels of linguistic and paralinguistic features contribute to voice recognition

    National Research Council Canada - National Science Library

    Zarate, Jean Mary; Tian, Xing; Woods, Kevin J P; Poeppel, David

    2015-01-01

    Voice or speaker recognition is critical in a wide variety of social contexts. In this study, we investigated the contributions of acoustic, phonological, lexical, and semantic information toward voice recognition...

  12. Emotion Recognition from Speech with Acoustic, Non-Linear and Wavelet-based Features Extracted in Different Acoustic Conditions

    OpenAIRE

    Vásquez Correa, Juan Camilo

    2016-01-01

    In the last years, there has a great progress in automatic speech recognition. The challenge now it is not only recognize the semantic content in the speech but also the called "paralinguistic" aspects of the speech, including the emotions, and the personality of the speaker. This research work aims in the development of a methodology for the automatic emotion recognition from speech signals in non-controlled noise conditions. For that purpose, different sets of acoustic, non-linear, and wave...

  13. Selection of clinical features for pattern recognition applied to gait analysis.

    Science.gov (United States)

    Altilio, Rosa; Paoloni, Marco; Panella, Massimo

    2017-04-01

    This paper deals with the opportunity of extracting useful information from medical data retrieved directly from a stereophotogrammetric system applied to gait analysis. A feature selection method to exhaustively evaluate all the possible combinations of the gait parameters is presented, in order to find the best subset able to classify among diseased and healthy subjects. This procedure will be used for estimating the performance of widely used classification algorithms, whose performance has been ascertained in many real-world problems with respect to well-known classification benchmarks, both in terms of number of selected features and classification accuracy. Precisely, support vector machine, Naive Bayes and K nearest neighbor classifiers can obtain the lowest classification error, with an accuracy greater than 97 %. For the considered classification problem, the whole set of features will be proved to be redundant and it can be significantly pruned. Namely, groups of 3 or 5 features only are able to preserve high accuracy when the aim is to check the anomaly of a gait. The step length and the swing speed are the most informative features for the gait analysis, but also cadence and stride may add useful information for the movement evaluation.

  14. Joint infrared target recognition and segmentation using a shape manifold-aware level set.

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    Yu, Liangjiang; Fan, Guoliang; Gong, Jiulu; Havlicek, Joseph P

    2015-04-29

    We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifolds (CVIM). A level set energy function is then iteratively optimized under the shape constraints provided by the CVIM. Since both the view and identity variables are expressed explicitly in the objective function, this approach naturally accomplishes recognition, segmentation and pose estimation as joint products of the optimization process. For realistic target chips, we solve the resulting multi-modal optimization problem by adopting a particle swarm optimization (PSO) algorithm and then improve the computational efficiency by implementing a gradient-boosted PSO (GB-PSO). Evaluation was performed using the Military Sensing Information Analysis Center (SENSIAC) ATR database, and experimental results show that both of the PSO algorithms reduce the cost of shape matching during CVIM-based shape inference. Particularly, GB-PSO outperforms other recent ATR algorithms, which require intensive shape matching, either explicitly (with pre-segmentation) or implicitly (without pre-segmentation).

  15. A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer.

    Science.gov (United States)

    Khan, Adil Mehmood; Lee, Young-Koo; Lee, Sungyoung Y; Kim, Tae-Seong

    2010-09-01

    Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.

  16. Designing of Medium-Size Humanoid Robot with Face Recognition Features

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    Christian Tarunajaya

    2016-02-01

    Full Text Available owadays, there have been so many development of robot that can receive command and do speech recognition and face recognition. In this research, we develop a humanoid robot system with a controller that based on Raspberry Pi 2. The methods we used are based on Audio recognition and detection, and also face recognition using PCA (Principal Component Analysis with OpenCV and Python. PCA is one of the algorithms to do face detection by doing reduction to the number of dimension of the image possessed. The result of this reduction process is then known as eigenface to do face recognition process. In this research, we still find a false recognition. It can be caused by many things, like database condition, maybe the images are too dark or less varied, blur test image, etc. The accuracy from 3 tests on different people is about 93% (28 correct recognitions out of 30.

  17. Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

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    Prima Dewi Purnamasari

    2017-05-01

    Full Text Available The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN, and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction

  18. A new standardized stimulus set for studying need-of-help recognition (NeoHelp.

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    Aenne A Brielmann

    Full Text Available This article presents the NeoHelp visual stimulus set created to facilitate investigation of need-of-help recognition with clinical and normative populations of different ages, including children. Need-of-help recognition is one aspect of socioemotional development and a necessary precondition for active helping. The NeoHelp consists of picture pairs showing everyday situations: The first item in a pair depicts a child needing help to achieve a goal; the second one shows the child achieving the goal. Pictures of birds in analogue situations are also included. These control stimuli enable implementation of a human-animal categorization task which serves to separate behavioral correlates specific to need-of-help recognition from general differentiation processes. It is a concern in experimental research to ensure that results do not relate to systematic perceptual differences when comparing responses to categories of different content. Therefore, we not only derived the NeoHelp-pictures within a pair from one another by altering as little as possible, but also assessed their perceptual similarity empirically. We show that NeoHelp-picture pairs are very similar regarding low-level perceptual properties across content categories. We obtained data from 60 children in a broad age range (4 to 13 years for three different paradigms, in order to assess whether the intended categorization and differentiation could be observed reliably in a normative population. Our results demonstrate that children can differentiate the pictures' content regarding both need-of-help category as well as species as intended in spite of the high perceptual similarities. We provide standard response characteristics (hit rates and response times that are useful for future selection of stimuli and comparison of results across studies. We show that task requirements coherently determine which aspects of the pictures influence response characteristics. Thus, we present Neo

  19. Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

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    Sadeghi, Zahra; Testolin, Alberto

    2017-08-01

    In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

  20. A new method of measuring similarity between two neutrosophic soft sets and its application in pattern recognition problems

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    Anjan Mukherjee

    2015-03-01

    Full Text Available Smarandache in 1995 introduced the concept of neutrosophic set and in 2013 Maji introduced the notion of neutrosophic soft set, which is a hybridization of neutrosophic set and soft set. After its introduction neutrosophic soft sets become most efficient tools to deals with problems that contain uncertainty such as problem in social, economic system, medical diagnosis, pattern recognition, game theory, coding theory and so on. In this work a new method of measuring similarity measure and weighted similarity measure between two neutrosophic soft sets (NSSs are proposed. A comparative study with existing similarity measures for neutrosophic soft sets also studied. A decision making method is established for neutrosophic soft set setting using similarity measures. Lastly a numerical example is given to demonstrate the possible application of similarity measures in pattern recognition problems.

  1. Adaptive Local Spatiotemporal Features from RGB-D Data for One-Shot Learning Gesture Recognition.

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    Lin, Jia; Ruan, Xiaogang; Yu, Naigong; Yang, Yee-Hong

    2016-12-17

    Noise and constant empirical motion constraints affect the extraction of distinctive spatiotemporal features from one or a few samples per gesture class. To tackle these problems, an adaptive local spatiotemporal feature (ALSTF) using fused RGB-D data is proposed. First, motion regions of interest (MRoIs) are adaptively extracted using grayscale and depth velocity variance information to greatly reduce the impact of noise. Then, corners are used as keypoints if their depth, and velocities of grayscale and of depth meet several adaptive local constraints in each MRoI. With further filtering of noise, an accurate and sufficient number of keypoints is obtained within the desired moving body parts (MBPs). Finally, four kinds of multiple descriptors are calculated and combined in extended gradient and motion spaces to represent the appearance and motion features of gestures. The experimental results on the ChaLearn gesture, CAD-60 and MSRDailyActivity3D datasets demonstrate that the proposed feature achieves higher performance compared with published state-of-the-art approaches under the one-shot learning setting and comparable accuracy under the leave-one-out cross validation.

  2. Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines.

    Science.gov (United States)

    Gruss, Sascha; Treister, Roi; Werner, Philipp; Traue, Harald C; Crawcour, Stephen; Andrade, Adriano; Walter, Steffen

    2015-01-01

    The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient's report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity. In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold) under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity. We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography. The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.

  3. Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines.

    Directory of Open Access Journals (Sweden)

    Sascha Gruss

    Full Text Available The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient's report on the pain sensation. Verbal scales, visual analog scales (VAS or numeric rating scales (NRS count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity.In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity.We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography.The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.

  4. Thai Finger-Spelling Recognition Using a Cascaded Classifier Based on Histogram of Orientation Gradient Features

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    Kittasil Silanon

    2017-01-01

    Full Text Available Hand posture recognition is an essential module in applications such as human-computer interaction (HCI, games, and sign language systems, in which performance and robustness are the primary requirements. In this paper, we proposed automatic classification to recognize 21 hand postures that represent letters in Thai finger-spelling based on Histogram of Orientation Gradient (HOG feature (which is applied with more focus on the information within certain region of the image rather than each single pixel and Adaptive Boost (i.e., AdaBoost learning technique to select the best weak classifier and to construct a strong classifier that consists of several weak classifiers to be cascaded in detection architecture. We collected 21 static hand posture images from 10 subjects for testing and training in Thai letters finger-spelling. The parameters for the training process have been adjusted in three experiments, false positive rates (FPR, true positive rates (TPR, and number of training stages (N, to achieve the most suitable training model for each hand posture. All cascaded classifiers are loaded into the system simultaneously to classify different hand postures. A correlation coefficient is computed to distinguish the hand postures that are similar. The system achieves approximately 78% accuracy on average on all classifier experiments.

  5. Face recognition based on matching of local features on 3D dynamic range sequences

    Science.gov (United States)

    Echeagaray-Patrón, B. A.; Kober, Vitaly

    2016-09-01

    3D face recognition has attracted attention in the last decade due to improvement of technology of 3D image acquisition and its wide range of applications such as access control, surveillance, human-computer interaction and biometric identification systems. Most research on 3D face recognition has focused on analysis of 3D still data. In this work, a new method for face recognition using dynamic 3D range sequences is proposed. Experimental results are presented and discussed using 3D sequences in the presence of pose variation. The performance of the proposed method is compared with that of conventional face recognition algorithms based on descriptors.

  6. An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System

    Directory of Open Access Journals (Sweden)

    Ahmadi Majid

    2003-01-01

    Full Text Available This paper introduces a novel method for the recognition of human faces in digital images using a new feature extraction method that combines the global and local information in frontal view of facial images. Radial basis function (RBF neural network with a hybrid learning algorithm (HLA has been used as a classifier. The proposed feature extraction method includes human face localization derived from the shape information. An efficient distance measure as facial candidate threshold (FCT is defined to distinguish between face and nonface images. Pseudo-Zernike moment invariant (PZMI with an efficient method for selecting moment order has been used. A newly defined parameter named axis correction ratio (ACR of images for disregarding irrelevant information of face images is introduced. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as RBF neural network learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL indicate that the proposed method for human face recognition yielded a recognition rate of 99.3%.

  7. Multi-script handwritten character recognition : Using feature descriptors and machine learning

    NARCIS (Netherlands)

    Surinta, Olarik

    2016-01-01

    Handwritten character recognition plays an important role in transforming raw visual image data obtained from handwritten documents using for example scanners to a format which is understandable by a computer. It is an important application in the field of pattern recognition, machine learning and

  8. Distinct anatomical correlates of discriminability and criterion setting in verbal recognition memory revealed by lesion-symptom mapping

    NARCIS (Netherlands)

    Biesbroek, J. Matthijs; van Zandvoort, Martine J E; Kappelle, L. Jaap; Schoo, Linda; Kuijf, Hugo J.; Velthuis, BK; Biessels, Geert Jan; Postma, Albert

    2015-01-01

    Recognition memory, that is, the ability to judge whether an item has been previously encountered in a particular context, depends on two factors: discriminability and criterion setting. Discriminability draws on memory processes while criterion setting (i.e., the application of a threshold

  9. Distinct anatomical correlates of discriminability and criterion setting in verbal recognition memory revealed by lesion-symptom mapping.

    Science.gov (United States)

    Biesbroek, J Matthijs; van Zandvoort, Martine J E; Kappelle, L Jaap; Schoo, Linda; Kuijf, Hugo J; Velthuis, Birgitta K; Biessels, Geert Jan; Postma, Albert

    2015-04-01

    Recognition memory, that is, the ability to judge whether an item has been previously encountered in a particular context, depends on two factors: discriminability and criterion setting. Discriminability draws on memory processes while criterion setting (i.e., the application of a threshold resulting in a yes/no response) is regarded as a process of cognitive control. Discriminability and criterion setting are assumed to draw on distinct anatomical structures, but definite evidence for this assumption is lacking. We applied voxel-based and region of interest-based lesion-symptom mapping to 83 patients in the acute phase of ischemic stroke to determine the anatomical correlates of discriminability and criterion setting in verbal recognition memory. Recognition memory was measured with the Rey Auditory Verbal Learning Test. Signal-detection theory was used to calculate measures for discriminability and criterion setting. Lesion-symptom mapping revealed that discriminability draws on left medial temporal and temporo-occipital structures, both thalami and the right hippocampus, while criterion setting draws on the right inferior frontal gyrus. Lesions in the right inferior frontal gyrus were associated with liberal response bias. These findings indicate that discriminability and criterion setting indeed depend on distinct anatomical structures and provide new insights in the anatomical correlates of these cognitive processes that underlie verbal recognition memory. © 2014 Wiley Periodicals, Inc.

  10. Decision forests for machine learning classification of large, noisy seafloor feature sets

    Science.gov (United States)

    Lawson, Ed; Smith, Denson; Sofge, Donald; Elmore, Paul; Petry, Frederick

    2017-02-01

    Extremely randomized trees (ET) classifiers, an extension of random forests (RF) are applied to classification of features such as seamounts derived from bathymetry data. This data is characterized by sparse training data from by large noisy features sets such as often found in other geospatial data. A variety of feature metrics may be useful for this task and we use a large number of metrics relevant to the task of finding seamounts. The major significant results to be described include: an outstanding seamount classification accuracy of 97%; an automated process to produce the most useful classification features that are relevant to geophysical scientists (as represented by the feature metrics); demonstration that topography provides the most important data representation for classification. As well as achieving good accuracy in classification, the human-understandable set of metrics generated by the classifier that are most relevant for the results are discussed.

  11. Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

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    Huile Xu

    2016-12-01

    Full Text Available Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT or wavelet transform (WT. However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA and instantaneous frequency (IF by means of empirical mode decomposition (EMD, as well as instantaneous energy density (IE and marginal spectrum (MS derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

  12. Recognition and assessment of resident' deterioration in the nursing home setting: a critical ethnography.

    Science.gov (United States)

    Laging, Bridget; Kenny, Amanda; Bauer, Michael; Nay, Rhonda

    2018-02-03

    To explore the recognition and assessment of resident deterioration in the nursing home setting. There is a dearth of research exploring how nurses and personal-care-assistants manage a deteriorating nursing home resident. Critical ethnography. Observation and semi-structured interviews with 66 participants (residents, family, nurses, personal-care-assistants and general practitioners) in two Australian nursing homes. The study has been reported in accordance with the Consolidated Criteria for Reporting Qualitative Research guidelines. The value of nursing assessment is poorly recognized in the nursing home setting. A lack of clarity regarding the importance of nursing assessments associated with resident care has contributed to a decreasing presence of registered nurses and an increasing reliance on personal-care-assistants who had inadequate skills and knowledge to recognize signs of deterioration. Registered nurses experienced limited organizational support for autonomous decision-making and were often expected to undertake protocol-driven decisions that contributed to potentially avoidable hospital transfers. Nurses need to demonstrate the importance of assessment, in association with day-to-day resident care, and demand standardized, regulated, educational preparation of an appropriate workforce who are competent in undertaking this role. Workforce structures that enhance familiarity between nursing home staff and residents could result in improved resident outcomes. The value of nursing assessment, in guiding decisions at the point of resident deterioration, warrants further consideration. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  13. Extricating Manual and Non-Manual Features for Subunit Level Medical Sign Modelling in Automatic Sign Language Classification and Recognition.

    Science.gov (United States)

    R, Elakkiya; K, Selvamani

    2017-09-22

    Subunit segmenting and modelling in medical sign language is one of the important studies in linguistic-oriented and vision-based Sign Language Recognition (SLR). Many efforts were made in the precedent to focus the functional subunits from the view of linguistic syllables but the problem is implementing such subunit extraction using syllables is not feasible in real-world computer vision techniques. And also, the present recognition systems are designed in such a way that it can detect the signer dependent actions under restricted and laboratory conditions. This research paper aims at solving these two important issues (1) Subunit extraction and (2) Signer independent action on visual sign language recognition. Subunit extraction involved in the sequential and parallel breakdown of sign gestures without any prior knowledge on syllables and number of subunits. A novel Bayesian Parallel Hidden Markov Model (BPaHMM) is introduced for subunit extraction to combine the features of manual and non-manual parameters to yield better results in classification and recognition of signs. Signer independent action aims in using a single web camera for different signer behaviour patterns and for cross-signer validation. Experimental results have proved that the proposed signer independent subunit level modelling for sign language classification and recognition has shown improvement and variations when compared with other existing works.

  14. Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search

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    Simon Fong

    2013-01-01

    Full Text Available Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.

  15. Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets.

    Science.gov (United States)

    Wang, Wen; Wang, Ruiping; Huang, Zhiwu; Shan, Shiguang; Chen, Xilin

    To address the problem of face recognition with image sets, we aim to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent image set as the Gaussian mixture model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. Since in the light of information geometry, the Gaussians lie on a specific Riemannian manifold, this paper presents a method named discriminant analysis on Riemannian manifold of Gaussian distributions (DARG). We investigate several distance metrics between Gaussians and accordingly two discriminative learning frameworks are presented to meet the geometric and statistical characteristics of the specific manifold. The first framework derives a series of provably positive definite probabilistic kernels to embed the manifold to a high-dimensional Hilbert space, where conventional discriminant analysis methods developed in Euclidean space can be applied, and a weighted Kernel discriminant analysis is devised which learns discriminative representation of the Gaussian components in GMMs with their prior probabilities as sample weights. Alternatively, the other framework extends the classical graph embedding method to the manifold by utilizing the distance metrics between Gaussians to construct the adjacency graph, and hence the original manifold is embedded to a lower-dimensional and discriminative target manifold with the geometric structure preserved and the interclass separability maximized. The proposed method is evaluated by face identification and verification tasks on four most challenging and largest databases, YouTube Celebrities, COX, YouTube Face DB, and Point-and-Shoot Challenge, to demonstrate its superiority over the state-of-the-art.To address the problem of face recognition with image sets, we aim to capture the underlying data distribution in each set and thus facilitate more

  16. On the use of wavelet for extracting feature patterns from Multitemporal google earth satellite data sets

    Science.gov (United States)

    Lasaponara, R.

    2012-04-01

    The great amount of multispectral VHR satellite images, even available free of charge in Google earth has opened new strategic challenges in the field of remote sensing for archaeological studies. These challenges substantially deal with: (i) the strategic exploitation of satellite data as much as possible, (ii) the setting up of effective and reliable automatic and/or semiautomatic data processing strategies and (iii) the integration with other data sources from documentary resources to the traditional ground survey, historical documentation, geophysical prospection, etc. VHR satellites provide high resolution data which can improve knowledge on past human activities providing precious qualitative and quantitative information developed to such an extent that currently they share many of the physical characteristics of aerial imagery. This makes them ideal for investigations ranging from a local to a regional scale (see. for example, Lasaponara and Masini 2006a,b, 2007a, 2011; Masini and Lasaponara 2006, 2007, Sparavigna, 2010). Moreover, satellite data are still the only data source for research performed in areas where aerial photography is restricted because of military or political reasons. Among the main advantages of using satellite remote sensing compared to traditional field archaeology herein we briefly focalize on the use of wavelet data processing for enhancing google earth satellite data with particular reference to multitemporal datasets. Study areas selected from Southern Italy, Middle East and South America are presented and discussed. Results obtained point out the use of automatic image enhancement can successfully applied as first step of supervised classification and intelligent data analysis for semiautomatic identification of features of archaeological interest. Reference Lasaponara R, Masini N (2006a) On the potential of panchromatic and multispectral Quickbird data for archaeological prospection. Int J Remote Sens 27: 3607-3614. Lasaponara R

  17. Spatial correlation of high density EMG signals provides features robust to electrode number and shift in pattern recognition for myocontrol.

    Science.gov (United States)

    Stango, Antonietta; Negro, Francesco; Farina, Dario

    2015-03-01

    Research on pattern recognition for myoelectric control has usually focused on a small number of electromyography (EMG) channels because of better clinical acceptability and low computational load with respect to multi-channel EMG. However, recently, high density (HD) EMG technology has substantially improved, also in practical usability, and can thus be applied in myocontrol. HD EMG provides several closely spaced recordings in multiple locations over the skin surface. This study considered the use of HD EMG for controlling upper limb prostheses, based on pattern recognition. In general, robustness and reliability of classical pattern recognition systems are influenced by electrode shift in dons and doff, and by the presence of malfunctioning channels. The aim of this study is to propose a new approach to attenuate these issues. The HD EMG grid of electrodes is an ensemble of sensors that records data spatially correlated. The experimental variogram, which is a measure of the degree of spatial correlation, was used as feature for classification, contrary to previous approaches that are based on temporal or frequency features. The classification based on the variogram was tested on seven able-bodied subjects and one subject with amputation, for the classification of nine and seven classes, respectively. The performance of the proposed approach was comparable with the classic methods based on time-domain and autoregressive features (average classification accuracy over all methods ∼ 95% for nine classes). However, the new spatial features demonstrated lower sensitivity to electrode shift ( ± 1 cm) with respect to the classic features . When even just one channel was noisy, the classification accuracy dropped by ∼ 10% for all methods. However, the new method could be applied without any retraining to a subset of high-quality channels whereas the classic methods require retraining when some channels are omitted. In conclusion, the new spatial feature space

  18. De novo prediction of PTBP1 binding and splicing targets reveals unexpected features of its RNA recognition and function.

    Directory of Open Access Journals (Sweden)

    Areum Han

    2014-01-01

    Full Text Available The splicing regulator Polypyrimidine Tract Binding Protein (PTBP1 has four RNA binding domains that each binds a short pyrimidine element, allowing recognition of diverse pyrimidine-rich sequences. This variation makes it difficult to evaluate PTBP1 binding to particular sites based on sequence alone and thus to identify target RNAs. Conversely, transcriptome-wide binding assays such as CLIP identify many in vivo targets, but do not provide a quantitative assessment of binding and are informative only for the cells where the analysis is performed. A general method of predicting PTBP1 binding and possible targets in any cell type is needed. We developed computational models that predict the binding and splicing targets of PTBP1. A Hidden Markov Model (HMM, trained on CLIP-seq data, was used to score probable PTBP1 binding sites. Scores from this model are highly correlated (ρ = -0.9 with experimentally determined dissociation constants. Notably, we find that the protein is not strictly pyrimidine specific, as interspersed Guanosine residues are well tolerated within PTBP1 binding sites. This model identifies many previously unrecognized PTBP1 binding sites, and can score PTBP1 binding across the transcriptome in the absence of CLIP data. Using this model to examine the placement of PTBP1 binding sites in controlling splicing, we trained a multinomial logistic model on sets of PTBP1 regulated and unregulated exons. Applying this model to rank exons across the mouse transcriptome identifies known PTBP1 targets and many new exons that were confirmed as PTBP1-repressed by RT-PCR and RNA-seq after PTBP1 depletion. We find that PTBP1 dependent exons are diverse in structure and do not all fit previous descriptions of the placement of PTBP1 binding sites. Our study uncovers new features of RNA recognition and splicing regulation by PTBP1. This approach can be applied to other multi-RRM domain proteins to assess binding site degeneracy and

  19. Digital field mapping for stimulating Secondary School students in the recognition of geological features and landforms

    Science.gov (United States)

    Giardino, Marco; Magagna, Alessandra; Ferrero, Elena; Perrone, Gianluigi

    2015-04-01

    Digital field mapping has certainly provided geoscientists with the opportunity to map and gather data in the field directly using digital tools and software rather than using paper maps, notebooks and analogue devices and then subsequently transferring the data to a digital format for subsequent analysis. But, the same opportunity has to be recognized for Geoscience education, as well as for stimulating and helping students in the recognition of landforms and interpretation of the geological and geomorphological components of a landscape. More, an early exposure to mapping during school and prior to university can optimise the ability to "read" and identify uncertainty in 3d models. During 2014, about 200 Secondary School students (aged 12-15) of the Piedmont region (NW Italy) participated in a research program involving the use of mobile devices (smartphone and tablet) in the field. Students, divided in groups, used the application Trimble Outdoors Navigators for tracking a geological trail in the Sangone Valley and for taking georeferenced pictures and notes. Back to school, students downloaded the digital data in a .kml file for the visualization on Google Earth. This allowed them: to compare the hand tracked trail on a paper map with the digital trail, and to discuss about the functioning and the precision of the tools; to overlap a digital/semitransparent version of the 2D paper map (a Regional Technical Map) used during the field trip on the 2.5D landscape of Google Earth, as to help them in the interpretation of conventional symbols such as contour lines; to perceive the landforms seen during the field trip as a part of a more complex Pleistocene glacial landscape; to understand the classical and innovative contributions from different geoscientific disciplines to the generation of a 3D structural geological model of the Rivoli-Avigliana Morainic Amphitheatre. In 2013 and 2014, some other pilot projects have been carried out in different areas of the

  20. FEATURE RECOGNITION BERBASIS CORNER DETECTION DENGAN METODE FAST, SURF DAN FLANN TREE UNTUK IDENTIFIKASI LOGO PADA AUGMENTED REALITY MOBILE SYSTEM

    OpenAIRE

    Rastri Prathivi

    2014-01-01

    Logo is a graphical symbol that is the identity of an organization, institution, or company. Logo is generally used to introduce to the public the existence of an organization, institution, or company. Through the existence of an agency logo can be seen by the public. Feature recognition is one of the processes that exist within an augmented reality system. One of uses augmented reality is able to recognize the identity of the logo through a camera.The first step to make a process of feature ...

  1. Feature Selection Method Based on Neighborhood Relationships: Applications in EEG Signal Identification and Chinese Character Recognition

    Directory of Open Access Journals (Sweden)

    Yu-Xiang Zhao

    2016-06-01

    Full Text Available In this study, a new feature selection algorithm, the neighborhood-relationship feature selection (NRFS algorithm, is proposed for identifying rat electroencephalogram signals and recognizing Chinese characters. In these two applications, dependent relationships exist among the feature vectors and their neighboring feature vectors. Therefore, the proposed NRFS algorithm was designed for solving this problem. By applying the NRFS algorithm, unselected feature vectors have a high priority of being added into the feature subset if the neighboring feature vectors have been selected. In addition, selected feature vectors have a high priority of being eliminated if the neighboring feature vectors are not selected. In the experiments conducted in this study, the NRFS algorithm was compared with two feature algorithms. The experimental results indicated that the NRFS algorithm can extract the crucial frequency bands for identifying rat vigilance states and identifying crucial character regions for recognizing Chinese characters.

  2. A Framework of Temporal-Spatial Descriptors-Based Feature Extraction for Improved Myoelectric Pattern Recognition.

    Science.gov (United States)

    Khushaba, Rami N; Al-Timemy, Ali H; Al-Ani, Ahmed; Al-Jumaily, Adel

    2017-10-01

    The extraction of the accurate and efficient descriptors of muscular activity plays an important role in tackling the challenging problem of myoelectric control of powered prostheses. In this paper, we present a new feature extraction framework that aims to give an enhanced representation of muscular activities through increasing the amount of information that can be extracted from individual and combined electromyogram (EMG) channels. We propose to use time-domain descriptors (TDDs) in estimating the EMG signal power spectrum characteristics; a step that preserves the computational power required for the construction of spectral features. Subsequently, TDD is used in a process that involves: 1) representing the temporal evolution of the EMG signals by progressively tracking the correlation between the TDD extracted from each analysis time window and a nonlinearly mapped version of it across the same EMG channel and 2) representing the spatial coherence between the different EMG channels, which is achieved by calculating the correlation between the TDD extracted from the differences of all possible combinations of pairs of channels and their nonlinearly mapped versions. The proposed temporal-spatial descriptors (TSDs) are validated on multiple sparse and high-density (HD) EMG data sets collected from a number of intact-limbed and amputees performing a large number of hand and finger movements. Classification results showed significant reductions in the achieved error rates in comparison to other methods, with the improvement of at least 8% on average across all subjects. Additionally, the proposed TSDs achieved significantly well in problems with HD-EMG with average classification errors of <5% across all subjects using windows lengths of 50 ms only.

  3. Level set method coupled with Energy Image features for brain MR image segmentation.

    Science.gov (United States)

    Punga, Mirela Visan; Gaurav, Rahul; Moraru, Luminita

    2014-06-01

    Up until now, the noise and intensity inhomogeneity are considered one of the major drawbacks in the field of brain magnetic resonance (MR) image segmentation. This paper introduces the energy image feature approach for intensity inhomogeneity correction. Our approach of segmentation takes the advantage of image features and preserves the advantages of the level set methods in region-based active contours framework. The energy image feature represents a new image obtained from the original image when the pixels' values are replaced by local energy values computed in the 3×3 mask size. The performance and utility of the energy image features were tested and compared through two different variants of level set methods: one as the encompassed local and global intensity fitting method and the other as the selective binary and Gaussian filtering regularized level set method. The reported results demonstrate the flexibility of the energy image feature to adapt to level set segmentation framework and to perform the challenging task of brain lesion segmentation in a rather robust way.

  4. Local binary pattern variants-based adaptive texture features analysis for posed and nonposed facial expression recognition

    Science.gov (United States)

    Sultana, Maryam; Bhatti, Naeem; Javed, Sajid; Jung, Soon Ki

    2017-09-01

    Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro- and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively.

  5. An open-set detection evaluation methodology applied to language and emotion recognition

    NARCIS (Netherlands)

    Leeuwen, D.A. van; Truong, K.P.

    2007-01-01

    This paper introduces a detection methodology for recognition technologies in speech for which it is dif cult to obtain an abundance of non-target classes. An example is language recognition, where we would like to be able to measure the detection capability of a single target language without

  6. Human red blood cell recognition enhancement with three-dimensional morphological features obtained by digital holographic imaging

    Science.gov (United States)

    Jaferzadeh, Keyvan; Moon, Inkyu

    2016-12-01

    The classification of erythrocytes plays an important role in the field of hematological diagnosis, specifically blood disorders. Since the biconcave shape of red blood cell (RBC) is altered during the different stages of hematological disorders, we believe that the three-dimensional (3-D) morphological features of erythrocyte provide better classification results than conventional two-dimensional (2-D) features. Therefore, we introduce a set of 3-D features related to the morphological and chemical properties of RBC profile and try to evaluate the discrimination power of these features against 2-D features with a neural network classifier. The 3-D features include erythrocyte surface area, volume, average cell thickness, sphericity index, sphericity coefficient and functionality factor, MCH and MCHSD, and two newly introduced features extracted from the ring section of RBC at the single-cell level. In contrast, the 2-D features are RBC projected surface area, perimeter, radius, elongation, and projected surface area to perimeter ratio. All features are obtained from images visualized by off-axis digital holographic microscopy with a numerical reconstruction algorithm, and four categories of biconcave (doughnut shape), flat-disc, stomatocyte, and echinospherocyte RBCs are interested. Our experimental results demonstrate that the 3-D features can be more useful in RBC classification than the 2-D features. Finally, we choose the best feature set of the 2-D and 3-D features by sequential forward feature selection technique, which yields better discrimination results. We believe that the final feature set evaluated with a neural network classification strategy can improve the RBC classification accuracy.

  7. Does Set for Variability Mediate the Influence of Vocabulary Knowledge on the Development of Word Recognition Skills?

    Science.gov (United States)

    Tunmer, William E.; Chapman, James W.

    2012-01-01

    This study investigated the hypothesis that vocabulary influences word recognition skills indirectly through "set for variability", the ability to determine the correct pronunciation of approximations to spoken English words. One hundred forty children participating in a 3-year longitudinal study were administered reading and…

  8. Face-based recognition techniques: proposals for the metrological characterization of global and feature-based approaches

    Science.gov (United States)

    Betta, G.; Capriglione, D.; Crenna, F.; Rossi, G. B.; Gasparetto, M.; Zappa, E.; Liguori, C.; Paolillo, A.

    2011-12-01

    Security systems based on face recognition through video surveillance systems deserve great interest. Their use is important in several areas including airport security, identification of individuals and access control to critical areas. These systems are based either on the measurement of details of a human face or on a global approach whereby faces are considered as a whole. The recognition is then performed by comparing the measured parameters with reference values stored in a database. The result of this comparison is not deterministic because measurement results are affected by uncertainty due to random variations and/or to systematic effects. In these circumstances the recognition of a face is subject to the risk of a faulty decision. Therefore, a proper metrological characterization is needed to improve the performance of such systems. Suitable methods are proposed for a quantitative metrological characterization of face measurement systems, on which recognition procedures are based. The proposed methods are applied to three different algorithms based either on linear discrimination, on eigenface analysis, or on feature detection.

  9. Time-Frequency Feature Representation Using Multi-Resolution Texture Analysis and Acoustic Activity Detector for Real-Life Speech Emotion Recognition

    Directory of Open Access Journals (Sweden)

    Kun-Ching Wang

    2015-01-01

    Full Text Available The classification of emotional speech is mostly considered in speech-related research on human-computer interaction (HCI. In this paper, the purpose is to present a novel feature extraction based on multi-resolutions texture image information (MRTII. The MRTII feature set is derived from multi-resolution texture analysis for characterization and classification of different emotions in a speech signal. The motivation is that we have to consider emotions have different intensity values in different frequency bands. In terms of human visual perceptual, the texture property on multi-resolution of emotional speech spectrogram should be a good feature set for emotion classification in speech. Furthermore, the multi-resolution analysis on texture can give a clearer discrimination between each emotion than uniform-resolution analysis on texture. In order to provide high accuracy of emotional discrimination especially in real-life, an acoustic activity detection (AAD algorithm must be applied into the MRTII-based feature extraction. Considering the presence of many blended emotions in real life, in this paper make use of two corpora of naturally-occurring dialogs recorded in real-life call centers. Compared with the traditional Mel-scale Frequency Cepstral Coefficients (MFCC and the state-of-the-art features, the MRTII features also can improve the correct classification rates of proposed systems among different language databases. Experimental results show that the proposed MRTII-based feature information inspired by human visual perception of the spectrogram image can provide significant classification for real-life emotional recognition in speech.

  10. Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes.

    Science.gov (United States)

    Yebes, J Javier; Bergasa, Luis M; García-Garrido, Miguel Ángel

    2015-04-20

    Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles). In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity), while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM.

  11. Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes

    Directory of Open Access Journals (Sweden)

    J. Javier Yebes

    2015-04-01

    Full Text Available Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles. In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity, while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM.

  12. An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks

    Directory of Open Access Journals (Sweden)

    Carlos E. Galván-Tejada

    2016-01-01

    Full Text Available This work presents a human activity recognition (HAR model based on audio features. The use of sound as an information source for HAR models represents a challenge because sound wave analyses generate very large amounts of data. However, feature selection techniques may reduce the amount of data required to represent an audio signal sample. Some of the audio features that were analyzed include Mel-frequency cepstral coefficients (MFCC. Although MFCC are commonly used in voice and instrument recognition, their utility within HAR models is yet to be confirmed, and this work validates their usefulness. Additionally, statistical features were extracted from the audio samples to generate the proposed HAR model. The size of the information is necessary to conform a HAR model impact directly on the accuracy of the model. This problem also was tackled in the present work; our results indicate that we are capable of recognizing a human activity with an accuracy of 85% using the HAR model proposed. This means that minimum computational costs are needed, thus allowing portable devices to identify human activities using audio as an information source.

  13. Speech Recognition and Acoustic Features in Combined Electric and Acoustic Stimulation

    Science.gov (United States)

    Yoon, Yang-soo; Li, Yongxin; Fu, Qian-Jie

    2012-01-01

    Purpose: In this study, the authors aimed to identify speech information processed by a hearing aid (HA) that is additive to information processed by a cochlear implant (CI) as a function of signal-to-noise ratio (SNR). Method: Speech recognition was measured with CI alone, HA alone, and CI + HA. Ten participants were separated into 2 groups; good…

  14. Learning spectral-temporal features with 3D CNNs for speech emotion recognition

    NARCIS (Netherlands)

    Kim, Jaebok; Truong, Khiet; Englebienne, Gwenn; Evers, Vanessa

    2017-01-01

    In this paper, we propose to use deep 3-dimensional convolutional networks (3D CNNs) in order to address the challenge of modelling spectro-temporal dynamics for speech emotion recognition (SER). Compared to a hybrid of Convolutional Neural Network and Long-Short-Term-Memory (CNN-LSTM), our proposed

  15. Multiple Levels of Recognition in Ants: A Feature of Complex Societies

    DEFF Research Database (Denmark)

    D'Ettorre, Patrizia

    2008-01-01

    Communication and recognition are essential for social life. Social insects are good model systems to study social behavior and complexity because their societies are evolutionarily stable and ecologically successful. Ants, in particular, show a large variety of adaptations and are extremely dive...

  16. Signal features of surface electromyography in advanced Parkinson's disease during different settings of deep brain stimulation.

    Science.gov (United States)

    Rissanen, Saara M; Ruonala, Verneri; Pekkonen, Eero; Kankaanpää, Markku; Airaksinen, Olavi; Karjalainen, Pasi A

    2015-12-01

    Electromyography (EMG) and acceleration (ACC) measurements are potential methods for quantifying efficacy of deep brain stimulation (DBS) treatment in Parkinson's disease (PD). The treatment efficacy depends on the settings of DBS parameters (pulse amplitude, frequency and width). This study quantified, if EMG and ACC signal features differ between different DBS settings and if DBS effect is unequal between different muscles. EMGs were measured from biceps brachii (BB) and tibialis anterior (TA) muscles of 13 PD patients. ACCs were measured from wrists. Measurements were performed during seven different settings of DBS and analyzed using methods based on spectral analysis, signal morphology and nonlinear dynamics. The results showed significant within-subject differences in the EMG signal kurtosis, correlation dimension, recurrence rate and EMG-ACC coherence between different DBS settings for BB but not for TA muscles. Correlations between EMG feature values and clinical rest tremor and rigidity scores were weak but significant. Surface EMG features differed between different DBS settings and DBS effect was unequal between upper and lower limb muscles. EMG changes pointed to previously defined optimal settings in most of patients, which should be quantified even more deeply in the upcoming studies. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  17. Characterization of the Effectiveness of Reporting Lists of Small Feature Sets Relative to the Accuracy of the Prior Biological Knowledge

    Directory of Open Access Journals (Sweden)

    Chen Zhao

    2010-03-01

    Full Text Available When confronted with a small sample, feature-selection algorithms often fail to find good feature sets, a problem exacerbated for high-dimensional data and large feature sets. The problem is compounded by the fact that, if one obtains a feature set with a low error estimate, the estimate is unreliable because training-data-based error estimators typically perform poorly on small samples, exhibiting optimistic bias or high variance. One way around the problem is limit the number of features being considered, restrict features sets to sizes such that all feature sets can be examined by exhaustive search, and report a list of the best performing feature sets. If the list is short, then it greatly restricts the possible feature sets to be considered as candidates; however, one can expect the lowest error estimates obtained to be optimistically biased so that there may not be a close-to-optimal feature set on the list. This paper provides a power analysis of this methodology; in particular, it examines the kind of results one should expect to obtain relative to the length of the list and the number of discriminating features among those considered. Two measures are employed. The first is the probability that there is at least one feature set on the list whose true classification error is within some given tolerance of the best feature set and the second is the expected number of feature sets on the list whose true errors are within the given tolerance of the best feature set. These values are plotted as functions of the list length to generate power curves. The results show that, if the number of discriminating features is not too small—that is, the prior biological knowledge is not too poor—then one should expect, with high probability, to find good feature sets. Availability: companion website at http://gsp.tamu.edu/Publications/supplementary/zhao09a/

  18. A spatiotemporal feature-based approach for facial expression recognition from depth video

    Science.gov (United States)

    Uddin, Md. Zia

    2015-07-01

    In this paper, a novel spatiotemporal feature-based method is proposed to recognize facial expressions from depth video. Independent Component Analysis (ICA) spatial features of the depth faces of facial expressions are first augmented with the optical flow motion features. Then, the augmented features are enhanced by Fisher Linear Discriminant Analysis (FLDA) to make them robust. The features are then combined with on Hidden Markov Models (HMMs) to model different facial expressions that are later used to recognize appropriate expression from a test expression depth video. The experimental results show superior performance of the proposed approach over the conventional methods.

  19. Management and performance features of cancer centers in Europe: A fuzzy-set analysis

    NARCIS (Netherlands)

    Wind, Anke; Lobo, Mariana Fernandes; van Dijk, Joris; Lepage-Nefkens, Isabelle; Laranja-Pontes, Jose; da Conceicao Goncalves, Vitor; van Harten, Willem H.; Rocha-Goncalves, Francisco Nuno

    2016-01-01

    The specific aim of this study is to identify the performance features of cancer centers in the European Union by using a fuzzy-set qualitative comparative analysis (fsQCA). The fsQCA method represents cases (cancer centers) as a combination of explanatory and outcome conditions. This study uses

  20. Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features

    Directory of Open Access Journals (Sweden)

    Min Zhang

    2015-01-01

    Full Text Available Control charts have been widely utilized for monitoring process variation in numerous applications. Abnormal patterns exhibited by control charts imply certain potentially assignable causes that may deteriorate the process performance. Most of the previous studies are concerned with the recognition of single abnormal control chart patterns (CCPs. This paper introduces an intelligent hybrid model for recognizing the mixture CCPs that includes three main aspects: feature extraction, classifier, and parameters optimization. In the feature extraction, statistical and shape features of observation data are used in the data input to get the effective data for the classifier. A multiclass support vector machine (MSVM applies for recognizing the mixture CCPs. Finally, genetic algorithm (GA is utilized to optimize the MSVM classifier by searching the best values of the parameters of MSVM and kernel function. The performance of the hybrid approach is evaluated by simulation experiments, and simulation results demonstrate that the proposed approach is able to effectively recognize mixture CCPs.

  1. Adaptive fuzzy leader clustering of complex data sets in pattern recognition

    Science.gov (United States)

    Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda

    1992-01-01

    A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.

  2. Representing Objects using Global 3D Relational Features for Recognition Tasks

    DEFF Research Database (Denmark)

    Mustafa, Wail

    2015-01-01

    -variant shape representations. We achieve both high performance and learning efficiency. The learning efficiency is expressed in terms of scalability to many objects while requiring only a few training samples. The system has also been applied in real-world application in which its reliability allowed......In robotic systems, visual interpretations of the environment compose an essential element in a variety of applications, especially those involving manipulation of objects. Interpreting the environment is often done in terms of recognition of objects using machine learning approaches. For user...... to initiate higher-level semantic interpretations of complex scenes. In the object category recognition task, we present a system that is capable of assigning multiple and nested categories for novel objects using a method developed for this purpose. Integrating this method with other multi-label learning...

  3. Real-time hand gesture recognition based on feature points extraction

    Science.gov (United States)

    Zaghbani, Soumaya; Jaouedi, Neziha; Boujnah, Noureddine; Bouhlel, Mohamed Salim

    2017-03-01

    Tracking moving objects is an area increasingly known in computer vision field. It plays a very important role in human-computer interaction. In this context we have developed a hand tracking and gesture recognition system that allows interaction with the machine in an intuitive and natural way. To ensure the tracking we apply the Kalman filter and detect the optimal points of the hand in order to determine the gesture expressed by user.

  4. Feature extraction using gray-level co-occurrence matrix of wavelet coefficients and texture matching for batik motif recognition

    Science.gov (United States)

    Suciati, Nanik; Herumurti, Darlis; Wijaya, Arya Yudhi

    2017-02-01

    Batik is one of Indonesian's traditional cloth. Motif or pattern drawn on a piece of batik fabric has a specific name and philosopy. Although batik cloths are widely used in everyday life, but only few people understand its motif and philosophy. This research is intended to develop a batik motif recognition system which can be used to identify motif of Batik image automatically. First, a batik image is decomposed into sub-images using wavelet transform. Six texture descriptors, i.e. max probability, correlation, contrast, uniformity, homogenity and entropy, are extracted from gray-level co-occurrence matrix of each sub-image. The texture features are then matched to the template features using canberra distance. The experiment is performed on Batik Dataset consisting of 1088 batik images grouped into seven motifs. The best recognition rate, that is 92,1%, is achieved using feature extraction process with 5 level wavelet decomposition and 4 directional gray-level co-occurrence matrix.

  5. DeepGaze II: Reading fixations from deep features trained on object recognition

    OpenAIRE

    Kümmerer, Matthias; Wallis, Thomas S. A.; Bethge, Matthias

    2016-01-01

    Here we present DeepGaze II, a model that predicts where people look in images. The model uses the features from the VGG-19 deep neural network trained to identify objects in images. Contrary to other saliency models that use deep features, here we use the VGG features for saliency prediction with no additional fine-tuning (rather, a few readout layers are trained on top of the VGG features to predict saliency). The model is therefore a strong test of transfer learning. After conservative cro...

  6. An automatic image recognition approach

    Directory of Open Access Journals (Sweden)

    Tudor Barbu

    2007-07-01

    Full Text Available Our paper focuses on the graphical analysis domain. We propose an automatic image recognition technique. This approach consists of two main pattern recognition steps. First, it performs an image feature extraction operation on an input image set, using statistical dispersion features. Then, an unsupervised classification process is performed on the previously obtained graphical feature vectors. An automatic region-growing based clustering procedure is proposed and utilized in the classification stage.

  7. Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries

    Directory of Open Access Journals (Sweden)

    Shengyu Liu

    2015-12-01

    Full Text Available Semantic features are very important for machine learning-based drug name recognition (DNR systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts. Building large-scale drug dictionaries is a time-consuming task and adding new drugs to existing drug dictionaries immediately after they are developed is also a challenge. In recent years, word embeddings that contain rich latent semantic information of words have been widely used to improve the performance of various natural language processing tasks. However, they have not been used in DNR systems. Compared to the semantic features based on drug dictionaries, the advantage of word embeddings lies in that learning them is unsupervised. In this paper, we investigate the effect of semantic features based on word embeddings on DNR and compare them with semantic features based on three drug dictionaries. We propose a conditional random fields (CRF-based system for DNR. The skip-gram model, an unsupervised algorithm, is used to induce word embeddings on about 17.3 GigaByte (GB unlabeled biomedical texts collected from MEDLINE (National Library of Medicine, Bethesda, MD, USA. The system is evaluated on the drug-drug interaction extraction (DDIExtraction 2013 corpus. Experimental results show that word embeddings significantly improve the performance of the DNR system and they are competitive with semantic features based on drug dictionaries. F-score is improved by 2.92 percentage points when word embeddings are added into the baseline system. It is comparative with the improvements from semantic features based on drug dictionaries. Furthermore, word embeddings are complementary to the semantic features based on drug dictionaries. When both word embeddings and semantic features based on drug dictionaries are added, the system achieves the best performance with an F-score of 78.37%, which outperforms the best system of the DDIExtraction 2013

  8. Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

    Science.gov (United States)

    Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

    2017-01-01

    Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images. PMID:28335510

  9. Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

    Directory of Open Access Journals (Sweden)

    Dat Tien Nguyen

    2017-03-01

    Full Text Available Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT, speed-up robust feature (SURF, local binary patterns (LBP, histogram of oriented gradients (HOG, and weighted HOG. Recently, the convolutional neural network (CNN method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

  10. Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

    Science.gov (United States)

    Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

    2017-03-20

    Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

  11. Human Depth Sensors-Based Activity Recognition Using Spatiotemporal Features and Hidden Markov Model for Smart Environments

    Directory of Open Access Journals (Sweden)

    Ahmad Jalal

    2016-01-01

    Full Text Available Nowadays, advancements in depth imaging technologies have made human activity recognition (HAR reliable without attaching optical markers or any other motion sensors to human body parts. This study presents a depth imaging-based HAR system to monitor and recognize human activities. In this work, we proposed spatiotemporal features approach to detect, track, and recognize human silhouettes using a sequence of RGB-D images. Under our proposed HAR framework, the required procedure includes detection of human depth silhouettes from the raw depth image sequence, removing background noise, and tracking of human silhouettes using frame differentiation constraints of human motion information. These depth silhouettes extract the spatiotemporal features based on depth sequential history, motion identification, optical flow, and joints information. Then, these features are processed by principal component analysis for dimension reduction and better feature representation. Finally, these optimal features are trained and they recognized activity using hidden Markov model. During experimental results, we demonstrate our proposed approach on three challenging depth videos datasets including IM-DailyDepthActivity, MSRAction3D, and MSRDailyActivity3D. All experimental results show the superiority of the proposed approach over the state-of-the-art methods.

  12. Probabilistic active recognition of multiple objects using Hough-based geometric matching features

    CSIR Research Space (South Africa)

    Govender, N

    2015-01-01

    Full Text Available be recognized simultaneously, and occlusion and clutter (through distracter objects) is common. We propose a representation for object viewpoints using Hough transform based geometric matching features, which are robust in such circumstances. We show how...

  13. Coastal karren features in temperate microtidal settings: spatial organization and temporal evolution

    Directory of Open Access Journals (Sweden)

    Lluís Gómez-Pujol

    2010-04-01

    Full Text Available Basin pools are the diagnostic feature of Coastal Karren landscape at temperate settings. According to the size and connectivity parameters four morphological zones are identified along limestone coastal profiles. Each zone reflects the balance between the effects of physical and chemical weathering-erosion agents. Broadly, marine abrasion, bioerosion and biological driven solution show a larger influence seaward, whereas non-biological driven solution enhances its participation landward

  14. Complex extreme learning machine applications in terahertz pulsed signals feature sets.

    Science.gov (United States)

    Yin, X-X; Hadjiloucas, S; Zhang, Y

    2014-11-01

    This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed

  15. Quantifying Histological Features of Cancer Biospecimens for Biobanking Quality Assurance Using Automated Morphometric Pattern Recognition Image Analysis Algorithms

    Science.gov (United States)

    Webster, Joshua D.; Simpson, Eleanor R.; Michalowski, Aleksandra M.; Hoover, Shelley B.; Simpson, R. Mark

    2011-01-01

    Biorepository-supported translational research depends on high-quality, well-annotated specimens. Histopathology assessment contributes insight into how representative lesions are for research objectives. Feasibility of documenting histological proportions of tumor and stroma was studied in an effort to enhance information regarding biorepository tissue heterogeneity. Using commercially available software, unique spatial-spectral algorithms were developed for applying automated pattern recognition morphometric image analysis to quantify histologic tumor and nontumor tissue areas in biospecimen tissue sections. Measurements were acquired successfully for 75/75 (100%) lymphomas, 76/77 (98.7%) osteosarcomas, and 60/70 (85.7%) melanomas. The percentage of tissue area occupied by tumor varied among patients and tumor types and was distributed around medians of 94% [interquartile range (IQR)=14%] for lymphomas, 84% for melanomas (IQR=24%), and 39% for osteosarcomas (IQR=44%). Within-patient comparisons from a subset, including multiple individual patient specimens, revealed ≤12% median coefficient of variation (CV) for lymphomas and melanomas. Phenotypic heterogeneity of osteosarcomas resulted in 33% median CV. Uniformly applied, tumor-specific pattern recognition software permits automated tissue-feature quantification. Furthermore, dispersion analyses of area measurements across collections, as well as of multiple specimens from individual patients, support using limited tissue slices to gauge features for some tumor types. Quantitative image analysis automation is anticipated to minimize variability associated with routine biorepository pathologic evaluations and enhance biomarker discovery by helping to guide the selection of study-appropriate specimens. PMID:21966258

  16. The Affordance of Speech Recognition Technology for EFL Learning in an Elementary School Setting

    Science.gov (United States)

    Liaw, Meei-Ling

    2014-01-01

    This study examined the use of speech recognition (SR) technology to support a group of elementary school children's learning of English as a foreign language (EFL). SR technology has been used in various language learning contexts. Its application to EFL teaching and learning is still relatively recent, but a solid understanding of its…

  17. Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals.

    Science.gov (United States)

    Elhaj, Fatin A; Salim, Naomie; Harris, Arief R; Swee, Tan Tian; Ahmed, Taqwa

    2016-04-01

    Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart abnormalities. Due to the presence of noise, the non-stationary nature of the ECG signal (i.e. the changing morphology of the ECG signal with respect to time) and the irregularity of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. The computer-aided analysis of ECG results assists physicians to detect cardiovascular diseases. The development of many existing arrhythmia systems has depended on the findings from linear experiments on ECG data which achieve high performance on noise-free data. However, nonlinear experiments characterize the ECG signal more effectively sense, extract hidden information in the ECG signal, and achieve good performance under noisy conditions. This paper investigates the representation ability of linear and nonlinear features and proposes a combination of such features in order to improve the classification of ECG data. In this study, five types of beat classes of arrhythmia as recommended by the Association for Advancement of Medical Instrumentation are analyzed: non-ectopic beats (N), supra-ventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F) and unclassifiable and paced beats (U). The characterization ability of nonlinear features such as high order statistics and cumulants and nonlinear feature reduction methods such as independent component analysis are combined with linear features, namely, the principal component analysis of discrete wavelet transform coefficients. The features are tested for their ability to differentiate different classes of data using different classifiers, namely, the support vector machine and neural network methods with tenfold cross-validation. Our proposed method is able to classify the N, S, V, F and U arrhythmia classes with high accuracy (98.91%) using a combined support

  18. Targeted Feature Recognition Using Mechanical Spatial Filtering with a Low-Cost Compliant Strain Sensor.

    Science.gov (United States)

    Barnett, Eli M; Lofton, Julian J; Yu, Miao; Bruck, Hugh A; Smela, Elisabeth

    2017-07-11

    A tactile sensing architecture is presented for detection of surface features that have a particular target size, and the concept is demonstrated with a braille pattern. The approach is akin to an inverse of mechanical profilometry. The sensing structure is constructed by suspending a stretchable strain-sensing membrane over a cavity. The structure is moved over the surface, and a signal is generated through mechanical spatial filtering if a feature is small enough to penetrate into the cavity. This simple design is tailorable and can be realized by standard machining or 3D printing. Images of target features can be produced with even a low-cost compliant sensor. In this work a disposable elastomeric piezoresistive strain sensor was used over a cylindrical "finger" part with a groove having a width corresponding to the braille dot size. A model was developed to help understand the working principle and guide finger design, revealing amplification when the cavity matches the feature size. The new sensing concept has the advantages of being easily reconfigured for a variety of sensing problems and retrofitted to a wide range of robotic hands, as well as compatibility with many compliant sensor types.

  19. Characterization of Mammographic Masses Based on Level Set Segmentation with New Image Features and Patient Information

    Science.gov (United States)

    Shi, Jiazheng; Sahiner, Berkman; Chan, Heang-Ping; Ge, Jun; Hadjiiski, Lubomir; Helvie, Mark A.; Nees, Alexis; Wu, Yi-Ta; Wei, Jun; Zhou, Chuan; Zhang, Yiheng; Cui, Jing

    2009-01-01

    Computer-aided diagnosis (CAD) for characterization of mammographic masses as malignant or benign has the potential to assist radiologists in reducing the biopsy rate without increasing false negatives. The purpose of this study was to develop an automated method for mammographic mass segmentation and explore new image based features in combination with patient information in order to improve the performance of mass characterization. Our previous CAD system, which used the active contour segmentation, and morphological, textural, and spiculation features, has achieved promising results in mass characterization. The new CAD system is based on the level set method, and includes two new types of image features related to the presence of microcalcifications with the mass and abruptness of the mass margin, and patient age. A linear discriminant analysis (LDA) classifier with stepwise feature selection was used to merge the extracted features into a classification score. The classification accuracy was evaluated using the area under the receiver operating characteristic curve. Our primary data set consisted of 427 biopsy-proven masses (200 malignant and 227 benign) in 909 regions of interest (ROIs) (451 malignant and 458 benign) from multiple mammographic views. Leave-one-case-out resampling was used for training and testing. The new CAD system based on the level set segmentation and the new mammographic feature space achieved a view-based Az value of 0.83±0.01. The improvement compared to the previous CAD system was statistically significant (p=0.02). When patient age was included in the new CAD system, view-based and case-based Az values were 0.85±0.01 and 0.87±0.02, respectively. The performance of the new CAD system was also compared to an experienced radiologist’s likelihood of malignancy rating. When patient age was used in classification, the accuracy of the new CAD system was comparable to that of the radiologist (p=0.34). The study also demonstrated the

  20. Predominant membrane localization is an essential feature of the bacterial signal recognition particle receptor

    Directory of Open Access Journals (Sweden)

    Graumann Peter

    2009-11-01

    Full Text Available Abstract Background The signal recognition particle (SRP receptor plays a vital role in co-translational protein targeting, because it connects the soluble SRP-ribosome-nascent chain complex (SRP-RNCs to the membrane bound Sec translocon. The eukaryotic SRP receptor (SR is a heterodimeric protein complex, consisting of two unrelated GTPases. The SRβ subunit is an integral membrane protein, which tethers the SRP-interacting SRα subunit permanently to the endoplasmic reticulum membrane. The prokaryotic SR lacks the SRβ subunit and consists of only the SRα homologue FtsY. Strikingly, although FtsY requires membrane contact for functionality, cell fractionation studies have localized FtsY predominantly to the cytosolic fraction of Escherichia coli. So far, the exact function of the soluble SR in E. coli is unknown, but it has been suggested that, in contrast to eukaryotes, the prokaryotic SR might bind SRP-RNCs already in the cytosol and only then initiates membrane targeting. Results In the current study we have determined the contribution of soluble FtsY to co-translational targeting in vitro and have re-analysed the localization of FtsY in vivo by fluorescence microscopy. Our data show that FtsY can bind to SRP-ribosome nascent chains (RNCs in the absence of membranes. However, these soluble FtsY-SRP-RNC complexes are not efficiently targeted to the membrane. In contrast, we observed effective targeting of SRP-RNCs to membrane-bond FtsY. These data show that soluble FtsY does not contribute significantly to cotranslational targeting in E. coli. In agreement with this observation, our in vivo analyses of FtsY localization in bacterial cells by fluorescence microscopy revealed that the vast majority of FtsY was localized to the inner membrane and that soluble FtsY constituted only a negligible species in vivo. Conclusion The exact function of the SRP receptor (SR in bacteria has so far been enigmatic. Our data show that the bacterial SR is

  1. Feature recognition of metal salt spray corrosion based on color spaces statistics analysis

    Science.gov (United States)

    Zou, Zhi; Ma, Liqun; Fan, Qiuqin; Gan, Xiaochuan; Qiao, Lei

    2017-09-01

    The article proposed a method to quantify corrosion characteristics of high strength alloy steel samples using digital image processing technique in color spaces. The distribution histograms in different channels of different spaces in corrosion images are plotted and analyzed. Select the proper color channel to extract the corrosion characteristics among three different spaces of RGB space, HSV space, YCbCr space. Combined the theory of corrosion generation, the data of color channels is processed and the feature of metal material salt spray corrosion is recognized. Through processing several sample color images of alloy steel, it is proved that the feature extracted by this procedure has better accuracy and the corrosion degree is quantifiable and the precision of discriminating the corrosion is improved.

  2. Towards human behavior recognition based on spatio temporal features and support vector machines

    Science.gov (United States)

    Ghabri, Sawsen; Ouarda, Wael; Alimi, Adel M.

    2017-03-01

    Security and surveillance are vital issues in today's world. The recent acts of terrorism have highlighted the urgent need for efficient surveillance. There is indeed a need for an automated system for video surveillance which can detect identity and activity of person. In this article, we propose a new paradigm to recognize an aggressive human behavior such as boxing action. Our proposed system for human activity detection includes the use of a fusion between Spatio Temporal Interest Point (STIP) and Histogram of Oriented Gradient (HoG) features. The novel feature called Spatio Temporal Histogram Oriented Gradient (STHOG). To evaluate the robustness of our proposed paradigm with a local application of HoG technique on STIP points, we made experiments on KTH human action dataset based on Multi Class Support Vector Machines classification. The proposed scheme outperforms basic descriptors like HoG and STIP to achieve 82.26% us an accuracy value of classification rate.

  3. Evaluation of Waveform Structure Features on Time Domain Target Recognition under Cross Polarization

    Science.gov (United States)

    Selver, M. A.; Seçmen, M.; Zoral, E. Y.

    2016-08-01

    Classification of aircraft targets from scattered electromagnetic waves is a challenging application, which suffers from aspect angle dependency. In order to eliminate the adverse effects of aspect angle, various strategies were developed including the techniques that rely on extraction of several features and design of suitable classification systems to process them. Recently, a hierarchical method, which uses features that take advantage of waveform structure of the scattered signals, is introduced and shown to have effective results. However, this approach has been applied to the special cases that consider only a single planar component of electric field that cause no-cross polarization at the observation point. In this study, two small scale aircraft models, Boeing-747 and DC-10, are selected as the targets and various polarizations are used to analyse the cross-polarization effects on system performance of the aforementioned method. The results reveal the advantages and the shortcomings of using waveform structures in time-domain target identification.

  4. A novel search coding method for generic object recognition based on shared features

    Science.gov (United States)

    Zheng, Ping; Sang, Nong

    2009-10-01

    In this paper, we consider the combined problem of distinguishing classes from the background and from each other, and propose an improved framework based on the previous state-of-the-art approaches. In the process of building ECOC (Error Correcting Output Coding) matrix (also called as sharing matrix), we adopt an encoding rule of one-versus-all, and maximize Hamming distance in categories as far as possible through heuristic search in sharing-code maps (i.e., layer joint boosting). Then the final classifier is responsible for detection, and ECOC matrix for recognition. In order to make full use of the output of the final classifier and its corresponding ECOC matrix, the following measures are worth considering: Firstly, a logistic function of the output mentioned above is used for a posterior probability of each codeword. Therefore the identified class label is the one corresponding to the codeword of Maximum a posteriori (MAP). Secondly, a similarity measurement utilizing the confusion matrix is advanced to focus on the similarities between classes. Thirdly, for the purpose of adaptive adjustment in Hamming distance, we change the subsequent search coding method according to the confusion matrix until the training errors are convergent. The experimental results illustrate the effectiveness of the proposed approach.

  5. Automatic Target Recognition: Statistical Feature Selection of Non-Gaussian Distributed Target Classes

    Science.gov (United States)

    2011-06-01

    SequentialForwardSelection_Hellinger(class1,class2,NumFeatComb) %%% SFS Algorithm---Best is the vector of the best subset GSF =1.1; dX=.1; NofFeatures=size... GSF ,dX); f_class1=KDE_MJW(class1(:,combi),GridCell); f_class2=KDE_MJW(class2(:,combi),GridCell); f_class1N=Normalize_PDF

  6. Mental sets in conduct problem youth with psychopathic features: entity versus incremental theories of intelligence.

    Science.gov (United States)

    Salekin, Randall T; Lester, Whitney S; Sellers, Mary-Kate

    2012-08-01

    The purpose of the current study was to examine the effect of a motivational intervention on conduct problem youth with psychopathic features. Specifically, the current study examined conduct problem youths' mental set (or theory) regarding intelligence (entity vs. incremental) upon task performance. We assessed 36 juvenile offenders with psychopathic features and tested whether providing them with two different messages regarding intelligence would affect their functioning on a task related to academic performance. The study employed a MANOVA design with two motivational conditions and three outcomes including fluency, flexibility, and originality. Results showed that youth with psychopathic features who were given a message that intelligence grows over time, were more fluent and flexible than youth who were informed that intelligence is static. There were no significant differences between the groups in terms of originality. The implications of these findings are discussed including the possible benefits of interventions for adolescent offenders with conduct problems and psychopathic features. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

  7. Feature extraction of the first difference of EMG time series for EMG pattern recognition.

    Science.gov (United States)

    Phinyomark, Angkoon; Quaine, Franck; Charbonnier, Sylvie; Serviere, Christine; Tarpin-Bernard, Franck; Laurillau, Yann

    2014-11-01

    This paper demonstrates the utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary. The technique was evaluated by three stationarity tests consisting of the variation of two statistical properties, i.e., mean and standard deviation, and the reverse arrangements test. As a result of the proposed technique, the first difference of EMG time series became more stationary compared to the original measured signal. Based on this finding, the performance of time-domain features extracted from raw and transformed EMG was investigated via an EMG classification problem (i.e., eight dynamic motions and four EMG channels) on data from 18 subjects. The results show that the classification accuracies of all features extracted from the transformed signals were higher than features extracted from the original signals for six different classifiers including quadratic discriminant analysis. On average, the proposed differencing technique improved classification accuracies by 2-8%. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  8. Detection of Small-Scaled Features Using Landsat and Sentinel-2 Data Sets

    Science.gov (United States)

    Steensen, Torge; Muller, Sonke; Dresen, Boris; Buscher, Olaf

    2016-08-01

    In advanced times of renewable energies, our attention has to be on secondary features that can be utilised to enhance our independence from fossil fuels. In terms of biomass, this focus lies on small-scaled features like vegetation units alongside roads or hedges between agricultural fields. Currently, there is no easily- accessible inventory, if at all, outlining the growth and re-growth patterns of such vegetation. Since they are trimmed at least annually to allow the passing of traffic, we can, theoretically, harvest the cut and convert it into energy. This, however, requires a map outlining the vegetation growth and the potential energy amount at different locations as well as adequate transport routes and potential processing plant locations. With the help of Landsat and Sentinel-2 data sets, we explore the possibilities to create such a map. Additional data is provided in the form of regularly acquired, airborne orthophotos and GIS-based infrastructure data.

  9. Testing of Haar-Like Feature in Region of Interest Detection for Automated Target Recognition (ATR) System

    Science.gov (United States)

    Zhang, Yuhan; Lu, Dr. Thomas

    2010-01-01

    The objectives of this project were to develop a ROI (Region of Interest) detector using Haar-like feature similar to the face detection in Intel's OpenCV library, implement it in Matlab code, and test the performance of the new ROI detector against the existing ROI detector that uses Optimal Trade-off Maximum Average Correlation Height filter (OTMACH). The ROI detector included 3 parts: 1, Automated Haar-like feature selection in finding a small set of the most relevant Haar-like features for detecting ROIs that contained a target. 2, Having the small set of Haar-like features from the last step, a neural network needed to be trained to recognize ROIs with targets by taking the Haar-like features as inputs. 3, using the trained neural network from the last step, a filtering method needed to be developed to process the neural network responses into a small set of regions of interests. This needed to be coded in Matlab. All the 3 parts needed to be coded in Matlab. The parameters in the detector needed to be trained by machine learning and tested with specific datasets. Since OpenCV library and Haar-like feature were not available in Matlab, the Haar-like feature calculation needed to be implemented in Matlab. The codes for Adaptive Boosting and max/min filters in Matlab could to be found from the Internet but needed to be integrated to serve the purpose of this project. The performance of the new detector was tested by comparing the accuracy and the speed of the new detector against the existing OTMACH detector. The speed was referred as the average speed to find the regions of interests in an image. The accuracy was measured by the number of false positives (false alarms) at the same detection rate between the two detectors.

  10. Wavelet Packet Feature Assessment for High-density Myoelectric Pattern Recognition and Channel Selection toward Stroke Rehabilitation

    Directory of Open Access Journals (Sweden)

    Dongqing Wang

    2016-11-01

    Full Text Available This study presented wavelet packet feature assessment of neural control information in paretic upper-limb muscles of stroke survivors for myoelectric pattern recognition, taking advantage of high-resolution time-frequency representations of surface electromyographic (EMG signals. On this basis, a novel channel selection method was developed by combining the Fisher's class separability index (FCSI and the sequential feedforward selection (SFS analyses, in order to determine a small number of appropriate EMG channels from original high-density EMG electrode array. The advantages of the wavelet packet features and the channel selection analyses were further illustrated by comparing with previous conventional approaches, in terms of classification performance when identifying 20 functional arm/hand movements implemented by 12 stroke survivors. This study offers a practical approach including paretic EMG feature extraction and channel selection that enables active myoelectric control of multiple degrees of freedom with paretic muscles. All these efforts will facilitate upper-limb dexterity restoration and improved stroke rehabilitation.

  11. A set-theoretic approach to linguistic feature structures and unification algorithms (I

    Directory of Open Access Journals (Sweden)

    N. Curteanu

    2000-10-01

    Full Text Available The paper proposes formal inductive definitions for linguistic feature structures (FSs taking values within a class of value types or sorts: single, disjunctive, (ordered lists, multisets (or bags, po-multisets (multisets embedded into a partially ordered set, and indexed (re-entrance values. The linguistic realization (semantics of the considered sorts is proposed. The FSs having these multi-sort values are organized as (rooted directed acyclic graphs. The concrete model of the FSs we had in mind for our set-theoretic definitions are the FSs used within the well-known HPSG linguistic theory. Set-theoretic general definitions for the proposed multi-sort FSs are defined. These constructive definitions start from atomic values and build recurrently multi-sorted values and structures, providing naturally a fixed-point semantics of the obtained FSs as a counterpart to the large class of logical semantics models on FSs. The linguistic unification algorithm based on tableau-subsumption is outlined. The Prolog code of the unification algorithm is provided and results of running it on some of the main multi-sort FSs is enclosed in the appendices. We consider the proposed formal approach to FS definitions and unification as necessary steps to set-theoretical implementations of natural language processing systems.

  12. A set-theoretic approach to linguistic feature structures and unification algorithms (II

    Directory of Open Access Journals (Sweden)

    N.Curteanu

    2001-02-01

    Full Text Available The paper proposes formal inductive definitions for linguistic feature structures (FSs taking values within a class of value types or sorts: single, disjunctive, (ordered lists, multisets (or bags, po-multisets (multisets embedded into a partially ordered set, and indexed (re-entrance values. The linguistic realization (semantics of the considered sorts is proposed. The FSs having these multi-sort values are organized as (rooted directed acyclic graphs. The concrete model of the FSs we had in mind for our set-theoretic definitions are the FSs used within the well-known HPSG linguistic theory. Set-theoretic general definitions for the proposed multi-sort FSs are defined. These constructive definitions start from atomic values and build recurrent multi-sorted values and structures, providing naturally a fixed-point semantics of the obtained FSs as a counterpart to the large class of logical semantics models on FSs. The linguistic unification algorithm based on tableau-subsumption is outlined. The Prolog code of the unification algorithm is provided and results of running it on some of the main multi-sort FSs is enclosed in the appendices. We consider the proposed formal approach to FSs definitions and unification as necessary steps to set-theoretical implementations of natural language processing systems.

  13. Ocean feature recognition using genetic algorithms with fuzzy fitness functions (GA/F3)

    Science.gov (United States)

    Ankenbrandt, C. A.; Buckles, B. P.; Petry, F. E.; Lybanon, M.

    1990-01-01

    A model for genetic algorithms with semantic nets is derived for which the relationships between concepts is depicted as a semantic net. An organism represents the manner in which objects in a scene are attached to concepts in the net. Predicates between object pairs are continuous valued truth functions in the form of an inverse exponential function (e sub beta lxl). 1:n relationships are combined via the fuzzy OR (Max (...)). Finally, predicates between pairs of concepts are resolved by taking the average of the combined predicate values of the objects attached to the concept at the tail of the arc representing the predicate in the semantic net. The method is illustrated by applying it to the identification of oceanic features in the North Atlantic.

  14. A statistical investigation into the stability of iris recognition in diverse population sets

    Science.gov (United States)

    Howard, John J.; Etter, Delores M.

    2014-05-01

    Iris recognition is increasingly being deployed on population wide scales for important applications such as border security, social service administration, criminal identification and general population management. The error rates for this incredibly accurate form of biometric identification are established using well known, laboratory quality datasets. However, it is has long been acknowledged in biometric theory that not all individuals have the same likelihood of being correctly serviced by a biometric system. Typically, techniques for identifying clients that are likely to experience a false non-match or a false match error are carried out on a per-subject basis. This research makes the novel hypothesis that certain ethnical denominations are more or less likely to experience a biometric error. Through established statistical techniques, we demonstrate this hypothesis to be true and document the notable effect that the ethnicity of the client has on iris similarity scores. Understanding the expected impact of ethnical diversity on iris recognition accuracy is crucial to the future success of this technology as it is deployed in areas where the target population consists of clientele from a range of geographic backgrounds, such as border crossings and immigration check points.

  15. Research on Remote Sensing recognition features of Yuan Yang Terraces in Yunnan Province (China)

    Science.gov (United States)

    Xiang, Jie; Chen, Jianping; Lai, ZiLi; Yang, Wei

    2016-04-01

    Yuan Yang terraces is one of the most famous terraces in China, and it was successfully listed in the world heritage list at the 37th world heritage convention. On the one hand, Yuan Yang terraces retain more soil and water, to reduce both hydrological connectivity and erosion, and to support irrigation. On the other hand, It has the important tourism value, bring the huge revenue to local residents. In order to protect and make use of Yuan Yang terraces better, This study analyzed the spatial distribution and spectral characteristics of terraces:(1) Through visual interpretation, the study recognized the terraces based on the spatial adjusted remote sensing image (2010 Geoeye-1 with resolution of 1m/pix), and extracted topographic feature (elevation, slope, aspect, etc.) based on the digital elevation model with resolution of 20m/pix. The terraces cover a total area of about 11.58Km2, accounted for 24.4% of the whole study area. The terraces appear at range from 1400m to 1800m in elevation, 10°to 20°in slope, northwest to northeast in aspect; (2) Using the method of weight of evidence, this study assessed the importance of different topographic feature. The results show that the sort of importance: elevation>slope>aspect; (3) The study counted the Normalized Difference Vegetation Index (NDVI) changes of terraces throughout the year, based on the landsat-5 image with resolution of 30m/pix. The results show that the changes of terraces' NDVI are bigger than other stuff (e.g. forest, road, house, etc.). Those work made a good preparations for establishing the dynamic remote sensing monitoring system of Yuan Yang terraces.

  16. Selection of an optimal feature set to predict heart transplantation outcomes.

    Science.gov (United States)

    Medved, Dennis; Nugues, Pierre; Nilsson, Johan

    2016-08-01

    Heart transplantation (HT) is a life saving procedure, but a limited donor supply forces the surgeons to prioritize the recipients. The understanding of factors that predict mortality could help the doctors with this task. The objective of this study is to find locally optimal feature sets to predict survival of HT patients for different time periods. To this end, we applied logistic regression together with a greedy forward and backward search. As data source, we used the United Network for Organ Sharing (UNOS) registry, where we extracted adult patients (>17 years) from January 1997 to December 2008. As methods to predict survival, we used the Index for Mortality Prediction After Cardiac Transplantation (IMPACT) and the International Heart Transplant Survival Algorithm (IHTSA). We used the LIBLINEAR library together with the Apache Spark cluster computing framework to carry out the computation and we found feature sets for 1, 5, and 10 year survival for which we obtained area under the ROC curves (AUROC) of 68%, 68%, and 76%, respectively.

  17. A Relation Extraction Framework for Biomedical Text Using Hybrid Feature Set

    Directory of Open Access Journals (Sweden)

    Abdul Wahab Muzaffar

    2015-01-01

    Full Text Available The information extraction from unstructured text segments is a complex task. Although manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually because of the exponential increase in data size. Thus, there is a need for automatic tools and techniques for information extraction in biomedical text mining. Relation extraction is a significant area under biomedical information extraction that has gained much importance in the last two decades. A lot of work has been done on biomedical relation extraction focusing on rule-based and machine learning techniques. In the last decade, the focus has changed to hybrid approaches showing better results. This research presents a hybrid feature set for classification of relations between biomedical entities. The main contribution of this research is done in the semantic feature set where verb phrases are ranked using Unified Medical Language System (UMLS and a ranking algorithm. Support Vector Machine and Naïve Bayes, the two effective machine learning techniques, are used to classify these relations. Our approach has been validated on the standard biomedical text corpus obtained from MEDLINE 2001. Conclusively, it can be articulated that our framework outperforms all state-of-the-art approaches used for relation extraction on the same corpus.

  18. Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI.

    Science.gov (United States)

    Qin, Yuan-Yuan; Hsu, Johnny T; Yoshida, Shoko; Faria, Andreia V; Oishi, Kumiko; Unschuld, Paul G; Redgrave, Graham W; Ying, Sarah H; Ross, Christopher A; van Zijl, Peter C M; Hillis, Argye E; Albert, Marilyn S; Lyketsos, Constantine G; Miller, Michael I; Mori, Susumu; Oishi, Kenichi

    2013-01-01

    We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

  19. Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liu

    2014-01-01

    Full Text Available In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL algorithm and support vector machine (SVM. We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.

  20. Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease.

    Science.gov (United States)

    Yuvaraj, R; Murugappan, M; Ibrahim, Norlinah Mohamed; Sundaraj, Kenneth; Omar, Mohd Iqbal; Mohamad, Khairiyah; Palaniappan, R

    2014-12-01

    In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders. Copyright © 2014 Elsevier B.V. All rights reserved.

  1. Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

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    Youjun Li

    2017-10-01

    Full Text Available The aim of this study is to recognize human emotions by electroencephalographic (EEG signals. The innovation of our research methods involves two aspects: First, we integrate the spatial characteristics, frequency domain, and temporal characteristics of the EEG signals, and map them to a two-dimensional image. With these images, we build a series of EEG Multidimensional Feature Image (EEG MFI sequences to represent the emotion variation with EEG signals. Second, we construct a hybrid deep neural network to deal with the EEG MFI sequences to recognize human emotional states where the hybrid deep neural network combined the Convolution Neural Networks (CNN and Long Short-Term-Memory (LSTM Recurrent Neural Networks (RNN. Empirical research is carried out with the open-source dataset DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals using our method, and the results demonstrate the significant improvements over current state-of-the-art approaches in this field. The average emotion classification accuracy of each subject with CLRNN (the hybrid neural networks that we proposed in this study is 75.21%.

  2. Moment Invariant Features Extraction for Hand Gesture Recognition of Sign Language based on SIBI

    Directory of Open Access Journals (Sweden)

    Angga Rahagiyanto

    2017-07-01

    Full Text Available Myo Armband became an immersive technology to help deaf people for communication each other. The problem on Myo sensor is unstable clock rate. It causes the different length data for the same period even on the same gesture. This research proposes Moment Invariant Method to extract the feature of sensor data from Myo. This method reduces the amount of data and makes the same length of data. This research is user-dependent, according to the characteristics of Myo Armband. The testing process was performed by using alphabet A to Z on SIBI, Indonesian Sign Language, with static and dynamic finger movements. There are 26 class of alphabets and 10 variants in each class. We use min-max normalization for guarantying the range of data. We use K-Nearest Neighbor method to classify dataset. Performance analysis with leave-one-out-validation method produced an accuracy of 82.31%. It requires a more advanced method of classification to improve the performance on the detection results.

  3. Development of Open-Set Word Recognition in Children: Speech-Shaped Noise and Two-Talker Speech Maskers.

    Science.gov (United States)

    Corbin, Nicole E; Bonino, Angela Yarnell; Buss, Emily; Leibold, Lori J

    2016-01-01

    The goal of this study was to establish the developmental trajectories for children's open-set recognition of monosyllabic words in each of two maskers: two-talker speech and speech-shaped noise. Listeners were 56 children (5 to 16 years) and 16 adults, all with normal hearing. Thresholds for 50% correct recognition of monosyllabic words were measured in a two-talker speech or a speech-shaped noise masker in the sound field using an open-set task. Target words were presented at a fixed level of 65 dB SPL throughout testing, while the masker level was adapted. A repeated-measures design was used to compare the performance of three age groups of children (5 to 7 years, 8 to 12 years, and 13 to 16 years) and a group of adults. The pattern of age-related changes during childhood was also compared between the two masker conditions. Listeners in all four age groups performed more poorly in the two-talker speech than the speech-shaped noise masker, but the developmental trajectories differed for the two masker conditions. For the speech-shaped noise masker, children's performance improved with age until about 10 years of age, with little systematic child-adult differences thereafter. In contrast, for the two-talker speech masker, children's thresholds gradually improved between 5 and 13 years of age, followed by an abrupt improvement in performance to adult-like levels. Children's thresholds in the two masker conditions were uncorrelated. Younger children require a more advantageous signal-to-noise ratio than older children and adults to achieve 50% correct word recognition in both masker conditions. However, children's ability to recognize words appears to take longer to mature and follows a different developmental trajectory for the two-talker speech masker than the speech-shaped noise masker. These findings highlight the importance of considering both age and masker type when evaluating children's masked speech perception abilities.

  4. Spliced leader-based metatranscriptomic analyses lead to recognition of hidden genomic features in dinoflagellates.

    Science.gov (United States)

    Lin, Senjie; Zhang, Huan; Zhuang, Yunyun; Tran, Bao; Gill, John

    2010-11-16

    Environmental transcriptomics (metatranscriptomics) for a specific lineage of eukaryotic microbes (e.g., Dinoflagellata) would be instrumental for unraveling the genetic mechanisms by which these microbes respond to the natural environment, but it has not been exploited because of technical difficulties. Using the recently discovered dinoflagellate mRNA-specific spliced leader as a selective primer, we constructed cDNA libraries (e-cDNAs) from one marine and two freshwater plankton assemblages. Small-scale sequencing of the e-cDNAs revealed functionally diverse transcriptomes proven to be of dinoflagellate origin. A set of dinoflagellate common genes and transcripts of dominant dinoflagellate species were identified. Further analyses of the dataset prompted us to delve into the existing, largely unannotated dinoflagellate EST datasets (DinoEST). Consequently, all four nucleosome core histones, two histone modification proteins, and a nucleosome assembly protein were detected, clearly indicating the presence of nucleosome-like machinery long thought not to exist in dinoflagellates. The isolation of rhodopsin from taxonomically and ecotypically diverse dinoflagellates and its structural similarity and phylogenetic affinity to xanthorhodopsin suggest a common genetic potential in dinoflagellates to use solar energy nonphotosynthetically. Furthermore, we found 55 cytoplasmic ribosomal proteins (RPs) from the e-cDNAs and 24 more from DinoEST, showing that the dinoflagellate phylum possesses all 79 eukaryotic RPs. Our results suggest that a sophisticated eukaryotic molecular machine operates in dinoflagellates that likely encodes many more unsuspected physiological capabilities and, meanwhile, demonstrate that unique spliced leaders are useful for profiling lineage-specific microbial transcriptomes in situ.

  5. Pattern recognition

    CERN Document Server

    Theodoridis, Sergios

    2003-01-01

    Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to ""learn"" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10

  6. Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body.

    Science.gov (United States)

    Nguyen, Dat Tien; Park, Kang Ryoung

    2016-07-21

    With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images.

  7. Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body

    Science.gov (United States)

    Nguyen, Dat Tien; Park, Kang Ryoung

    2016-01-01

    With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images. PMID:27455264

  8. 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set.

    Science.gov (United States)

    Popuri, Karteek; Cobzas, Dana; Murtha, Albert; Jägersand, Martin

    2012-07-01

    Brain tumor segmentation is a required step before any radiation treatment or surgery. When performed manually, segmentation is time consuming and prone to human errors. Therefore, there have been significant efforts to automate the process. But, automatic tumor segmentation from MRI data is a particularly challenging task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. In our work, we propose an automatic brain tumor segmentation method that addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multidimensional feature set. Then, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this work is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned region statistics in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters from the normal brain region to be in the tumor region. This leads to a better disambiguation of the tumor from brain tissue. We evaluated the performance of our automatic segmentation method on 15 real MRI scans of brain tumor patients, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Validation with the expert segmentation labels yielded encouraging results: Jaccard (58%), Precision (81%), Recall (67%), Hausdorff distance (24 mm). Using priors on the brain/tumor appearance, our proposed automatic 3D variational

  9. The Use of Fuzzy Set Classification for Pattern Recognition of the Polygraph

    Science.gov (United States)

    1993-12-01

    and David C. Raskin, "Human versus computerized evaluations of polygraph data in a laboratory setting, "Journal of Applied Psycology , Vol.73, 1988 No2...of Applied Psycology , Vol.73, 1988 No 2, pp. 291-308 [3] John E. Reid and Fred E. Inbau, Truth and Deception: The Polygraph ( Lie Detector

  10. Spline curve matching with sparse knot sets: applications to deformable shape detection and recognition

    Science.gov (United States)

    Sang-Mook Lee; A. Lynn. Abbott; Neil A. Clark; Philip A. Araman

    2003-01-01

    Splines can be used to approximate noisy data with a few control points. This paper presents a new curve matching method for deformable shapes using two-dimensional splines. In contrast to the residual error criterion, which is based on relative locations of corresponding knot points such that is reliable primarily for dense point sets, we use deformation energy of...

  11. Extracting time-frequency feature of single-channel vastus medialis EMG signals for knee exercise pattern recognition.

    Science.gov (United States)

    Zhang, Yi; Li, Peiyang; Zhu, Xuyang; Su, Steven W; Guo, Qing; Xu, Peng; Yao, Dezhong

    2017-01-01

    The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited. In this study, three types of knee exercises, i.e., flexion of the leg up (standing), hip extension from a sitting position (sitting) and gait (walking) are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded. Four types of lower-limb motions including standing, sitting, stance phase of walking, and swing phase of walking, are segmented. The Wavelet Transform (WT) based Singular Value Decomposition (SVD) approach is proposed for the classification of four lower-limb motions using a single-channel EMG signal from the muscle group of vastus medialis. Based on lower-limb motions from all subjects, the combination of five-level wavelet decomposition and SVD is used to comprise the feature vector. The Support Vector Machine (SVM) is then configured to build a multiple-subject classifier for which the subject independent accuracy will be given across all subjects for the classification of four types of lower-limb motions. In order to effectively indicate the classification performance, EMG features from time-domain (e.g., Mean Absolute Value (MAV), Root-Mean-Square (RMS), integrated EMG (iEMG), Zero Crossing (ZC)) and frequency-domain (e.g., Mean Frequency (MNF) and Median Frequency (MDF)) are also used to classify lower-limb motions. The five-fold cross validation is performed and it repeats fifty times in order to acquire the robust subject independent accuracy. Results show that the proposed WT-based SVD approach has the classification accuracy of 91.85%±0.88% which

  12. Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm

    Directory of Open Access Journals (Sweden)

    Simon Fong

    2015-01-01

    Full Text Available Human motion sensing technology gains tremendous popularity nowadays with practical applications such as video surveillance for security, hand signing, and smart-home and gaming. These applications capture human motions in real-time from video sensors, the data patterns are nonstationary and ever changing. While the hardware technology of such motion sensing devices as well as their data collection process become relatively mature, the computational challenge lies in the real-time analysis of these live feeds. In this paper we argue that traditional data mining methods run short of accurately analyzing the human activity patterns from the sensor data stream. The shortcoming is due to the algorithmic design which is not adaptive to the dynamic changes in the dynamic gesture motions. The successor of these algorithms which is known as data stream mining is evaluated versus traditional data mining, through a case of gesture recognition over motion data by using Microsoft Kinect sensors. Three different subjects were asked to read three comic strips and to tell the stories in front of the sensor. The data stream contains coordinates of articulation points and various positions of the parts of the human body corresponding to the actions that the user performs. In particular, a novel technique of feature selection using swarm search and accelerated PSO is proposed for enabling fast preprocessing for inducing an improved classification model in real-time. Superior result is shown in the experiment that runs on this empirical data stream. The contribution of this paper is on a comparative study between using traditional and data stream mining algorithms and incorporation of the novel improved feature selection technique with a scenario where different gesture patterns are to be recognized from streaming sensor data.

  13. Lip-reading aids word recognition most in moderate noise: a Bayesian explanation using high-dimensional feature space.

    Directory of Open Access Journals (Sweden)

    Wei Ji Ma

    Full Text Available Watching a speaker's facial movements can dramatically enhance our ability to comprehend words, especially in noisy environments. From a general doctrine of combining information from different sensory modalities (the principle of inverse effectiveness, one would expect that the visual signals would be most effective at the highest levels of auditory noise. In contrast, we find, in accord with a recent paper, that visual information improves performance more at intermediate levels of auditory noise than at the highest levels, and we show that a novel visual stimulus containing only temporal information does the same. We present a Bayesian model of optimal cue integration that can explain these conflicts. In this model, words are regarded as points in a multidimensional space and word recognition is a probabilistic inference process. When the dimensionality of the feature space is low, the Bayesian model predicts inverse effectiveness; when the dimensionality is high, the enhancement is maximal at intermediate auditory noise levels. When the auditory and visual stimuli differ slightly in high noise, the model makes a counterintuitive prediction: as sound quality increases, the proportion of reported words corresponding to the visual stimulus should first increase and then decrease. We confirm this prediction in a behavioral experiment. We conclude that auditory-visual speech perception obeys the same notion of optimality previously observed only for simple multisensory stimuli.

  14. ClusTrack: feature extraction and similarity measures for clustering of genome-wide data sets.

    Directory of Open Access Journals (Sweden)

    Halfdan Rydbeck

    Full Text Available Clustering is a popular technique for explorative analysis of data, as it can reveal subgroupings and similarities between data in an unsupervised manner. While clustering is routinely applied to gene expression data, there is a lack of appropriate general methodology for clustering of sequence-level genomic and epigenomic data, e.g. ChIP-based data. We here introduce a general methodology for clustering data sets of coordinates relative to a genome assembly, i.e. genomic tracks. By defining appropriate feature extraction approaches and similarity measures, we allow biologically meaningful clustering to be performed for genomic tracks using standard clustering algorithms. An implementation of the methodology is provided through a tool, ClusTrack, which allows fine-tuned clustering analyses to be specified through a web-based interface. We apply our methods to the clustering of occupancy of the H3K4me1 histone modification in samples from a range of different cell types. The majority of samples form meaningful subclusters, confirming that the definitions of features and similarity capture biological, rather than technical, variation between the genomic tracks. Input data and results are available, and can be reproduced, through a Galaxy Pages document at http://hyperbrowser.uio.no/hb/u/hb-superuser/p/clustrack. The clustering functionality is available as a Galaxy tool, under the menu option "Specialized analyzis of tracks", and the submenu option "Cluster tracks based on genome level similarity", at the Genomic HyperBrowser server: http://hyperbrowser.uio.no/hb/.

  15. Comparing features sets for content-based image retrieval in a medical-case database

    Science.gov (United States)

    Muller, Henning; Rosset, Antoine; Vallee, Jean-Paul; Geissbuhler, Antoine

    2004-04-01

    Content-based image retrieval systems (CBIRSs) have frequently been proposed for the use in medical image databases and PACS. Still, only few systems were developed and used in a real clinical environment. It rather seems that medical professionals define their needs and computer scientists develop systems based on data sets they receive with little or no interaction between the two groups. A first study on the diagnostic use of medical image retrieval also shows an improvement in diagnostics when using CBIRSs which underlines the potential importance of this technique. This article explains the use of an open source image retrieval system (GIFT - GNU Image Finding Tool) for the retrieval of medical images in the medical case database system CasImage that is used in daily, clinical routine in the university hospitals of Geneva. Although the base system of GIFT shows an unsatisfactory performance, already little changes in the feature space show to significantly improve the retrieval results. The performance of variations in feature space with respect to color (gray level) quantizations and changes in texture analysis (Gabor filters) is compared. Whereas stock photography relies mainly on colors for retrieval, medical images need a large number of gray levels for successful retrieval, especially when executing feedback queries. The results also show that a too fine granularity in the gray levels lowers the retrieval quality, especially with single-image queries. For the evaluation of the retrieval peformance, a subset of the entire case database of more than 40,000 images is taken with a total of 3752 images. Ground truth was generated by a user who defined the expected query result of a perfect system by selecting images relevant to a given query image. The results show that a smaller number of gray levels (32 - 64) leads to a better retrieval performance, especially when using relevance feedback. The use of more scales and directions for the Gabor filters in the

  16. Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification

    Science.gov (United States)

    Balaguer, A.; Ruiz, L. A.; Hermosilla, T.; Recio, J. A.

    2010-02-01

    In this paper, a comprehensive set of texture features extracted from the experimental semivariogram of specific image objects is proposed and described, and their usefulness for land use classification of high resolution images is evaluated. Fourteen features are defined and categorized into three different groups, according to the location of their respective parameters in the semivariogram curve: (i) features that use parameters close to the origin of the semivariogram, (ii) the parameters employed extend to the first maximum, and (iii) the parameters employed are extracted from the first to the second maximum. A selection of the most relevant features has been performed, combining the analysis and interpretation of redundancies, and using statistical discriminant analysis methods. The suitability of the proposed features for object-based image classification has been evaluated using digital aerial images from an agricultural area on the Mediterranean coast of Spain. The performance of the selected semivariogram features has been compared with two different sets of texture features: those derived from the grey level co-occurrence matrix, and the values of raw semivariance directly extracted from the semivariogram at different positions. As a result of the tests, the classification accuracies obtained using the proposed semivariogram features are, in general, higher and more balanced than those obtained using the other two sets of standard texture features.

  17. A New User Dependent Iris Recognition System Based on an Area Preserving Pointwise Level Set Segmentation Approach

    Directory of Open Access Journals (Sweden)

    Nakissa Barzegar

    2009-01-01

    Full Text Available This paper presents a new user dependent approach in iris recognition systems. In the proposed method, consistent bits of iris code are calculated, based on the user specifications, using the user's mask. Another contribution of our work is in the iris segmentation phase, where a new pointwise level set approach with area preserving has been used for determining inner and outer iris boundaries, both exclusively performed in one step. Thanks to the special properties of this segmentation technique, there is no constraint about angles of head tilt. Furthermore, we showed that this algorithm is robust in noisy situations and can locate irises which are partly occluded by eyelid and eyelashes. Experimental results, on three renowned iris databases (CASIAIrisV3, Bath, and Ubiris, show that our method outperforms some of the existing methods, both in terms of accuracy and response time.

  18. A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

    Directory of Open Access Journals (Sweden)

    Ahmad Jalal

    2017-08-01

    Full Text Available Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition.

  19. Development of Open-Set Word Recognition in Children: Speech-Shaped Noise and Two-Talker Speech Maskers

    Science.gov (United States)

    Corbin, Nicole E.; Bonino, Angela Yarnell; Buss, Emily; Leibold, Lori J.

    2015-01-01

    Objective The goal of this study was to establish the developmental trajectories for children’s open-set recognition of monosyllabic words in each of two maskers: two-talker speech and speech-shaped noise. Design Listeners were 56 children (5 to 16 yrs) and 16 adults, all with normal hearing. Thresholds for 50% correct recognition of monosyllabic words were measured in a two-talker speech or a speech-shaped noise masker in the sound field using an open-set task. Target words were presented at a fixed level of 65 dB SPL throughout testing, while the masker level was adapted. A repeated-measures design was used to compare the performance of three age groups of children (5 to 7 yrs, 8 to 12 yrs, and 13 to 16 yrs) and a group of adults. The pattern of age-related changes during childhood was also compared between the two masker conditions. Results Listeners in all four age groups performed more poorly in the two-talker speech than the speech-shaped noise masker, but the developmental trajectories differed for the two masker conditions. For the speech-shaped noise masker, children’s performance improved with age until about 10 years of age, with little systematic child-adult differences thereafter. In contrast, for the two-talker speech masker, children’s thresholds gradually improved between 5 and 13 years of age, followed by an abrupt improvement in performance to adult-like levels. Children’s thresholds in the two masker conditions were uncorrelated. Conclusions Younger children require a more advantageous signal-to-noise ratio than older children and adults to achieve 50% correct word recognition in both masker conditions. However, children’s ability to recognize words appears to take longer to mature and follows a different developmental trajectory for the two-talker speech masker than the speech-shaped noise masker. These findings highlight the importance of considering both age and masker type when evaluating children’s masked speech perception

  20. The Recognition without Cued Recall Phenomenon: Support for a Feature-Matching Theory over a Partial Recollection Account

    Science.gov (United States)

    Ryals, Anthony J.; Cleary, Anne M.

    2012-01-01

    Among cues that fail to elicit successful recall, participants can still discriminate between cues that do and do not resemble studied items. This ability is referred to as recognition without cued recall (RWCR). We hypothesized that whereas recognition with cued recall is at least partly based on recalled studied information, RWCR results from a…

  1. Quantitative Analysis of the Association Angle between T-cell Receptor Vα/Vβ Domains Reveals Important Features for Epitope Recognition.

    Directory of Open Access Journals (Sweden)

    Thomas Hoffmann

    2015-07-01

    Full Text Available T-cell receptors (TCR play an important role in the adaptive immune system as they recognize pathogen- or cancer-based epitopes and thus initiate the cell-mediated immune response. Therefore there exists a growing interest in the optimization of TCRs for medical purposes like adoptive T-cell therapy. However, the molecular mechanisms behind T-cell signaling are still predominantly unknown. For small sets of TCRs it was observed that the angle between their Vα- and Vβ-domains, which bind the epitope, can vary and might be important for epitope recognition. Here we present a comprehensive, quantitative study of the variation in the Vα/Vβ interdomain-angle and its influence on epitope recognition, performing a systematic bioinformatics analysis based on a representative set of experimental TCR structures. For this purpose we developed a new, cuboid-based superpositioning method, which allows a unique, quantitative analysis of the Vα/Vβ-angles. Angle-based clustering led to six significantly different clusters. Analysis of these clusters revealed the unexpected result that the angle is predominantly influenced by the TCR-clonotype, whereas the bound epitope has only a minor influence. Furthermore we could identify a previously unknown center of rotation (CoR, which is shared by all TCRs. All TCR geometries can be obtained by rotation around this center, rendering it a new, common TCR feature with the potential of improving the accuracy of TCR structure prediction considerably. The importance of Vα/Vβ rotation for signaling was confirmed as we observed larger variances in the Vα/Vβ-angles in unbound TCRs compared to epitope-bound TCRs. Our results strongly support a two-step mechanism for TCR-epitope: First, preformation of a flexible TCR geometry in the unbound state and second, locking of the Vα/Vβ-angle in a TCR-type specific geometry upon epitope-MHC association, the latter being driven by rotation around the unique center of rotation.

  2. Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Joonwhoan Lee

    2013-06-01

    Full Text Available Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+ facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.

  3. Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

    Science.gov (United States)

    Kroll, Christine; von der Werth, Monika; Leuck, Holger; Stahl, Christoph; Schertler, Klaus

    2017-05-01

    For Intelligence, Surveillance, Reconnaissance (ISR) missions of manned and unmanned air systems typical electrooptical payloads provide high-definition video data which has to be exploited with respect to relevant ground targets in real-time by automatic/assisted target recognition software. Airbus Defence and Space is developing required technologies for real-time sensor exploitation since years and has combined the latest advances of Deep Convolutional Neural Networks (CNN) with a proprietary high-speed Support Vector Machine (SVM) learning method into a powerful object recognition system with impressive results on relevant high-definition video scenes compared to conventional target recognition approaches. This paper describes the principal requirements for real-time target recognition in high-definition video for ISR missions and the Airbus approach of combining an invariant feature extraction using pre-trained CNNs and the high-speed training and classification ability of a novel frequency-domain SVM training method. The frequency-domain approach allows for a highly optimized implementation for General Purpose Computation on a Graphics Processing Unit (GPGPU) and also an efficient training of large training samples. The selected CNN which is pre-trained only once on domain-extrinsic data reveals a highly invariant feature extraction. This allows for a significantly reduced adaptation and training of the target recognition method for new target classes and mission scenarios. A comprehensive training and test dataset was defined and prepared using relevant high-definition airborne video sequences. The assessment concept is explained and performance results are given using the established precision-recall diagrams, average precision and runtime figures on representative test data. A comparison to legacy target recognition approaches shows the impressive performance increase by the proposed CNN+SVM machine-learning approach and the capability of real-time high

  4. Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting.

    Science.gov (United States)

    O'Brien, Megan K; Shawen, Nicholas; Mummidisetty, Chaithanya K; Kaur, Saninder; Bo, Xiao; Poellabauer, Christian; Kording, Konrad; Jayaraman, Arun

    2017-05-25

    Smartphones contain sensors that measure movement-related data, making them promising tools for monitoring physical activity after a stroke. Activity recognition (AR) systems are typically trained on movement data from healthy individuals collected in a laboratory setting. However, movement patterns change after a stroke (eg, gait impairment), and activities may be performed differently at home than in a lab. Thus, it is important to validate AR for gait-impaired stroke patients in a home setting for accurate clinical predictions. In this study, we sought to evaluate AR performance in a home setting for individuals who had suffered a stroke, by using different sets of training activities. Specifically, we compared AR performance for persons with stroke while varying the origin of training data, based on either population (healthy persons or persons with stoke) or environment (laboratory or home setting). Thirty individuals with stroke and fifteen healthy subjects performed a series of mobility-related activities, either in a laboratory or at home, while wearing a smartphone. A custom-built app collected signals from the phone's accelerometer, gyroscope, and barometer sensors, and subjects self-labeled the mobility activities. We trained a random forest AR model using either healthy or stroke activity data. Primary measures of AR performance were (1) the mean recall of activities and (2) the misclassification of stationary and ambulatory activities. A classifier trained on stroke activity data performed better than one trained on healthy activity data, improving average recall from 53% to 75%. The healthy-trained classifier performance declined with gait impairment severity, more often misclassifying ambulatory activities as stationary ones. The classifier trained on in-lab activities had a lower average recall for at-home activities (56%) than for in-lab activities collected on a different day (77%). Stroke-based training data is needed for high quality AR among

  5. Epidemiology, recognition and documentation of sepsis in the pre-hospital setting and associated clinical outcomes: a prospective multicenter study

    NARCIS (Netherlands)

    Alam, Nadia; Doerga, Kirtiedevi B. N. S; Hussain, Tahira; Hussain, Sadia; Holleman, Frits; Kramer, Mark H. H.; Nanayakkara, Prabath W. B.

    2016-01-01

    General practitioners (GPs) and the emergency medical services (EMS) personnel have a pivotal role as points of entry into the acute care chain. This study was conducted to investigate the recognition of sepsis by GPs and EMS personnel and to evaluate the associations between recognition of sepsis

  6. Homogeneity Analysis with "k" Sets of Variables: An Alternating Least Squares Method with Optimal Scaling Features.

    Science.gov (United States)

    van der Burg, Eeke; de Leeuw, Jan

    1988-01-01

    Homogeneity analysis (multiple correspondence analysis), which is usually applied to "k" separate variables, was applied to sets of variables by using sums within sets. The resulting technique, OVERALS, uses optimal scaling. The corresponding OVERALS computer program minimizes a least squares loss function via an alternating least…

  7. Features of Recently Transmitted HIV-1 Clade C Viruses that Impact Antibody Recognition: Implications for Active and Passive Immunization.

    Science.gov (United States)

    Rademeyer, Cecilia; Korber, Bette; Seaman, Michael S; Giorgi, Elena E; Thebus, Ruwayhida; Robles, Alexander; Sheward, Daniel J; Wagh, Kshitij; Garrity, Jetta; Carey, Brittany R; Gao, Hongmei; Greene, Kelli M; Tang, Haili; Bandawe, Gama P; Marais, Jinny C; Diphoko, Thabo E; Hraber, Peter; Tumba, Nancy; Moore, Penny L; Gray, Glenda E; Kublin, James; McElrath, M Juliana; Vermeulen, Marion; Middelkoop, Keren; Bekker, Linda-Gail; Hoelscher, Michael; Maboko, Leonard; Makhema, Joseph; Robb, Merlin L; Abdool Karim, Salim; Abdool Karim, Quarraisha; Kim, Jerome H; Hahn, Beatrice H; Gao, Feng; Swanstrom, Ronald; Morris, Lynn; Montefiori, David C; Williamson, Carolyn

    2016-07-01

    The development of biomedical interventions to reduce acquisition of HIV-1 infection remains a global priority, however their potential effectiveness is challenged by very high HIV-1 envelope diversity. Two large prophylactic trials in high incidence, clade C epidemic regions in southern Africa are imminent; passive administration of the monoclonal antibody VRC01, and active immunization with a clade C modified RV144-like vaccines. We have created a large representative panel of C clade viruses to enable assessment of antibody responses to vaccines and natural infection in Southern Africa, and we investigated the genotypic and neutralization properties of recently transmitted clade C viruses to determine how viral diversity impacted antibody recognition. We further explore the implications of these findings for the potential effectiveness of these trials. A panel of 200 HIV-1 Envelope pseudoviruses was constructed from clade C viruses collected within the first 100 days following infection. Viruses collected pre-seroconversion were significantly more resistant to serum neutralization compared to post-seroconversion viruses (p = 0.001). Over 13 years of the study as the epidemic matured, HIV-1 diversified (p = 0.0009) and became more neutralization resistant to monoclonal antibodies VRC01, PG9 and 4E10. When tested at therapeutic levels (10ug/ml), VRC01 only neutralized 80% of viruses in the panel, although it did exhibit potent neutralization activity against sensitive viruses (IC50 titres of 0.42 μg/ml). The Gp120 amino acid similarity between the clade C panel and candidate C-clade vaccine protein boosts (Ce1086 and TV1) was 77%, which is 8% more distant than between CRF01_AE viruses and the RV144 CRF01_AE immunogen. Furthermore, two vaccine signature sites, K169 in V2 and I307 in V3, associated with reduced infection risk in RV144, occurred less frequently in clade C panel viruses than in CRF01_AE viruses from Thailand. Increased resistance of pre

  8. Features of Recently Transmitted HIV-1 Clade C Viruses that Impact Antibody Recognition: Implications for Active and Passive Immunization.

    Directory of Open Access Journals (Sweden)

    Cecilia Rademeyer

    2016-07-01

    Full Text Available The development of biomedical interventions to reduce acquisition of HIV-1 infection remains a global priority, however their potential effectiveness is challenged by very high HIV-1 envelope diversity. Two large prophylactic trials in high incidence, clade C epidemic regions in southern Africa are imminent; passive administration of the monoclonal antibody VRC01, and active immunization with a clade C modified RV144-like vaccines. We have created a large representative panel of C clade viruses to enable assessment of antibody responses to vaccines and natural infection in Southern Africa, and we investigated the genotypic and neutralization properties of recently transmitted clade C viruses to determine how viral diversity impacted antibody recognition. We further explore the implications of these findings for the potential effectiveness of these trials. A panel of 200 HIV-1 Envelope pseudoviruses was constructed from clade C viruses collected within the first 100 days following infection. Viruses collected pre-seroconversion were significantly more resistant to serum neutralization compared to post-seroconversion viruses (p = 0.001. Over 13 years of the study as the epidemic matured, HIV-1 diversified (p = 0.0009 and became more neutralization resistant to monoclonal antibodies VRC01, PG9 and 4E10. When tested at therapeutic levels (10ug/ml, VRC01 only neutralized 80% of viruses in the panel, although it did exhibit potent neutralization activity against sensitive viruses (IC50 titres of 0.42 μg/ml. The Gp120 amino acid similarity between the clade C panel and candidate C-clade vaccine protein boosts (Ce1086 and TV1 was 77%, which is 8% more distant than between CRF01_AE viruses and the RV144 CRF01_AE immunogen. Furthermore, two vaccine signature sites, K169 in V2 and I307 in V3, associated with reduced infection risk in RV144, occurred less frequently in clade C panel viruses than in CRF01_AE viruses from Thailand. Increased resistance of

  9. Treatment Integrity of Elaborated Semantic Feature Analysis Aphasia Therapy Delivered in Individual and Group Settings

    Science.gov (United States)

    Kladouchou, Vasiliki; Papathanasiou, Ilias; Efstratiadou, Eva A.; Christaki, Vasiliki; Hilari, Katerina

    2017-01-01

    Background & Aims: This study ran within the framework of the Thales Aphasia Project that investigated the efficacy of elaborated semantic feature analysis (ESFA). We evaluated the treatment integrity (TI) of ESFA, i.e., the degree to which therapists implemented treatment as intended by the treatment protocol, in two different formats:…

  10. Cognitive and artificial representations in handwriting recognition

    Science.gov (United States)

    Lenaghan, Andrew P.; Malyan, Ron

    1996-03-01

    Both cognitive processes and artificial recognition systems may be characterized by the forms of representation they build and manipulate. This paper looks at how handwriting is represented in current recognition systems and the psychological evidence for its representation in the cognitive processes responsible for reading. Empirical psychological work on feature extraction in early visual processing is surveyed to show that a sound psychological basis for feature extraction exists and to describe the features this approach leads to. The first stage of the development of an architecture for a handwriting recognition system which has been strongly influenced by the psychological evidence for the cognitive processes and representations used in early visual processing, is reported. This architecture builds a number of parallel low level feature maps from raw data. These feature maps are thresholded and a region labeling algorithm is used to generate sets of features. Fuzzy logic is used to quantify the uncertainty in the presence of individual features.

  11. EMOTION RECOGNITION USING TEXTURE ANALYSIS

    Directory of Open Access Journals (Sweden)

    A. V. Zhabinski

    2014-01-01

    Full Text Available In the paper the role of texture (pixel values in a task of emotion recognition in the image of a face is explored. Texture-based method is compared to classic methods based on coordinates of key point. In addition, a new combined method is presented that unites both sets of features.

  12. Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain.

    Science.gov (United States)

    da Silveira, Thiago L T; Kozakevicius, Alice J; Rodrigues, Cesar R

    2017-02-01

    The main objective of this study was to enhance the performance of sleep stage classification using single-channel electroencephalograms (EEGs), which are highly desirable for many emerging technologies, such as telemedicine and home care. The proposed method consists of decomposing EEGs by a discrete wavelet transform and computing the kurtosis, skewness and variance of its coefficients at selected levels. A random forest predictor is trained to classify each epoch into one of the Rechtschaffen and Kales' stages. By performing a comprehensive set of tests on 106,376 epochs available from the Physionet public database, it is demonstrated that the use of these three statistical moments has enhanced performance when compared to their application in the time domain. Furthermore, the chosen set of features has the advantage of exhibiting a stable classification performance for all scoring systems, i.e., from 2- to 6-state sleep stages. The stability of the feature set is confirmed with ReliefF tests which show a performance reduction when any individual feature is removed, suggesting that this group of feature cannot be further reduced. The accuracies and kappa coefficients yield higher than 90 % and 0.8, respectively, for all of the 2- to 6-state sleep stage classification cases.

  13. Treatment integrity of elaborated semantic feature analysis aphasia therapydelivered in individual and group settings

    OpenAIRE

    Kladouchou, V.; Papathanasiou, I.; Efstratiadou, E. A.; Christaki, V.; Hilari, K.

    2017-01-01

    Background & Aims\\ud \\ud This study ran within the framework of the Thales Aphasia Project that investigated the efficacy of elaborated semantic feature analysis (ESFA). We evaluated the treatment integrity (TI) of ESFA, i.e., the degree to which therapists implemented treatment as intended by the treatment protocol, in two different formats: individual and group therapy.\\ud \\ud Methods & Procedures\\ud \\ud Based on the ESFA manual, observation of therapy videos and TI literature, we developed...

  14. Build a Robust Learning Feature Descriptor by Using a New Image Visualization Method for Indoor Scenario Recognition

    Directory of Open Access Journals (Sweden)

    Jichao Jiao

    2017-07-01

    Full Text Available In order to recognize indoor scenarios, we extract image features for detecting objects, however, computers can make some unexpected mistakes. After visualizing the histogram of oriented gradient (HOG features, we find that the world through the eyes of a computer is indeed different from human eyes, which assists researchers to see the reasons that cause a computer to make errors. Additionally, according to the visualization, we notice that the HOG features can obtain rich texture information. However, a large amount of background interference is also introduced. In order to enhance the robustness of the HOG feature, we propose an improved method for suppressing the background interference. On the basis of the original HOG feature, we introduce a principal component analysis (PCA to extract the principal components of the image colour information. Then, a new hybrid feature descriptor, which is named HOG–PCA (HOGP, is made by deeply fusing these two features. Finally, the HOGP is compared to the state-of-the-art HOG feature descriptor in four scenes under different illumination. In the simulation and experimental tests, the qualitative and quantitative assessments indicate that the visualizing images of the HOGP feature are close to the observation results obtained by human eyes, which is better than the original HOG feature for object detection. Furthermore, the runtime of our proposed algorithm is hardly increased in comparison to the classic HOG feature.

  15. Build a Robust Learning Feature Descriptor by Using a New Image Visualization Method for Indoor Scenario Recognition.

    Science.gov (United States)

    Jiao, Jichao; Wang, Xin; Deng, Zhongliang

    2017-07-04

    In order to recognize indoor scenarios, we extract image features for detecting objects, however, computers can make some unexpected mistakes. After visualizing the histogram of oriented gradient (HOG) features, we find that the world through the eyes of a computer is indeed different from human eyes, which assists researchers to see the reasons that cause a computer to make errors. Additionally, according to the visualization, we notice that the HOG features can obtain rich texture information. However, a large amount of background interference is also introduced. In order to enhance the robustness of the HOG feature, we propose an improved method for suppressing the background interference. On the basis of the original HOG feature, we introduce a principal component analysis (PCA) to extract the principal components of the image colour information. Then, a new hybrid feature descriptor, which is named HOG-PCA (HOGP), is made by deeply fusing these two features. Finally, the HOGP is compared to the state-of-the-art HOG feature descriptor in four scenes under different illumination. In the simulation and experimental tests, the qualitative and quantitative assessments indicate that the visualizing images of the HOGP feature are close to the observation results obtained by human eyes, which is better than the original HOG feature for object detection. Furthermore, the runtime of our proposed algorithm is hardly increased in comparison to the classic HOG feature.

  16. Research on Face Recognition Based on Embedded System

    Directory of Open Access Journals (Sweden)

    Hong Zhao

    2013-01-01

    Full Text Available Because a number of image feature data to store, complex calculation to execute during the face recognition, therefore the face recognition process was realized only by PCs with high performance. In this paper, the OpenCV facial Haar-like features were used to identify face region; the Principal Component Analysis (PCA was employed in quick extraction of face features and the Euclidean Distance was also adopted in face recognition; as thus, data amount and computational complexity would be reduced effectively in face recognition, and the face recognition could be carried out on embedded platform. Finally, based on Tiny6410 embedded platform, a set of embedded face recognition systems was constructed. The test results showed that the system has stable operation and high recognition rate can be used in portable and mobile identification and authentication.

  17. Credit scoring using ensemble of various classifiers on reduced feature set

    Directory of Open Access Journals (Sweden)

    Dahiya Shashi

    2015-01-01

    Full Text Available Credit scoring methods are widely used for evaluating loan applications in financial and banking institutions. Credit score identifies if applicant customers belong to good risk applicant group or a bad risk applicant group. These decisions are based on the demographic data of the customers, overall business by the customer with bank, and loan payment history of the loan applicants. The advantages of using credit scoring models include reducing the cost of credit analysis, enabling faster credit decisions and diminishing possible risk. Many statistical and machine learning techniques such as Logistic Regression, Support Vector Machines, Neural Networks and Decision tree algorithms have been used independently and as hybrid credit scoring models. This paper proposes an ensemble based technique combining seven individual models to increase the classification accuracy. Feature selection has also been used for selecting important attributes for classification. Cross classification was conducted using three data partitions. German credit dataset having 1000 instances and 21 attributes is used in the present study. The results of the experiments revealed that the ensemble model yielded a very good accuracy when compared to individual models. In all three different partitions, the ensemble model was able to classify more than 80% of the loan customers as good creditors correctly. Also, for 70:30 partition there was a good impact of feature selection on the accuracy of classifiers. The results were improved for almost all individual models including the ensemble model.

  18. Tech-Assisted Language Learning Tasks in an EFL Setting: Use of Hand phone Recording Feature

    Directory of Open Access Journals (Sweden)

    Alireza Shakarami

    2014-09-01

    Full Text Available Technology with its speedy great leaps forward has undeniable impact on every aspect of our life in the new millennium. It has supplied us with different affordances almost daily or more precisely in a matter of hours. Technology and Computer seems to be a break through as for their roles in the Twenty-First century educational system. Examples are numerous, among which CALL, CMC, and Virtual learning spaces come to mind instantly. Amongst the newly developed gadgets of today are the sophisticated smart Hand phones which are far more ahead of a communication tool once designed for. Development of Hand phone as a wide-spread multi-tasking gadget has urged researchers to investigate its effect on every aspect of learning process including language learning. This study attempts to explore the effects of using cell phone audio recording feature, by Iranian EFL learners, on the development of their speaking skills. Thirty-five sophomore students were enrolled in a pre-posttest designed study. Data on their English speaking experience using audio–recording features of their Hand phones were collected. At the end of the semester, the performance of both groups, treatment and control, were observed, evaluated, and analyzed; thereafter procured qualitatively at the next phase. The quantitative outcome lent support to integrating Hand phones as part of the language learning curriculum. Keywords:

  19. Exploratory Data Analysis of Acceleration Signals to Select Light-Weight and Accurate Features for Real-Time Activity Recognition on Smartphones

    Directory of Open Access Journals (Sweden)

    Seok-Won Lee

    2013-09-01

    Full Text Available Smartphone-based activity recognition (SP-AR recognizes users’ activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification is performed on the device. Most of these online systems use either a high sampling rate (SR or long data-window (DW to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR process, and an accurate AR-model in this case can be built using a low SR (20 Hz and a small DW (3 s. The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.

  20. On the transferability of rule sets for mapping cirques using Object-based feature extraction

    OpenAIRE

    Seijmonsbergen, A. C.; Anders, N.S.; Gabriner, R.; W. Bouten

    2014-01-01

    Cirques are complex landforms resulting from glacial erosion and occur in the mountains of western Austria at various topographic levels. After deglaciation they may potentially hold climate proxies, are showcases of vegetation regrowth and play an important role in the regulation of mountain hydrology. Our objective is to develop a workflow to test an object‐based rule‐set that decomposes LiDAR DEMs into the main cirque components: divide, cirque headwall, cirque floor and into the sub‐compo...

  1. LLE Score: A New Filter-Based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition.

    Science.gov (United States)

    Yao, Chao; Liu, Ya-Feng; Jiang, Bo; Han, Jungong; Han, Junwei

    2017-11-01

    The task of feature selection is to find the most representative features from the original high-dimensional data. Because of the absence of the information of class labels, selecting the appropriate features in unsupervised learning scenarios is much harder than that in supervised scenarios. In this paper, we investigate the potential of locally linear embedding (LLE), which is a popular manifold learning method, in feature selection task. It is straightforward to apply the idea of LLE to the graph-preserving feature selection framework. However, we find that this straightforward application suffers from some problems. For example, it fails when the elements in the feature are all equal; it does not enjoy the property of scaling invariance and cannot capture the change of the graph efficiently. To solve these problems, we propose a new filter-based feature selection method based on LLE in this paper, which is named as LLE score. The proposed criterion measures the difference between the local structure of each feature and that of the original data. Our experiments of classification task on two face image data sets, an object image data set, and a handwriting digits data set show that LLE score outperforms state-of-the-art methods, including data variance, Laplacian score, and sparsity score.

  2. Printed Persian Subword Recognition Using Wavelet Packet Descriptors

    Directory of Open Access Journals (Sweden)

    Samira Nasrollahi

    2013-01-01

    Full Text Available In this paper, we present a new approach to offline OCR (optical character recognition for printed Persian subwords using wavelet packet transform. The proposed algorithm is used to extract font invariant and size invariant features from 87804 subwords of 4 fonts and 3 sizes. The feature vectors are compressed using PCA. The obtained feature vectors yield a pictorial dictionary for which an entry is the mean of each group that consists of the same subword with 4 fonts in 3 sizes. The sets of these features are congregated by combining them with the dot features for the recognition of printed Persian subwords. To evaluate the feature extraction results, this algorithm was tested on a set of 2000 subwords in printed Persian text documents. An encouraging recognition rate of 97.9% is got at subword level recognition.

  3. Cariprazine, a dopamine D(3)-receptor-preferring partial agonist, blocks phencyclidine-induced impairments of working memory, attention set-shifting, and recognition memory in the mouse.

    Science.gov (United States)

    Zimnisky, Ross; Chang, Gloria; Gyertyán, István; Kiss, Béla; Adham, Nika; Schmauss, Claudia

    2013-03-01

    A major challenge in the pharmacological treatment of psychotic disorders is the effective management of the associated cognitive dysfunctions. Novel concepts emphasize a potential benefit of partial agonists acting upon dopamine D(2)-like receptors in ameliorating these cognitive deficits, and pre-clinical studies suggest that D(3)-receptor-preferring compounds can exert pro-cognitive effects. The objective of the study was to use acute phencyclidine (PCP) treatment to model the cognitive deficits of schizophrenia in mice, and to test the efficacy of the novel, dopamine D(3)-receptor-preferring drug cariprazine in ameliorating the severity of PCP-triggered cognitive deficits. One group of wild-type or D(3)-receptor knockout mice was acutely treated with either saline or phencyclidine (PCP, 1 mg/kg). A separate group of mice was treated with cariprazine prior to PCP administration. Both groups were then tested in three cognitive tasks: social interaction/recognition and recognition memory, spatial working memory, and attention-set-shifting. PCP effectively disrupted social recognition and social recognition memory, spatial working memory, and extradimensional attention set-shifting. Cariprazine pretreatment significantly attenuated the emergence of these cognitive deficits in PCP-treated wild-type mice, but not in PCP-treated D(3)-receptor knockout mice. In an animal model of PCP-induced cognitive impairment, cariprazine pretreatment significantly diminished PCP-triggered cognitive deficits, and studies on knockout mice show that dopamine D(3) receptors contribute to this effect.

  4. Comparative analysis of different process simulation settings of a micro injection molded part featuring conformal cooling

    DEFF Research Database (Denmark)

    Marhöfer, David Maximilian; Tosello, Guido; Islam, Aminul

    2015-01-01

    Process simulations are applied in all fields of engineering in order to support and optimize the design and quality of products and their manufacturing processes. Micro injection molding is not an exception in this regard. Simulations enable to investigate the process and the part quality....... In the reported work, process simulations using Autodesk Moldflow Insight 2015® are applied to a micro mechanical part to be fabricated by micro injection molding and with over-all dimensions of 12.0 × 3.0 × 0.8 mm³ and micro features (micro hole, diameter of 580 μm, and sharp radii down to 100 μm). Three...... of the implementation of the actual mold block, conventional cooling, and conformal cooling. In the comparison, characteristic quality criteria for injection molding are studied, such as the filling behavior of the cavity, the injection pressure, the temperature distribution, and the resulting part warpage...

  5. QSPR models for half-wave reduction potential of steroids: a comparative study between feature selection and feature extraction from subsets of or entire set of descriptors.

    Science.gov (United States)

    Hemmateenejad, Bahram; Yazdani, Mahdieh

    2009-02-16

    Steroids are widely distributed in nature and are found in plants, animals, and fungi in abundance. A data set consists of a diverse set of steroids have been used to develop quantitative structure-electrochemistry relationship (QSER) models for their half-wave reduction potential. Modeling was established by means of multiple linear regression (MLR) and principle component regression (PCR) analyses. In MLR analysis, the QSPR models were constructed by first grouping descriptors and then stepwise selection of variables from each group (MLR1) and stepwise selection of predictor variables from the pool of all calculated descriptors (MLR2). Similar procedure was used in PCR analysis so that the principal components (or features) were extracted from different group of descriptors (PCR1) and from entire set of descriptors (PCR2). The resulted models were evaluated using cross-validation, chance correlation, application to prediction reduction potential of some test samples and accessing applicability domain. Both MLR approaches represented accurate results however the QSPR model found by MLR1 was statistically more significant. PCR1 approach produced a model as accurate as MLR approaches whereas less accurate results were obtained by PCR2 approach. In overall, the correlation coefficients of cross-validation and prediction of the QSPR models resulted from MLR1, MLR2 and PCR1 approaches were higher than 90%, which show the high ability of the models to predict reduction potential of the studied steroids.

  6. Recognition of Paddy, Brown Rice and White Rice Cultivars Based on Textural Features of Images and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    I Golpour

    2015-03-01

    Full Text Available Identification of rice cultivars is very important in modern agriculture. Texture properties could be used to identify of rice cultivars among of the various factors. The digital images processing can be used as a new approach to extract texture features. The objective of this research was to identify rice cultivars using of texture features with using image processing and back propagation artificial neural networks. To identify rice cultivars, five rice cultivars Fajr, Shiroodi, Neda, Tarom mahalli and Khazar were selected. Finally, 108 textural features were extracted from rice images using gray level co-occurrence matrix. Then cultivar identification was carried out using Back Propagation Artificial Neural Network. After evaluation of the network with one hidden layer using texture features, the highest classification accuracy for paddy cultivars, brown rice and white rice were obtained 92.2%, 97.8% and 98.9%, respectively. After evaluation of the network with two hidden layers, the average accuracy for classification of paddy cultivars was obtained to be 96.67%, for brown rice it was 97.78% and for white rice the classification accuracy was 98.88%. The highest mean classification accuracy acquired for paddy cultivars with 45 features was achieved to be 98.9%, for brown rice cultivars with 11 selected features it was 93.3% and it was 96.7% with 18 selected features for rice cultivars.

  7. Key features of maltreatment of the infirm elderly in home settings.

    Science.gov (United States)

    Mendonca, J D; Velamoor, V R; Sauve, D

    1996-03-01

    To identify contributory factors of elder abuse by caregivers in home settings. Using a reliable instrument, visiting nurses rated observations symptomatic of abuse and neglect found in their current caseload of elderly patients. Their observations were also classified as related or unrelated to wilfull maltreatment. Regression analysis produced the following significant findings: 1. signs of poor physical care were found to be predictors of physical abuse; 2. signs of psychosocial distress and exploitation were identified as predictors of emotional abuse; 3. defensiveness and irritability shown by caregivers and strained family relationships, in general, were also associated with abuse. It appears that emotional abuse is more prevalent than, and not a necessary precursor of, physical abuse; however, reliable signs of impending or actual abuse of the elderly can be found in a home during visitation.

  8. Feature-specific event-related potential effects to action- and sound-related verbs during visual word recognition

    Directory of Open Access Journals (Sweden)

    Margot Popp

    2016-12-01

    Full Text Available Grounded cognition theories suggest that conceptual representations essentially depend on modality-specific sensory and motor systems. Feature-specific brain activation across different feature types such as action or audition has been intensively investigated in nouns, while feature-specific conceptual category differences in verbs mainly focused on body part specific effects. The present work aimed at assessing whether feature-specific event-related potential (ERP differences between action and sound concepts, as previously observed in nouns, can also be found within the word class of verbs. In Experiment 1, participants were visually presented with carefully matched sound and action verbs within a lexical decision task, which provides implicit access to word meaning and minimizes strategic access to semantic word features. Experiment 2 tested whether pre-activating the verb concept in a context phase, in which the verb is presented with a related context noun, modulates subsequent feature-specific action vs. sound verb processing within the lexical decision task. In Experiment 1, ERP analyses revealed a differential ERP polarity pattern for action and sound verbs at parietal and central electrodes similar to previous results in nouns. Pre-activation of the meaning of verbs in the preceding context phase in Experiment 2 resulted in a polarity-reversal of feature-specific ERP effects in the lexical decision task compared with Experiment 1. This parallels analogous earlier findings for primed action and sound related nouns. In line with grounded cognitions theories, our ERP study provides evidence for a differential processing of action and sound verbs similar to earlier observation for concrete nouns. Although the localizational value of ERPs must be viewed with caution, our results indicate that the meaning of verbs is linked to different neural circuits depending on conceptual feature relevance.

  9. EEG emotion recognition using reduced channel wavelet entropy and average wavelet coefficient features with normal Mutual Information method.

    Science.gov (United States)

    Candra, Henry; Yuwono, Mitchell; Chai, Rifai; Nguyen, Hung T; Su, Steven

    2017-07-01

    Recognizing emotion from EEG signals is a complicated task that requires complex features and a substantial number of EEG channels. Simple algorithms to analyse the feature and reduce the EEG channel number will give an indispensable advantages. Therefore, this study explores a combination of wavelet entropy and average wavelet coefficient (WEAVE) as a potential EEG-emotion feature to classify valence and arousal emotions with the advantage of the ability to identify the occurrence of a pattern while at the same time identify the shape of a pattern in EEG emotion signal. The complexity of the feature was reduced using the Normalized Mutual Information (NMI) method to obtain a reduced number of channels. Classification with the WEAVE feature achieved 76.8% accuracy for valence and 74.3% for arousal emotion, respectively. The analysis with NMI shows that the WEAVE feature has linear characteristics and offers possibilities to reduce the EEG channels to a certain number. Further analysis also reveals that detection of valence emotion with reduced EEG channels has a different combination of EEG channels compared to arousal emotion.

  10. Student recognition of visual affordances: Supporting use of physics simulations in whole class and small group settings

    Science.gov (United States)

    Stephens, A. Lynn

    The purpose of this study is to investigate student interactions with simulations, and teacher support of those interactions, within naturalistic high school physics classroom settings. This study focuses on data from two lesson sequences that were conducted in several physics classrooms. The lesson sequences were conducted in a whole class discussion format in approximately half of the class sections and in a hands-on-computer small group format in matched class sections. Analysis used a mixed methods approach where: (1) quantitative methods were used to evaluate pre-post data; (2) open coding and selective coding were used for transcript analysis; and (3) comparative case studies were used to consider the quantitative and qualitative data in light of each other and to suggested possible explanations. Although teachers expressed the expectation that the small group students would learn more, no evidence was found in pre-post analysis for an advantage for the small group sections. Instead, a slight trend was observed in favor of the whole class discussion sections, especially for students in the less advanced sections. In seeking to explain these results, qualitative analyses of transcript and videotape data were conducted, revealing that many more episodes of support for interpreting visual elements of the simulations occurred in the whole class setting than in the matched small group discussions; not only teachers, but, at times, students used more visual support moves in the whole class discussion setting. In addition, concepts that had been identified as key were discussed for longer periods of time in the whole class setting than in the matched small group discussions in six of nine matched sets. For one of the lesson sequences, analysis of student work on in-class activity sheets identified no evidence that any of the Honors or College Preparatory students in the small groups had made use in their thinking of the key features of the sophisticated and popular

  11. Part-of-Speech Enhanced Context Recognition

    DEFF Research Database (Denmark)

    Madsen, Rasmus Elsborg; Larsen, Jan; Hansen, Lars Kai

    2004-01-01

    Language independent `bag-of-words' representations are surprisingly efective for text classi¯cation. In this communi- cation our aim is to elucidate the synergy between language inde- pendent features and simple language model features. We consider term tag features estimated by a so-called part...... and a probabilistic neural network classi- fier. Three medium size data-sets are analyzed and we find consis- tent synergy between the term and natural language features in all three sets for a range of training set sizes. The most significant en- hancement is found for small text databases where high recognition...

  12. Using Computers for Assessment of Facial Features and Recognition of Anatomical Variants that Result in Unfavorable Rhinoplasty Outcomes

    Directory of Open Access Journals (Sweden)

    Tarik Ozkul

    2008-04-01

    Full Text Available Rhinoplasty and facial plastic surgery are among the most frequently performed surgical procedures in the world. Although the underlying anatomical features of nose and face are very well known, performing a successful facial surgery requires not only surgical skills but also aesthetical talent from surgeon. Sculpting facial features surgically in correct proportions to end up with an aesthetically pleasing result is highly difficult. To further complicate the matter, some patients may have some anatomical features which affect rhinoplasty operation outcome negatively. If goes undetected, these anatomical variants jeopardize the surgery causing unexpected rhinoplasty outcomes. In this study, a model is developed with the aid of artificial intelligence tools, which analyses facial features of the patient from photograph, and generates an index of "appropriateness" of the facial features and an index of existence of anatomical variants that effect rhinoplasty negatively. The software tool developed is intended to detect the variants and warn the surgeon before the surgery. Another purpose of the tool is to generate an objective score to assess the outcome of the surgery.

  13. Perceptual Confusions Among Consonants, Revisited: Cross-Spectral Integration of Phonetic-Feature Information and Consonant Recognition

    DEFF Research Database (Denmark)

    Christiansen, Thomas Ulrich; Greenberg, Steven

    2012-01-01

    -spectral summation. This difference is mirrored in a measure of error-pattern similarity across bands—Symmetric Redundancy. Consonants, as well as Voicing and Manner, share a moderate degree of redundancy between bands. In contrast, the cross-spectral redundancy associated with Place is close to zero, which means....... Adding a third band increases the IT by an amount somewhat less than predicted by linear cross-spectral integration (i.e., a compressive function). In contrast, for Place of Articulation, the IT gained through addition of a second or third slit is far more than predicted by linear, cross...... for why conventional cross-spectral integration speech models, such as the Articulation Index, Speech Intelligibility Index, and the Speech Transmission Index do not predict intelligibility and segment recognition well under certain conditions (e.g., discontiguous frequency bands, audio-visual speech)....

  14. Automated Feature Set Selection and Its Application to MCC Identification in Digital Mammograms for Breast Cancer Detection

    Directory of Open Access Journals (Sweden)

    Wu-Chung Shen

    2013-04-01

    Full Text Available We propose a fully automated algorithm that is able to select a discriminative feature set from a training database via sequential forward selection (SFS, sequential backward selection (SBS, and F-score methods. We applied this scheme to microcalcifications cluster (MCC detection in digital mammograms for early breast cancer detection. The system was able to select features fully automatically, regardless of the input training mammograms used. We tested the proposed scheme using a database of 111 clinical mammograms containing 1,050 microcalcifications (MCs. The accuracy of the system was examined via a free response receiver operating characteristic (fROC curve of the test dataset. The system performance for MC identifications was Az = 0.9897, the sensitivity was 92%, and 0.65 false positives (FPs were generated per image for MCC detection.

  15. Character recognition in a Japanese text recognition system

    Science.gov (United States)

    Hong, Tao; Srikantan, Geetha; Zandy, V. C.; Fang, Chi; Srihari, Sargur N.

    1996-03-01

    Cherry Blossom is a machine-printed Japanese document recognition system developed at CEDAR in past years. This paper focuses on the character recognition part of the system. for Japanese character classification, two feature sets are used in the system: one is the local stroke direction feature; another is the gradient, structural and concavity feature. Based on each of those features, two different classifiers are designed: one is the so-called minimum error subspace classifier; another is the fast nearest-neighbor (FNN) classifier. Although the original version of the FNN classifier uses Euclidean distance measurement, its new version uses both Euclidean distance and the distance calculation defined in the ME subspace method. This integration improved performance significantly. The number of character classes handled by those classifiers is about 3,300 (including alphanumeric, kana and level-1 Kanji JIS). Classifiers were trained and tested on 200 ppi character images from CEDAR Japanese character image CD-ROM.

  16. Discovery and Molecular Basis of a Diverse Set of Polycomb Repressive Complex 2 Inhibitors Recognition by EED.

    Directory of Open Access Journals (Sweden)

    Ling Li

    Full Text Available Polycomb repressive complex 2 (PRC2, a histone H3 lysine 27 methyltransferase, plays a key role in gene regulation and is a known epigenetics drug target for cancer therapy. The WD40 domain-containing protein EED is the regulatory subunit of PRC2. It binds to the tri-methylated lysine 27 of the histone H3 (H3K27me3, and through which stimulates the activity of PRC2 allosterically. Recently, we disclosed a novel PRC2 inhibitor EED226 which binds to the K27me3-pocket on EED and showed strong antitumor activity in xenograft mice model. Here, we further report the identification and validation of four other EED binders along with EED162, the parental compound of EED226. The crystal structures for all these five compounds in complex with EED revealed a common deep pocket induced by the binding of this diverse set of compounds. This pocket was created after significant conformational rearrangement of the aromatic cage residues (Y365, Y148 and F97 in the H3K27me3 binding pocket of EED, the width of which was delineated by the side chains of these rearranged residues. In addition, all five compounds interact with the Arg367 at the bottom of the pocket. Each compound also displays unique features in its interaction with EED, suggesting the dynamics of the H3K27me3 pocket in accommodating the binding of different compounds. Our results provide structural insights for rational design of novel EED binder for the inhibition of PRC2 complex activity.

  17. Emotion recognition using eigenvalues and Levenberg–Marquardt ...

    Indian Academy of Sciences (India)

    Vilas H Gaidhane

    based method, these methods are more sensitive to noise and tracking errors. Automatic facial expression recognition involves facial features representation and classification. Facial features representation is used to calculate a set of features from the original face images. In literature, optical flow analysis has been used to ...

  18. Image recognition with missing-features based on gaussian mixture model and graph constrained nonnegative matrix factorization.

    Science.gov (United States)

    Zhuyan Zhang; Hongqing Zhu; Xuan Tao

    2017-07-01

    The demand for automatically recognizing medical images for screening, reference and management is growing faster than ever. Missing data phenomenon in medical image applications is common existence, and it could be inevitable. In this paper, we have addressed the problem of recognizing medical images with missing-features via Gaussian mixture model (GMM)-based approach. Since training a GMM by directly using high-dimensional feature vectors will result in instability, we have proposed a novel strategy to train the GMM from the corresponding reduced-dimensional one. The proposed method contains training and test phases. The former contains feature extraction, graph constrained nonnegative matrix factorization (NMF), GMM training, and the alternating expectation conditional maximization (AECM) for extending the reduced-dimensional GMM. In test phase, two methods, marginalizing GMM using Bayesian decision (MGBD) and conditional mean imputation (CMI), are applied to impute missing-features. Posterior probability of test images is calculated to identify objects. Experimental results on three real datasets demonstrate the feasibility and efficiency of the proposed scheme.

  19. SigVox - A 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds

    Science.gov (United States)

    Wang, Jinhu; Lindenbergh, Roderik; Menenti, Massimo

    2017-06-01

    Urban road environments contain a variety of objects including different types of lamp poles and traffic signs. Its monitoring is traditionally conducted by visual inspection, which is time consuming and expensive. Mobile laser scanning (MLS) systems sample the road environment efficiently by acquiring large and accurate point clouds. This work proposes a methodology for urban road object recognition from MLS point clouds. The proposed method uses, for the first time, shape descriptors of complete objects to match repetitive objects in large point clouds. To do so, a novel 3D multi-scale shape descriptor is introduced, that is embedded in a workflow that efficiently and automatically identifies different types of lamp poles and traffic signs. The workflow starts by tiling the raw point clouds along the scanning trajectory and by identifying non-ground points. After voxelization of the non-ground points, connected voxels are clustered to form candidate objects. For automatic recognition of lamp poles and street signs, a 3D significant eigenvector based shape descriptor using voxels (SigVox) is introduced. The 3D SigVox descriptor is constructed by first subdividing the points with an octree into several levels. Next, significant eigenvectors of the points in each voxel are determined by principal component analysis (PCA) and mapped onto the appropriate triangle of a sphere approximating icosahedron. This step is repeated for different scales. By determining the similarity of 3D SigVox descriptors between candidate point clusters and training objects, street furniture is automatically identified. The feasibility and quality of the proposed method is verified on two point clouds obtained in opposite direction of a stretch of road of 4 km. 6 types of lamp pole and 4 types of road sign were selected as objects of interest. Ground truth validation showed that the overall accuracy of the ∼170 automatically recognized objects is approximately 95%. The results demonstrate

  20. Using sense-making theory to aid understanding of the recognition, assessment and management of pain in patients with dementia in acute hospital settings.

    Science.gov (United States)

    Dowding, Dawn; Lichtner, Valentina; Allcock, Nick; Briggs, Michelle; James, Kirstin; Keady, John; Lasrado, Reena; Sampson, Elizabeth L; Swarbrick, Caroline; José Closs, S

    2016-01-01

    The recognition, assessment and management of pain in hospital settings is suboptimal, and is a particular challenge in patients with dementia. The existing process guiding pain assessment and management in clinical settings is based on the assumption that nurses follow a sequential linear approach to decision making. In this paper we re-evaluate this theoretical assumption drawing on findings from a study of pain recognition, assessment and management in patients with dementia. To provide a revised conceptual model of pain recognition, assessment and management based on sense-making theories of decision making. The research we refer to is an exploratory ethnographic study using nested case sites. Patients with dementia (n=31) were the unit of data collection, nested in 11 wards (vascular, continuing care, stroke rehabilitation, orthopaedic, acute medicine, care of the elderly, elective and emergency surgery), located in four NHS hospital organizations in the UK. Data consisted of observations of patients at bedside (170h in total); observations of the context of care; audits of patient hospital records; documentary analysis of artefacts; semi-structured interviews (n=56) and informal open conversations with staff and carers (family members). Existing conceptualizations of pain recognition, assessment and management do not fully explain how the decision process occurs in clinical practice. Our research indicates that pain recognition, assessment and management is not an individual cognitive activity; rather it is carried out by groups of individuals over time and within a specific organizational culture or climate, which influences both health care professional and patient behaviour. We propose a revised theoretical model of decision making related to pain assessment and management for patients with dementia based on theories of sense-making, which is reflective of the reality of clinical decision making in acute hospital wards. The revised model recognizes the

  1. The expression and recognition of emotions in the voice across five nations: A lens model analysis based on acoustic features.

    Science.gov (United States)

    Laukka, Petri; Elfenbein, Hillary Anger; Thingujam, Nutankumar S; Rockstuhl, Thomas; Iraki, Frederick K; Chui, Wanda; Althoff, Jean

    2016-11-01

    This study extends previous work on emotion communication across cultures with a large-scale investigation of the physical expression cues in vocal tone. In doing so, it provides the first direct test of a key proposition of dialect theory, namely that greater accuracy of detecting emotions from one's own cultural group-known as in-group advantage-results from a match between culturally specific schemas in emotional expression style and culturally specific schemas in emotion recognition. Study 1 used stimuli from 100 professional actors from five English-speaking nations vocally conveying 11 emotional states (anger, contempt, fear, happiness, interest, lust, neutral, pride, relief, sadness, and shame) using standard-content sentences. Detailed acoustic analyses showed many similarities across groups, and yet also systematic group differences. This provides evidence for cultural accents in expressive style at the level of acoustic cues. In Study 2, listeners evaluated these expressions in a 5 × 5 design balanced across groups. Cross-cultural accuracy was greater than expected by chance. However, there was also in-group advantage, which varied across emotions. A lens model analysis of fundamental acoustic properties examined patterns in emotional expression and perception within and across groups. Acoustic cues were used relatively similarly across groups both to produce and judge emotions, and yet there were also subtle cultural differences. Speakers appear to have a culturally nuanced schema for enacting vocal tones via acoustic cues, and perceivers have a culturally nuanced schema in judging them. Consistent with dialect theory's prediction, in-group judgments showed a greater match between these schemas used for emotional expression and perception. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  2. Emotion Recognition with Eigen Features of Frequency Band Activities Embedded in Induced Brain Oscillations Mediated by Affective Pictures.

    Science.gov (United States)

    Aydin, Serap; Demirtaş, Serdar; Ateş, Kahraman; Tunga, M Alper

    2016-05-01

    In this study, singular spectrum analysis (SSA) has been used for the first time in order to extract emotional features from well-defined electroencephalography (EEG) frequency band activities (BAs) so-called delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-16 Hz), beta (16-32 Hz), gamma (32-64 Hz). These five BAs were estimated by applying sixth-level multi-resolution wavelet decomposition (MRWD) with Daubechies wavelets (db-8) to single channel nonaveraged emotional EEG oscillations of 6 s for each scalp location over 16 recording sites (Fp1, Fp2, F3, F4, F7, F8, C3, C4, P3, P4, T3, T4, T5, T6, O1, O2). Every trial was mediated by different emotional stimuli which were selected from international affective picture system (IAPS) to induce emotional states such as pleasant (P), neutral (N), and unpleasant (UP). Largest principal components (PCs) of BAs were considered as emotional features and data mining approaches were used for the first time in order to classify both three different (P, N, UP) and two contrasting (P and UP) emotional states for 30 healthy controls. Emotional features extracted from gamma BAs (GBAs) for 16 recording sites provided the high classification accuracies of 87.1% and 100% for classification of three emotional states and two contrasting emotional states, respectively. In conclusion, we found the followings: (1) Eigenspectra of high frequency BAs in EEG are highly sensitive to emotional hemispheric activations, (2) emotional states are mostly mediated by GBA, (3) pleasant pictures induce the higher cortical activation in contrast to unpleasant pictures, (4) contrasting emotions induce opposite cortical activations, (5) cognitive activities are necessary for an emotion to occur.

  3. A Study in Speech Recognition Using a Kohonen Neural Network Dynamic Programming and Multi-Feature Fusion

    Science.gov (United States)

    1989-12-01

    FEATURE FUSION THESIS Presented to the Faculty of the School of Engineering of the Air Force Institute of Technology Air University In Partial...years of schooling . Because of her sacrifice and understanding, I was able to fulfill many personal dreams. Wayne F. Recla AossionFo TIS GRA&f’- DTIC...where: 42 LINEAR / EXPONENTIAL Example Gain Graph Gaia Value 0.1 0.08 0.04 0.02 S󈧎 20 30 40 80 so 70 so 90 100 Training Iterations (x 1000) - Linea

  4. "I'm concerned - What Do I Do?" recognition and management of disordered eating in fitness center settings.

    Science.gov (United States)

    Bratland-Sanda, Solfrid; Sundgot-Borgen, Jorunn

    2015-05-01

    To examine group fitness instructors' knowledge and attitudes toward identification and management of disordered eating (DE). Group fitness instructors representing the three largest fitness center companies in Norway (n = 837, response rate: 57%) completed a questionnaire through Questback (www.questback.com). The questionnaire contained items regarding gender, age, educational background, exercise behavior, and knowledge of recognition and response to DE. Eighty-nine percent of the respondents reported knowledge about symptoms of DE, 29% was classified with adequate DE knowledge skills. Forty-nine percent of the instructors reported current concern about DE among one or more members, 47% reported knowledge about how to recognize and respond to DE, and 37% reported knowledge about their fitness center's guidelines for approaching DE concerns. The level of formal education in sports and exercise, and a history of self-reported eating disorder, but not fitness instructor experience, were explanatory factors for knowledge about DE symptoms. Both exercise specific educational level and instructor experience were explanatory variables for knowledge about recognition of and response to DE concerns. Implications of the findings include a need for increased confidence among group fitness instructors regarding how to approach DE concerns, increased awareness of excessive/compulsive exercise as a symptom of DE, and enhanced dissemination of existing guidelines for managing DE concerns among members and/or staff. © 2014 Wiley Periodicals, Inc.

  5. Speech emotion recognition methods: A literature review

    Science.gov (United States)

    Basharirad, Babak; Moradhaseli, Mohammadreza

    2017-10-01

    Recently, attention of the emotional speech signals research has been boosted in human machine interfaces due to availability of high computation capability. There are many systems proposed in the literature to identify the emotional state through speech. Selection of suitable feature sets, design of a proper classifications methods and prepare an appropriate dataset are the main key issues of speech emotion recognition systems. This paper critically analyzed the current available approaches of speech emotion recognition methods based on the three evaluating parameters (feature set, classification of features, accurately usage). In addition, this paper also evaluates the performance and limitations of available methods. Furthermore, it highlights the current promising direction for improvement of speech emotion recognition systems.

  6. Partial least squares regression can aid in detecting differential abundance of multiple features in sets of metagenomic samples

    Directory of Open Access Journals (Sweden)

    Ondrej eLibiger

    2015-12-01

    Full Text Available It is now feasible to examine the composition and diversity of microbial communities (i.e., `microbiomes‘ that populate different human organs and orifices using DNA sequencing and related technologies. To explore the potential links between changes in microbial communities and various diseases in the human body, it is essential to test associations involving different species within and across microbiomes, environmental settings and disease states. Although a number of statistical techniques exist for carrying out relevant analyses, it is unclear which of these techniques exhibit the greatest statistical power to detect associations given the complexity of most microbiome datasets. We compared the statistical power of principal component regression, partial least squares regression, regularized regression, distance-based regression, Hill's diversity measures, and a modified test implemented in the popular and widely used microbiome analysis methodology 'Metastats‘ across a wide range of simulated scenarios involving changes in feature abundance between two sets of metagenomic samples. For this purpose, simulation studies were used to change the abundance of microbial species in a real dataset from a published study examining human hands. Each technique was applied to the same data, and its ability to detect the simulated change in abundance was assessed. We hypothesized that a small subset of methods would outperform the rest in terms of the statistical power. Indeed, we found that the Metastats technique modified to accommodate multivariate analysis and partial least squares regression yielded high power under the models and data sets we studied. The statistical power of diversity measure-based tests, distance-based regression and regularized regression was significantly lower. Our results provide insight into powerful analysis strategies that utilize information on species counts from large microbiome data sets exhibiting skewed frequency

  7. Systematic feature analysis on timber defect images

    Directory of Open Access Journals (Sweden)

    Ummi Rabaah Hashim

    2017-07-01

    Full Text Available Feature extraction is unquestionably an important process in a pattern recognition system. A defined set of features makes the identification task more efficiently. This paper addresses the extraction and analysis of features based on statistical texture to characterize images of timber defects. A series of procedures including feature extraction and feature analysis was executed to construct an appropriate feature set that could significantly separate amongst defects and clear wood classes. The feature set aimed for later use in a timber defect detection system. For Accessing the discrimination capability of the features extracted, visual exploratory analysis and confirmatory statistical analysis were performed on defect and clear wood images of Meranti (Shorea spp. timber species. Results from the analysis demonstrated that there was a significant distinction between defect classes and clear wood utilizing the proposed set of texture features.

  8. Election Districts and Precincts, PrecinctPoly-The data set is a polygon feature consisting of 220 segments representing voter precinct boundaries., Published in 1991, Davis County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Election Districts and Precincts dataset current as of 1991. PrecinctPoly-The data set is a polygon feature consisting of 220 segments representing voter precinct...

  9. The impact of image reconstruction settings on 18F-FDG PET radiomic features. Multi-scanner phantom and patient studies

    Energy Technology Data Exchange (ETDEWEB)

    Shiri, Isaac; Abdollahi, Hamid [Iran University of Medical Sciences, Department of Medical Physics, School of Medicine, Tehran (Iran, Islamic Republic of); Rahmim, Arman [Johns Hopkins University, Department of Radiology, Baltimore, MD (United States); Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, MD (United States); Ghaffarian, Pardis [Shahid Beheshti University of Medical Sciences, Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Tehran (Iran, Islamic Republic of); Shahid Beheshti University of Medical Sciences, PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Tehran (Iran, Islamic Republic of); Geramifar, Parham [Tehran University of Medical Sciences, Research Center for Nuclear Medicine, Shariati Hospital, Tehran (Iran, Islamic Republic of); Bitarafan-Rajabi, Ahmad [Iran University of Medical Sciences, Department of Medical Physics, School of Medicine, Tehran (Iran, Islamic Republic of); Iran University of Medical Sciences, Department of Nuclear Medicine, Rajaei Cardiovascular, Medical and Research Center, Tehran (Iran, Islamic Republic of)

    2017-11-15

    The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings. Phantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied. Lesions were delineated and one hundred radiomic features were extracted. All radiomics features were categorized based on coefficient of variation (COV). Forty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20%. All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively. In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively. Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features. Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies. (orig.)

  10. Hydrography, HydroLabels-The data set is a text feature containing labels of all hydro features. It consists of more than 230 text nodes of the hydro_line and hydro_poly features for display purposes., Published in 2005, Davis County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Hydrography dataset current as of 2005. HydroLabels-The data set is a text feature containing labels of all hydro features. It consists of more than 230 text nodes...

  11. Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    B. Ojeda-Magaña

    2013-01-01

    Full Text Available We take the concept of typicality from the field of cognitive psychology, and we apply the meaning to the interpretation of numerical data sets and color images through fuzzy clustering algorithms, particularly the GKPFCM, looking to get better information from the processed data. The Gustafson Kessel Possibilistic Fuzzy c-means (GKPFCM is a hybrid algorithm that is based on a relative typicality (membership degree, Fuzzy c-means and an absolute typicality (typicality value, Possibilistic c-means. Thus, using both typicalities makes it possible to learn and analyze data as well as to relate the results with the theory of prototypes. In order to demonstrate these results we use a synthetic data set and a digitized image of a glass, in a first example, and images from the Berkley database, in a second example. The results clearly demonstrate the advantages of the information obtained about numerical data sets, taking into account the different meaning of typicalities and the availability of both values with the clustering algorithm used. This approach allows the identification of small homogeneous regions, which are difficult to find.

  12. Vision-Based Navigation and Recognition

    National Research Council Canada - National Science Library

    Rosenfeld, Azriel

    1996-01-01

    .... (4) Invariants -- both geometric and other types. (5) Human faces: Analysis of images of human faces, including feature extraction, face recognition, compression, and recognition of facial expressions...

  13. Vision-Based Navigation and Recognition

    National Research Council Canada - National Science Library

    Rosenfeld, Azriel

    1998-01-01

    .... (4) Invariants: both geometric and other types. (5) Human faces: Analysis of images of human faces, including feature extraction, face recognition, compression, and recognition of facial expressions...

  14. Biologically inspired emotion recognition from speech

    Directory of Open Access Journals (Sweden)

    Buscicchio Cosimo

    2011-01-01

    Full Text Available Abstract Emotion recognition has become a fundamental task in human-computer interaction systems. In this article, we propose an emotion recognition approach based on biologically inspired methods. Specifically, emotion classification is performed using a long short-term memory (LSTM recurrent neural network which is able to recognize long-range dependencies between successive temporal patterns. We propose to represent data using features derived from two different models: mel-frequency cepstral coefficients (MFCC and the Lyon cochlear model. In the experimental phase, results obtained from the LSTM network and the two different feature sets are compared, showing that features derived from the Lyon cochlear model give better recognition results in comparison with those obtained with the traditional MFCC representation.

  15. Identification of a molecular recognition feature in the E1A oncoprotein that binds the SUMO conjugase UBC9 and likely interferes with polySUMOylation.

    Science.gov (United States)

    Yousef, A F; Fonseca, G J; Pelka, P; Ablack, J N G; Walsh, C; Dick, F A; Bazett-Jones, D P; Shaw, G S; Mymryk, J S

    2010-08-19

    Hub proteins have central roles in regulating cellular processes. By targeting a single cellular hub, a viral oncogene may gain control over an entire module in the cellular interaction network that is potentially comprised of hundreds of proteins. The adenovirus E1A oncoprotein is a viral hub that interacts with many cellular hub proteins by short linear motifs/molecular recognition features (MoRFs). These interactions transform the architecture of the cellular protein interaction network and virtually reprogram the cell. To identify additional MoRFs within E1A, we screened portions of E1A for their ability to activate yeast pseudohyphal growth or differentiation. This identified a novel functional region within E1A conserved region 2 comprised of the sequence EVIDLT. This MoRF is necessary and sufficient to bind the N-terminal region of the SUMO conjugase UBC9, which also interacts with SUMO noncovalently and is involved in polySUMOylation. Our results suggest that E1A interferes with polySUMOylation, but not with monoSUMOylation. These data provide the first insight into the consequences of the interaction of E1A with UBC9, which was initially described in 1996. We further demonstrate that polySUMOylation regulates pseudohyphal growth and promyelocytic leukemia body reorganization by E1A. In conclusion, the interaction of the E1A oncogene with UBC9 mimics the normal binding between SUMO and UBC9 and represents a novel mechanism to modulate polySUMOylation.

  16. Face recognition using Krawtchouk moment

    Indian Academy of Sciences (India)

    Feature extraction is one of the important tasks in face recognition. Moments are widely used feature extractor due to their superior discriminatory power and geometrical invariance. Moments generally capture the global features of the image. This paper proposes Krawtchouk moment for feature extraction in face recognition ...

  17. Gait recognition based on Kinect sensor

    Science.gov (United States)

    Ahmed, Mohammed; Al-Jawad, Naseer; Sabir, Azhin T.

    2014-05-01

    This paper presents gait recognition based on human skeleton and trajectory of joint points captured by Microsoft Kinect sensor. In this paper Two sets of dynamic features are extracted during one gait cycle: the first is Horizontal Distance Features (HDF) that is based on the distances between (Ankles, knees, hands, shoulders), the second set is the Vertical Distance Features (VDF) that provide significant information of human gait extracted from the height to the ground of (hand, shoulder, and ankles) during one gait cycle. Extracting these two sets of feature are difficult and not accurate based on using traditional camera, therefore the Kinect sensor is used in this paper to determine the precise measurements. The two sets of feature are separately tested and then fused to create one feature vector. A database has been created in house to perform our experiments. This database consists of sixteen males and four females. For each individual, 10 videos have been recorded, each record includes in average two gait cycles. The Kinect sensor is used here to extract all the skeleton points, and these points are used to build up the feature vectors mentioned above. K-nearest neighbor is used as the classification method based on Cityblock distance function. Based on the experimental result the proposed method provides 56% as a recognition rate using HDF, while VDF provided 83.5% recognition accuracy. When fusing both of the HDF and VDF as one feature vector, the recognition rate increased to 92%, the experimental result shows that our method provides significant result compared to the existence methods.

  18. Embedded Bernoulli Mixture HMMs for Continuous Handwritten Text Recognition

    Science.gov (United States)

    Giménez, Adrià; Juan, Alfons

    Hidden Markov Models (HMMs) are now widely used in off-line handwritten text recognition. As in speech recognition, they are usually built from shared, embedded HMMs at symbol level, in which state-conditional probability density functions are modelled with Gaussian mixtures. In contrast to speech recognition, however, it is unclear which kind of real-valued features should be used and, indeed, very different features sets are in use today. In this paper, we propose to by-pass feature extraction and directly fed columns of raw, binary image pixels into embedded Bernoulli mixture HMMs, that is, embedded HMMs in which the emission probabilities are modelled with Bernoulli mixtures. The idea is to ensure that no discriminative information is filtered out during feature extraction, which in some sense is integrated into the recognition model. Good empirical results are reported on the well-known IAM database.

  19. Water Quality Assessment in the Harbin Reach of the Songhuajiang River (China Based on a Fuzzy Rough Set and an Attribute Recognition Theoretical Model

    Directory of Open Access Journals (Sweden)

    Yan An

    2014-03-01

    Full Text Available A large number of parameters are acquired during practical water quality monitoring. If all the parameters are used in water quality assessment, the computational complexity will definitely increase. In order to reduce the input space dimensions, a fuzzy rough set was introduced to perform attribute reduction. Then, an attribute recognition theoretical model and entropy method were combined to assess water quality in the Harbin reach of the Songhuajiang River in China. A dataset consisting of ten parameters was collected from January to October in 2012. Fuzzy rough set was applied to reduce the ten parameters to four parameters: BOD5, NH3-N, TP, and F. coli (Reduct A. Considering that DO is a usual parameter in water quality assessment, another reduct, including DO, BOD5, NH3-N, TP, TN, F, and F. coli (Reduct B, was obtained. The assessment results of Reduct B show a good consistency with those of Reduct A, and this means that DO is not always necessary to assess water quality. The results with attribute reduction are not exactly the same as those without attribute reduction, which can be attributed to the α value decided by subjective experience. The assessment results gained by the fuzzy rough set obviously reduce computational complexity, and are acceptable and reliable. The model proposed in this paper enhances the water quality assessment system.

  20. Increasing awareness with recognition of pulsatile tinnitus for nurse practitioners in the primary care setting: A case study.

    Science.gov (United States)

    Vecchiarelli, Kelly; Amar, Arun Paul; Emanuele, Donna

    2017-09-01

    Pulsatile tinnitus is a whooshing sound heard synchronous with the heartbeat. It is an uncommon symptom affecting fewer than 10% of patients with tinnitus. It often goes unrecognized in the primary care setting. Failure to recognize this symptom can result in a missed or delayed diagnosis of a potentially life-threatening condition known as a dural arteriovenous fistula. The purpose of this case study is to provide a structured approach to the identification of pulsatile tinnitus and provide management recommendations. A case study and review of pertinent literature. Pulsatile tinnitus usually has a vascular treatable cause. A comprehensive history and physical examination will alert the nurse practitioner (NP) when pulsatile tinnitus is present. Auscultation in specific areas of the head can detect audible or objective pulsatile tinnitus. Pulsatile tinnitus that is audible to the examiner is an urgent medical condition requiring immediate consultation and referral. Knowledge of pulsatile tinnitus and awareness of this often treatable condition directs the NP to perform a detailed assessment when patients present with tinnitus, directs appropriate referral for care and treatment, and can reduce the risk of delayed or missed diagnosis. ©2017 American Association of Nurse Practitioners.

  1. Global 21 cm Signal Extraction from Foreground and Instrumental Effects. I. Pattern Recognition Framework for Separation Using Training Sets

    Science.gov (United States)

    Tauscher, Keith; Rapetti, David; Burns, Jack O.; Switzer, Eric

    2018-02-01

    The sky-averaged (global) highly redshifted 21 cm spectrum from neutral hydrogen is expected to appear in the VHF range of ∼20–200 MHz and its spectral shape and strength are determined by the heating properties of the first stars and black holes, by the nature and duration of reionization, and by the presence or absence of exotic physics. Measurements of the global signal would therefore provide us with a wealth of astrophysical and cosmological knowledge. However, the signal has not yet been detected because it must be seen through strong foregrounds weighted by a large beam, instrumental calibration errors, and ionospheric, ground, and radio-frequency-interference effects, which we collectively refer to as “systematics.” Here, we present a signal extraction method for global signal experiments which uses Singular Value Decomposition of “training sets” to produce systematics basis functions specifically suited to each observation. Instead of requiring precise absolute knowledge of the systematics, our method effectively requires precise knowledge of how the systematics can vary. After calculating eigenmodes for the signal and systematics, we perform a weighted least square fit of the corresponding coefficients and select the number of modes to include by minimizing an information criterion. We compare the performance of the signal extraction when minimizing various information criteria and find that minimizing the Deviance Information Criterion most consistently yields unbiased fits. The methods used here are built into our widely applicable, publicly available Python package, pylinex, which analytically calculates constraints on signals and systematics from given data, errors, and training sets.

  2. Ultraperformance liquid chromatography-mass spectrometry based comprehensive metabolomics combined with pattern recognition and network analysis methods for characterization of metabolites and metabolic pathways from biological data sets.

    Science.gov (United States)

    Zhang, Ai-hua; Sun, Hui; Han, Ying; Yan, Guang-li; Yuan, Ye; Song, Gao-chen; Yuan, Xiao-xia; Xie, Ning; Wang, Xi-jun

    2013-08-06

    Metabolomics is the study of metabolic changes in biological systems and provides the small molecule fingerprints related to the disease. Extracting biomedical information from large metabolomics data sets by multivariate data analysis is of considerable complexity. Therefore, more efficient and optimizing metabolomics data processing technologies are needed to improve mass spectrometry applications in biomarker discovery. Here, we report the findings of urine metabolomic investigation of hepatitis C virus (HCV) patients by high-throughput ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) coupled with pattern recognition methods (principal component analysis, partial least-squares, and OPLS-DA) and network pharmacology. A total of 20 urinary differential metabolites (13 upregulated and 7 downregulated) were identified and contributed to HCV progress, involve several key metabolic pathways such as taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, histidine metabolism, arginine and proline metabolism, and so forth. Metabolites identified through metabolic profiling may facilitate the development of more accurate marker algorithms to better monitor disease progression. Network analysis validated close contact between these metabolites and implied the importance of the metabolic pathways. Mapping altered metabolites to KEGG pathways identified alterations in a variety of biological processes mediated through complex networks. These findings may be promising to yield a valuable and noninvasive tool that insights into the pathophysiology of HCV and to advance the early diagnosis and monitor the progression of disease. Overall, this investigation illustrates the power of the UPLC-MS platform combined with the pattern recognition and network analysis methods that can engender new insights into HCV pathobiology.

  3. Pattern recognition & machine learning

    CERN Document Server

    Anzai, Y

    1992-01-01

    This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

  4. Neural Network and Letter Recognition.

    Science.gov (United States)

    Lee, Hue Yeon

    Neural net architectures and learning algorithms that recognize hand written 36 alphanumeric characters are studied. The thin line input patterns written in 32 x 32 binary array are used. The system is comprised of two major components, viz. a preprocessing unit and a Recognition unit. The preprocessing unit in turn consists of three layers of neurons; the U-layer, the V-layer, and the C -layer. The functions of the U-layer is to extract local features by template matching. The correlation between the detected local features are considered. Through correlating neurons in a plane with their neighboring neurons, the V-layer would thicken the on-cells or lines that are groups of on-cells of the previous layer. These two correlations would yield some deformation tolerance and some of the rotational tolerance of the system. The C-layer then compresses data through the 'Gabor' transform. Pattern dependent choice of center and wavelengths of 'Gabor' filters is the cause of shift and scale tolerance of the system. Three different learning schemes had been investigated in the recognition unit, namely; the error back propagation learning with hidden units, a simple perceptron learning, and a competitive learning. Their performances were analyzed and compared. Since sometimes the network fails to distinguish between two letters that are inherently similar, additional ambiguity resolving neural nets are introduced on top of the above main neural net. The two dimensional Fourier transform is used as the preprocessing and the perceptron is used as the recognition unit of the ambiguity resolver. One hundred different person's handwriting sets are collected. Some of these are used as the training sets and the remainders are used as the test sets. The correct recognition rate of the system increases with the number of training sets and eventually saturates at a certain value. Similar recognition rates are obtained for the above three different learning algorithms. The minimum error

  5. The clinical features of burns resulting from two aerial devices set off in a public fireworks display: 149 case reports.

    Science.gov (United States)

    He, Xiaosheng; Sun, Dongjie; Zhong, Xiaochun; Liu, Maolin; Ni, Youdi

    2014-12-01

    We report the clinical features of 149 cases with aerial devices burns in a public fireworks display. The characteristic features included sudden onset, masses of terrified burn victims, small and deep wounds, mild disease conditions, and favorable prognosis. Unlike in home or illegal fireworks displays, the body areas most often involved were the extremity, chest, abdomen, and back, and most of the victims were adults in these public fireworks displays. Copyright © 2014 Elsevier Ltd and ISBI. All rights reserved.

  6. Radar Target Recognition Based on Stacked Denoising Sparse Autoencoder

    Directory of Open Access Journals (Sweden)

    Zhao Feixiang

    2017-04-01

    Full Text Available Feature extraction is a key step in radar target recognition. The quality of the extracted features determines the performance of target recognition. However, obtaining the deep nature of the data is difficult using the traditional method. The autoencoder can learn features by making use of data and can obtain feature expressions at different levels of data. To eliminate the influence of noise, the method of radar target recognition based on stacked denoising sparse autoencoder is proposed in this paper. This method can extract features directly and efficiently by setting different hidden layers and numbers of iterations. Experimental results show that the proposed method is superior to the K-nearest neighbor method and the traditional stacked autoencoder.

  7. Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

    Science.gov (United States)

    Siddiqi, Muhammad Hameed; Lee, Sungyoung; Lee, Young-Koo; Khan, Adil Mehmood; Truc, Phan Tran Ho

    2013-01-01

    Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER) system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER. PMID:24316568

  8. Speaker Recognition

    DEFF Research Database (Denmark)

    Mølgaard, Lasse Lohilahti; Jørgensen, Kasper Winther

    2005-01-01

    Speaker recognition is basically divided into speaker identification and speaker verification. Verification is the task of automatically determining if a person really is the person he or she claims to be. This technology can be used as a biometric feature for verifying the identity of a person...... in applications like banking by telephone and voice mail. The focus of this project is speaker identification, which consists of mapping a speech signal from an unknown speaker to a database of known speakers, i.e. the system has been trained with a number of speakers which the system can recognize....

  9. Technology of building an expert system based on a set of quantitative features of tumor cell nuclei for diagnosing breast cancer.

    Science.gov (United States)

    Kirillov, Vladimir

    2013-06-01

    The technology of building an expert system for diagnosing malignant nature of invasive tumors of the mammary gland based on a set of quantitative features of the cell nuclei has been developed. Its peculiarity was the presence of weighting coefficients in all the features. Quantitative features were obtained by transforming the initial morphometric data with the help of simple (evaluation of mean values and building of histograms) and complex (regression analysis) mathematical operations. The expert system consisted of one-dimensional X-matrix used for investigations and two-dimensional standard S-matrix. The X-matrix elements were assigned for filling with the quantitative features of the studied sample with a nonestablished diagnosis. The S-matrix elements contained threshold values of quantitative features from the system of diagnostic decision criteria for malignant forms of diseases and their weighting coefficients. Threshold values of nuclear features (larger or smaller) were determined taking into account the range of their values in the groups of malignant and benign pathology. Significance of quantitative features in diagnosing diseases has been assessed. The presence of weighting coefficients allowed diagnosing malignant and benign pathology in a quantitative form by the diagnostic index value. Diagnostic index was calculated by the sum of weighting coefficients of features of the studied sample, which fell within the range of system of the S-matrix diagnostic decision criteria. Clinical trials revealed high efficiency of the developed approach while diagnosis of breast cancer invasive forms at a preoperative stage. Copyright © 2012 Wiley Periodicals, Inc.

  10. Off-line cursive handwriting recognition using multiple classifier systems—on the influence of vocabulary, ensemble, and training set size

    Science.gov (United States)

    Günter, Simon; Bunke, Horst

    2005-03-01

    Unconstrained handwritten text recognition is one of the most difficult problems in the field of pattern recognition. Recently, a number of classifier creation and combination methods, known as ensemble methods, have been proposed in the field of machine learning. They have shown improved recognition performance over single classifiers. In this paper, we examine the influence of the vocabulary size, the number of training samples, and the number of classifiers on the performance of three ensemble methods in the context of cursive handwriting recognition. All experiments were conducted using an off-line handwritten word recognizer based on hidden Markov models (HMMs).

  11. Subauditory Speech Recognition based on EMG/EPG Signals

    Science.gov (United States)

    Jorgensen, Charles; Lee, Diana Dee; Agabon, Shane; Lau, Sonie (Technical Monitor)

    2003-01-01

    Sub-vocal electromyogram/electro palatogram (EMG/EPG) signal classification is demonstrated as a method for silent speech recognition. Recorded electrode signals from the larynx and sublingual areas below the jaw are noise filtered and transformed into features using complex dual quad tree wavelet transforms. Feature sets for six sub-vocally pronounced words are trained using a trust region scaled conjugate gradient neural network. Real time signals for previously unseen patterns are classified into categories suitable for primitive control of graphic objects. Feature construction, recognition accuracy and an approach for extension of the technique to a variety of real world application areas are presented.

  12. Rats (Rattus norvegicus) flexibly retrieve objects' non-spatial and spatial information from their visuospatial working memory: effects of integrated and separate processing of these features in a missing-object recognition task.

    Science.gov (United States)

    Keshen, Corrine; Cohen, Jerome

    2016-01-01

    After being trained to find a previous missing object within an array of four different objects, rats received occasional probe trials with such test arrays rotated from that of their respective three-object study arrays. Only animals exposed to each object's non-spatial features consistently paired with both its spatial features (feeder's relative orientation and direction) in the first experiment or with only feeder's relative orientation in the second experiment (Fixed Configuration groups) were adversely affected by probe trial test array rotations. This effect, however, was less persistent for this group in the second experiment but re-emerged when objects' non-spatial features were later rendered uninformative. Animals that had both types of each object's features randomly paired over trials but not between a trial's study and test array (Varied Configuration groups) were not adversely affected on probe trials but improved their missing-object recognition in the first experiment. These findings suggest that the Fixed Configuration groups had integrated each object's non-spatial with both (in Experiment 1) or one (in Experiment 2) of its spatial features to construct a single representation that they could not easily compare to any object in a rotated probe test array. The Varied Configuration groups must maintain separate representations of each object's features to solve this task. This prevented them from exhibiting such adverse effects on rotated probe trial test arrays but enhanced the rats' missing-object recognition in the first experiment. We discussed how rats' flexible use (retrieval) of encoded information from their visuospatial working memory corresponds to that of humans' visuospatial memory in object change detection and complex object recognition tasks. We also discussed how foraging-specific factors may have influenced each group's performance in this task.

  13. Emotional recognition from the speech signal for a virtual education agent

    Science.gov (United States)

    Tickle, A.; Raghu, S.; Elshaw, M.

    2013-06-01

    This paper explores the extraction of features from the speech wave to perform intelligent emotion recognition. A feature extract tool (openSmile) was used to obtain a baseline set of 998 acoustic features from a set of emotional speech recordings from a microphone. The initial features were reduced to the most important ones so recognition of emotions using a supervised neural network could be performed. Given that the future use of virtual education agents lies with making the agents more interactive, developing agents with the capability to recognise and adapt to the emotional state of humans is an important step.

  14. Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.

    Science.gov (United States)

    Khaligh-Razavi, Seyed-Mahdi; Henriksson, Linda; Kay, Kendrick; Kriegeskorte, Nikolaus

    2017-02-01

    Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set. This method, called representational similarity analysis (RSA), enables us to test the representational feature space as is (fixed RSA) or to fit a linear transformation that mixes the nonlinear model features so as to best explain a cortical area's representational space (mixed RSA). Like voxel/population-receptive-field modelling, mixed RSA uses a training set (different stimuli) to fit one weight per model feature and response channel (voxels here), so as to best predict the response profile across images for each response channel. We analysed response patterns elicited by natural images, which were measured with functional magnetic resonance imaging (fMRI). We found that early visual areas were best accounted for by shallow models, such as a Gabor wavelet pyramid (GWP). The GWP model performed similarly with and without mixing, suggesting that the original features already approximated the representational space, obviating the need for mixing. However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network and mixing of its feature set was essential for this model to explain the representation. We suspect that mixing was essential because the convolutional network had been trained to discriminate a set of 1000 categories, whose frequencies

  15. Writer adaptation in off-line Arabic handwriting recognition

    Science.gov (United States)

    Ball, Gregory R.; Srihari, Sargur N.

    2008-01-01

    Writer adaptation or specialization is the adjustment of handwriting recognition algorithms to a specific writer's style of handwriting. Such adjustment yields significantly improved recognition rates over counterpart general recognition algorithms. We present the first unconstrained off-line handwriting adaptation algorithm for Arabic presented in the literature. We discuss an iterative bootstrapping model which adapts a writer-independent model to a writer-dependent model using a small number of words achieving a large recognition rate increase in the process. Furthermore, we describe a confidence weighting method which generates better results by weighting words based on their length. We also discuss script features unique to Arabic, and how we incorporate them into our adaptation process. Even though Arabic has many more character classes than languages such as English, significant improvement was observed. The testing set consisting of about 100 pages of handwritten text had an initial average overall recognition rate of 67%. After the basic adaptation was finished, the overall recognition rate was 73.3%. As the improvement was most marked for the longer words, and the set of confidently recognized longer words contained many fewer false results, a second method was presented using them alone, resulting in a recognition rate of about 75%. Initially, these words had a 69.5% recognition rate, improving to about a 92% recognition rate after adaptation. A novel hybrid method is presented with a rate of about 77.2%.

  16. Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

    OpenAIRE

    Dat Tien Nguyen; Ki Wan Kim; Hyung Gil Hong; Ja Hyung Koo; Min Cheol Kim; Kang Ryoung Park

    2017-01-01

    Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has ...

  17. Gesture recognition for smart home applications using portable radar sensors.

    Science.gov (United States)

    Wan, Qian; Li, Yiran; Li, Changzhi; Pal, Ranadip

    2014-01-01

    In this article, we consider the design of a human gesture recognition system based on pattern recognition of signatures from a portable smart radar sensor. Powered by AAA batteries, the smart radar sensor operates in the 2.4 GHz industrial, scientific and medical (ISM) band. We analyzed the feature space using principle components and application-specific time and frequency domain features extracted from radar signals for two different sets of gestures. We illustrate that a nearest neighbor based classifier can achieve greater than 95% accuracy for multi class classification using 10 fold cross validation when features are extracted based on magnitude differences and Doppler shifts as compared to features extracted through orthogonal transformations. The reported results illustrate the potential of intelligent radars integrated with a pattern recognition system for high accuracy smart home and health monitoring purposes.

  18. RSCM: Region Selection and Concurrency Model for Multi-Class Weather Recognition.

    Science.gov (United States)

    Lin, Di; Lu, Cewu; Huang, Hui; Jia, Jiaya

    2017-09-01

    Toward weather condition recognition, we emphasize the importance of regional cues in this paper and address a few important problems regarding appropriate representation, its differentiation among regions, and weather-condition feature construction. Our major contribution is, first, to construct a multi-class benchmark data set containing 65 000 images from six common categories for sunny, cloudy, rainy, snowy, haze, and thunder weather. This data set also benefits weather classification and attribute recognition. Second, we propose a deep learning framework named region selection and concurrency model (RSCM) to help discover regional properties and concurrency. We evaluate RSCM on our multi-class benchmark data and another public data set for weather recognition.

  19. A multi-biometric feature-fusion framework for improved uni-modal and multi-modal human identification

    CSIR Research Space (South Africa)

    Brown, K

    2016-05-01

    Full Text Available . However, individual feature sets can outperform a fused feature sets con- taining one or more poor quality individual feature sets. This is expected to further improve the recognition performance. The scope includes the use of different sized datasets..., fingerprint or palmprint. Yao et al. [5] combined the face and palmprint and pro- cessed the fused dataset with four PCA-based feature-fusion algorithms. The best performing algorithm filters both input modalities with Gabor filters followed by weighted...

  20. SPECIFIC FEATURES OF PSYCHOLOGICAL CARE FOR PATIENTS WITH PULMONARY TUBERCULOSIS DURING INTENSIVE CHEMOTHERAPY (IN THE HOSPITAL SETTING

    Directory of Open Access Journals (Sweden)

    V. V. Streltsov

    2014-01-01

    Full Text Available The psychological trauma of pulmonary tuberculosis and long-term treatment may cause the development and progression of different borderline neuropsychic disorders in patients, lower therapeutic effectiveness, and prematurely discontinue therapy. The main practical tasks of psychological rehabilitation during intensive treatment are to render care for a patient during his adaptation to the hospital setting, to correct inadequate attitude towards disease, and to motivate active cooperation with specialists. Competent psychological support of drug therapy promotes a reduction in the intensity of psychic and somatic experiences in the patient and an increase in his psychological resources. A respective microclimate in the tuberculosis control facility and a patient-centered doctorpatient model should be considered as the most important rehabilitation factors.

  1. Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

    Science.gov (United States)

    Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

    2016-01-01

    recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease. PMID:27977767

  2. Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

    Directory of Open Access Journals (Sweden)

    Feng Qin

    recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

  3. Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

    Science.gov (United States)

    Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

    2016-01-01

    recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

  4. Gene Set-Based Functionome Analysis of Pathogenesis in Epithelial Ovarian Serous Carcinoma and the Molecular Features in Different FIGO Stages

    Directory of Open Access Journals (Sweden)

    Chia-Ming Chang

    2016-06-01

    Full Text Available Serous carcinoma (SC is the most common subtype of epithelial ovarian carcinoma and is divided into four stages by the Federation of Gynecologists and Obstetrics (FIGO staging system. Currently, the molecular functions and biological processes of SC at different FIGO stages have not been quantified. Here, we conducted a whole-genome integrative analysis to investigate the functions of SC at different stages. The function, as defined by the GO term or canonical pathway gene set, was quantified by measuring the changes in the gene expressional order between cancerous and normal control states. The quantified function, i.e., the gene set regularity (GSR index, was utilized to investigate the pathogenesis and functional regulation of SC at different FIGO stages. We showed that the informativeness of the GSR indices was sufficient for accurate pattern recognition and classification for machine learning. The function regularity presented by the GSR indices showed stepwise deterioration during SC progression from FIGO stage I to stage IV. The pathogenesis of SC was centered on cell cycle deregulation and accompanied with multiple functional aberrations as well as their interactions.

  5. Molecular recognition in a diverse set of protein-ligand interactions studied with molecular dynamics simulations and end-point free energy calculations.

    Science.gov (United States)

    Wang, Bo; Li, Liwei; Hurley, Thomas D; Meroueh, Samy O

    2013-10-28

    End-point free energy calculations using MM-GBSA and MM-PBSA provide a detailed understanding of molecular recognition in protein-ligand interactions. The binding free energy can be used to rank-order protein-ligand structures in virtual screening for compound or target identification. Here, we carry out free energy calculations for a diverse set of 11 proteins bound to 14 small molecules using extensive explicit-solvent MD simulations. The structure of these complexes was previously solved by crystallography and their binding studied with isothermal titration calorimetry (ITC) data enabling direct comparison to the MM-GBSA and MM-PBSA calculations. Four MM-GBSA and three MM-PBSA calculations reproduced the ITC free energy within 1 kcal·mol(-1) highlighting the challenges in reproducing the absolute free energy from end-point free energy calculations. MM-GBSA exhibited better rank-ordering with a Spearman ρ of 0.68 compared to 0.40 for MM-PBSA with dielectric constant (ε = 1). An increase in ε resulted in significantly better rank-ordering for MM-PBSA (ρ = 0.91 for ε = 10), but larger ε significantly reduced the contributions of electrostatics, suggesting that the improvement is due to the nonpolar and entropy components, rather than a better representation of the electrostatics. The SVRKB scoring function applied to MD snapshots resulted in excellent rank-ordering (ρ = 0.81). Calculations of the configurational entropy using normal-mode analysis led to free energies that correlated significantly better to the ITC free energy than the MD-based quasi-harmonic approach, but the computed entropies showed no correlation with the ITC entropy. When the adaptation energy is taken into consideration by running separate simulations for complex, apo, and ligand (MM-PBSAADAPT), there is less agreement with the ITC data for the individual free energies, but remarkably good rank-ordering is observed (ρ = 0.89). Interestingly, filtering MD snapshots by prescoring

  6. MGRA: Motion Gesture Recognition via Accelerometer

    Directory of Open Access Journals (Sweden)

    Feng Hong

    2016-04-01

    Full Text Available Accelerometers have been widely embedded in most current mobile devices, enabling easy and intuitive operations. This paper proposes a Motion Gesture Recognition system (MGRA based on accelerometer data only, which is entirely implemented on mobile devices and can provide users with real-time interactions. A robust and unique feature set is enumerated through the time domain, the frequency domain and singular value decomposition analysis using our motion gesture set containing 11,110 traces. The best feature vector for classification is selected, taking both static and mobile scenarios into consideration. MGRA exploits support vector machine as the classifier with the best feature vector. Evaluations confirm that MGRA can accommodate a broad set of gesture variations within each class, including execution time, amplitude and non-gestural movement. Extensive evaluations confirm that MGRA achieves higher accuracy under both static and mobile scenarios and costs less computation time and energy on an LG Nexus 5 than previous methods.

  7. Challenging ocular image recognition

    Science.gov (United States)

    Pauca, V. Paúl; Forkin, Michael; Xu, Xiao; Plemmons, Robert; Ross, Arun A.

    2011-06-01

    Ocular recognition is a new area of biometric investigation targeted at overcoming the limitations of iris recognition performance in the presence of non-ideal data. There are several advantages for increasing the area beyond the iris, yet there are also key issues that must be addressed such as size of the ocular region, factors affecting performance, and appropriate corpora to study these factors in isolation. In this paper, we explore and identify some of these issues with the goal of better defining parameters for ocular recognition. An empirical study is performed where iris recognition methods are contrasted with texture and point operators on existing iris and face datasets. The experimental results show a dramatic recognition performance gain when additional features are considered in the presence of poor quality iris data, offering strong evidence for extending interest beyond the iris. The experiments also highlight the need for the direct collection of additional ocular imagery.

  8. Rapid staining and imaging of subnuclear features to differentiate between malignant and benign breast tissues at a point-of-care setting.

    Science.gov (United States)

    Mueller, Jenna L; Gallagher, Jennifer E; Chitalia, Rhea; Krieger, Marlee; Erkanli, Alaattin; Willett, Rebecca M; Geradts, Joseph; Ramanujam, Nimmi

    2016-07-01

    Histopathology is the clinical standard for tissue diagnosis; however, it requires tissue processing, laboratory personnel and infrastructure, and a highly trained pathologist to diagnose the tissue. Optical microscopy can provide real-time diagnosis, which could be used to inform the management of breast cancer. The goal of this work is to obtain images of tissue morphology through fluorescence microscopy and vital fluorescent stains and to develop a strategy to segment and quantify breast tissue features in order to enable automated tissue diagnosis. We combined acriflavine staining, fluorescence microscopy, and a technique called sparse component analysis to segment nuclei and nucleoli, which are collectively referred to as acriflavine positive features (APFs). A series of variables, which included the density, area fraction, diameter, and spacing of APFs, were quantified from images taken from clinical core needle breast biopsies and used to create a multivariate classification model. The model was developed using a training data set and validated using an independent testing data set. The top performing classification model included the density and area fraction of smaller APFs (those less than 7 µm in diameter, which likely correspond to stained nucleoli).When applied to the independent testing set composed of 25 biopsy panels, the model achieved a sensitivity of 82 %, a specificity of 79 %, and an overall accuracy of 80 %. These results indicate that our quantitative microscopy toolbox is a potentially viable approach for detecting the presence of malignancy in clinical core needle breast biopsies.

  9. Application of pattern recognition techniques to the identification of aerospace acoustic sources

    Science.gov (United States)

    Fuller, Chris R.; Obrien, Walter F.; Cabell, Randolph H.

    1988-01-01

    A pattern recognition system was developed that successfully recognizes simulated spectra of five different types of transportation noise sources. The system generates hyperplanes during a training stage to separate the classes and correctly classify unknown patterns in classification mode. A feature selector in the system reduces a large number of features to a smaller optimal set, maximizing performance and minimizing computation.

  10. A Computer-Aided Diagnosis System for Dynamic Contrast-Enhanced MR Images Based on Level Set Segmentation and ReliefF Feature Selection

    Directory of Open Access Journals (Sweden)

    Zhiyong Pang

    2015-01-01

    Full Text Available This study established a fully automated computer-aided diagnosis (CAD system for the classification of malignant and benign masses via breast magnetic resonance imaging (BMRI. A breast segmentation method consisting of a preprocessing step to identify the air-breast interfacing boundary and curve fitting for chest wall line (CWL segmentation was included in the proposed CAD system. The Chan-Vese (CV model level set (LS segmentation method was adopted to segment breast mass and demonstrated sufficiently good segmentation performance. The support vector machine (SVM classifier with ReliefF feature selection was used to merge the extracted morphological and texture features into a classification score. The accuracy, sensitivity, and specificity measurements for the leave-half-case-out resampling method were 92.3%, 98.2%, and 76.2%, respectively. For the leave-one-case-out resampling method, the measurements were 90.0%, 98.7%, and 73.8%, respectively.

  11. Face recognition using Krawtchouk moment

    Indian Academy of Sciences (India)

    Abstract. Feature extraction is one of the important tasks in face recognition. Moments are widely used feature extractor due to their superior discriminatory power and geometrical invariance. Moments generally capture the global features of the image. This paper proposes Krawtchouk moment for feature extraction in face ...

  12. Public domain optical character recognition

    Science.gov (United States)

    Garris, Michael D.; Blue, James L.; Candela, Gerald T.; Dimmick, Darrin L.; Geist, Jon C.; Grother, Patrick J.; Janet, Stanley A.; Wilson, Charles L.

    1995-03-01

    A public domain document processing system has been developed by the National Institute of Standards and Technology (NIST). The system is a standard reference form-based handprint recognition system for evaluating optical character recognition (OCR), and it is intended to provide a baseline of performance on an open application. The system's source code, training data, performance assessment tools, and type of forms processed are all publicly available. The system recognizes the handprint entered on handwriting sample forms like the ones distributed with NIST Special Database 1. From these forms, the system reads hand-printed numeric fields, upper and lowercase alphabetic fields, and unconstrained text paragraphs comprised of words from a limited-size dictionary. The modular design of the system makes it useful for component evaluation and comparison, training and testing set validation, and multiple system voting schemes. The system contains a number of significant contributions to OCR technology, including an optimized probabilistic neural network (PNN) classifier that operates a factor of 20 times faster than traditional software implementations of the algorithm. The source code for the recognition system is written in C and is organized into 11 libraries. In all, there are approximately 19,000 lines of code supporting more than 550 subroutines. Source code is provided for form registration, form removal, field isolation, field segmentation, character normalization, feature extraction, character classification, and dictionary-based postprocessing. The recognition system has been successfully compiled and tested on a host of UNIX workstations. This paper gives an overview of the recognition system's software architecture, including descriptions of the various system components along with timing and accuracy statistics.

  13. Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface.

    Science.gov (United States)

    Siuly; Li, Yan; Paul Wen, Peng

    2014-03-01

    Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  14. Human Face Recognition Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Răzvan-Daniel Albu

    2009-10-01

    Full Text Available In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. The convolutional network extracts successively larger features in a hierarchical set of layers. With the weights of the trained neural networks there are created kernel windows used for feature extraction in a 3-stage algorithm. I present experimental results illustrating the efficiency of the proposed approach. I use a database of 796 images of 159 individuals from Reims University which contains quite a high degree of variability in expression, pose, and facial details.

  15. Hidden Conditional Neural Fields for Continuous Phoneme Speech Recognition

    Science.gov (United States)

    Fujii, Yasuhisa; Yamamoto, Kazumasa; Nakagawa, Seiichi

    In this paper, we propose Hidden Conditional Neural Fields (HCNF) for continuous phoneme speech recognition, which are a combination of Hidden Conditional Random Fields (HCRF) and a Multi-Layer Perceptron (MLP), and inherit their merits, namely, the discriminative property for sequences from HCRF and the ability to extract non-linear features from an MLP. HCNF can incorporate many types of features from which non-linear features can be extracted, and is trained by sequential criteria. We first present the formulation of HCNF and then examine three methods to further improve automatic speech recognition using HCNF, which is an objective function that explicitly considers training errors, provides a hierarchical tandem-style feature and includes a deep non-linear feature extractor for the observation function. We show that HCNF can be trained realistically without any initial model and outperforms HCRF and the triphone hidden Markov model trained by the minimum phone error (MPE) manner using experimental results for continuous English phoneme recognition on the TIMIT core test set and Japanese phoneme recognition on the IPA 100 test set.

  16. Behavioral model of visual perception and recognition

    Science.gov (United States)

    Rybak, Ilya A.; Golovan, Alexander V.; Gusakova, Valentina I.

    1993-09-01

    In the processes of visual perception and recognition human eyes actively select essential information by way of successive fixations at the most informative points of the image. A behavioral program defining a scanpath of the image is formed at the stage of learning (object memorizing) and consists of sequential motor actions, which are shifts of attention from one to another point of fixation, and sensory signals expected to arrive in response to each shift of attention. In the modern view of the problem, invariant object recognition is provided by the following: (1) separated processing of `what' (object features) and `where' (spatial features) information at high levels of the visual system; (2) mechanisms of visual attention using `where' information; (3) representation of `what' information in an object-based frame of reference (OFR). However, most recent models of vision based on OFR have demonstrated the ability of invariant recognition of only simple objects like letters or binary objects without background, i.e. objects to which a frame of reference is easily attached. In contrast, we use not OFR, but a feature-based frame of reference (FFR), connected with the basic feature (edge) at the fixation point. This has provided for our model, the ability for invariant representation of complex objects in gray-level images, but demands realization of behavioral aspects of vision described above. The developed model contains a neural network subsystem of low-level vision which extracts a set of primary features (edges) in each fixation, and high- level subsystem consisting of `what' (Sensory Memory) and `where' (Motor Memory) modules. The resolution of primary features extraction decreases with distances from the point of fixation. FFR provides both the invariant representation of object features in Sensor Memory and shifts of attention in Motor Memory. Object recognition consists in successive recall (from Motor Memory) and execution of shifts of attention and

  17. Artificial intelligence tools for pattern recognition

    Science.gov (United States)

    Acevedo, Elena; Acevedo, Antonio; Felipe, Federico; Avilés, Pedro

    2017-06-01

    In this work, we present a system for pattern recognition that combines the power of genetic algorithms for solving problems and the efficiency of the morphological associative memories. We use a set of 48 tire prints divided into 8 brands of tires. The images have dimensions of 200 x 200 pixels. We applied Hough transform to obtain lines as main features. The number of lines obtained is 449. The genetic algorithm reduces the number of features to ten suitable lines that give thus the 100% of recognition. Morphological associative memories were used as evaluation function. The selection algorithms were Tournament and Roulette wheel. For reproduction, we applied one-point, two-point and uniform crossover.

  18. Intestinal T-cell lymphoma with enteropathy-associated T-cell lymphoma-like features arising in the setting of adult autoimmune enteropathy.

    Science.gov (United States)

    Ciccocioppo, Rachele; Croci, Giorgio A; Biagi, Federico; Vanoli, Alessandro; Alvisi, Costanza; Cavenaghi, Giorgio; Riboni, Roberta; Arra, Mariarosa; Gobbi, Paolo G; Paulli, Marco; Corazza, Gino R

    2018-02-14

    Enteropathy-associated T-cell lymphoma is regarded as a dismal, late complication of coeliac disease, though a single case of T-cell lymphoma with such features arising in the setting of autoimmune enteropathy of the adult has been reported to date. We aim to describe the case of a 41-year-old woman complaining of severe malabsorption syndrome, who was diagnosed with autoimmune enteropathy based on the presence of flat intestinal mucosa unresponsive to any dietary restriction and positivity for enterocyte autoantibodies. Steroid therapy led to a complete recovery of both mucosal and clinical findings over 12 years, when disease relapse was accompanied by the appearance of monoclonal rearrangement of T-cell receptor-γ and peculiar T-cell phenotypic abnormalities, leading to a rapid transition to an overt T-cell lymphoma with features of the enteropathy-associated subtype. Despite intensive treatment, the patient developed cerebral metastasis and died 9 months later. Our case enhances the concept of enteropathy-associated T-cell lymphoma as a disease that may arise in the setting of enteropathies other than coeliac disease, thus representing a heterogeneous entity. Moreover, our observations support the need of a close follow-up of these patients, coupled with comprehensive characterization of mucosal biopsies. Copyright © 2018 John Wiley & Sons, Ltd.

  19. Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection.

    Science.gov (United States)

    Prabusankarlal, Kadayanallur Mahadevan; Thirumoorthy, Palanisamy; Manavalan, Radhakrishnan

    2017-04-01

    A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions. A total of 34 features, including 29 textural and five morphological, are applied to a [Formula: see text]-fold cross-validation scheme, in which more relevant features are selected by quick-reduct algorithm, and the breast masses are discriminated into benign or malignant using SaDE-ELM classifier. The diagnosis accuracy of the system is assessed using parameters, such as accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), Matthew's correlation coefficient (MCC), and area ([Formula: see text]) under receiver operating characteristics curve. The performance of the proposed system is also compared with other classifiers, such as support vector machine and ELM. The results indicated that the proposed SaDE algorithm has superior performance with [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] compared to other classifiers.

  20. Development of Feature Set, Classification Implementation and Applications for Vowel Migration/Modification in Sung Filipino (Tagalog Texts and Perceived Intelligibility

    Directory of Open Access Journals (Sweden)

    Virginia B. Bustos

    2009-12-01

    Full Text Available With the emergence of research on real-time visual feedback to supplement vocal pedagogy, the utilization of technology in the world of music is now seen to accelerate skills learning and enhance cognitive development. The researchers of this project aim to further analyze vowel intelligibility and develop software applications intended to be used not only by professional singers but also by individuals who wish to improve their singing capability. Data in the form of sung vowels and song pieces were obtained from 46 singers. A Listening Test was then conducted on these samples to obtain the ground truth for vowel classification based on human perception. Simulation of the human auditory perception of sung Filipino vowels was performed using formant frequencies and Mel-frequency cepstral coefficients as feature vector inputs to a two-stage Discriminant Analysis classifier. The setup resulted in an over-all Training Set accuracy of 89.4% and an over-all Test Set accuracy of 90.9%. The accuracy of the classifier, measured in terms of the correspondence of vowel classifications obtained from the classifier with the results of the Listening Test, reached 92.3%. Using information obtained from the classifier, offline and online/real-time software applications were developed. The main application features include the display of the spectral envelope and spectrogram, pitch and vibrato analysis and direct feedback on the classification of the sung vowel. These features were recommended by singers who were surveyed and were incorporated in the applications to aid singers to adjust formant locations, directly determine listener’s perception of sung vowels, perform modeling effectively and carry out vowel migration.

  1. Feature Sets for Screenshot Detection

    Science.gov (United States)

    2013-06-01

    Machine Perception of Three-Dimensional Solids” in which he proposed what would become one of the first edge detection algorithms [16]. His algorithm...L. G. Roberts, “Machine perception of three-dimensional solids,” DTIC Document, Tech. Rep., 1963. [17] J. Canny, “A computational approach to edge...Jakubowicz, J.-M. Morel, and G. Randall, “ LSD : a Line Segment Detector,” Image Processing On Line, 2012. [24] A. Halder, N. Chatterjee, A. Kar, S. Pal, and S

  2. Recording small landscape features by object recognition : possibilities and limitations of automated procedures to support monitoring in the frame of the GeoCAP

    NARCIS (Netherlands)

    Krause, A.U.M.

    2011-01-01

    In compliance with EU Common Agricultural Policy (CAP), a digital Land Parcel Information System (LPIS) exists in The Netherlands. However, its content is basically limited to the primarily (net) agricultural area. So far landscape features defined by the CAP EC law were not yet included. In

  3. Pattern recognition experiments in the mandala/cosine domain.

    Science.gov (United States)

    Hsu, Y S; Prum, S; Kagel, J H; Andrews, H C

    1983-05-01

    The problem of recognition of objects in images is investigated from the simultaneous viewpoints of image bandwidth compression and automatic target recognition. A scenario is suggested in which recognition is implemented on features in the block cosine transform domain which is useful for data compression as well. While most image frames would be processed by the automatic recognition algorithms in the compressed domain without need for image reconstruction, this still allows for visual image classification of targets with poor recognition rates (by human viewing at the receiving terminal). It has been found that the Mandala sorting of the block cosine domain results in a more effective domain for selecting target identification parameters. Useful features from this Mandala/cosine domain are developed based upon correlation parameters and homogeneity measures which appear to successfully discriminate between natural and man-made objects. The Bhattacharyya feature discriminator is used to provide a 10:1 compression of the feature space for implementation of simple statistical decision surfaces (Gaussian and minimum distance classification). Imagery sensed in the visible spectra with a resolution of approximately 5-10 ft is used to illustrate the success of the technique on targets such as ships to be separated from clouds. A data set of 38 images is used for experimental verification with typical classification results ranging from the high 80's to low 90 percentile regions depending on the options choosen.

  4. Fast keypoint recognition using random ferns.

    Science.gov (United States)

    Ozuysal, Mustafa; Calonder, Michael; Lepetit, Vincent; Fua, Pascal

    2010-03-01

    While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well as the number of classes grows. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image data sets containing very significant perspective changes.

  5. Statistical modeling of speech Poincaré sections in combination of frequency analysis to improve speech recognition performance

    Science.gov (United States)

    Jafari, Ayyoob; Almasganj, Farshad; Bidhendi, Maryam Nabi

    2010-09-01

    This paper introduces a combinational feature extraction approach to improve speech recognition systems. The main idea is to simultaneously benefit from some features obtained from Poincaré section applied to speech reconstructed phase space (RPS) and typical Mel frequency cepstral coefficients (MFCCs) which have a proved role in speech recognition field. With an appropriate dimension, the reconstructed phase space of speech signal is assured to be topologically equivalent to the dynamics of the speech production system, and could therefore include information that may be absent in linear analysis approaches. Moreover, complicated systems such as speech production system can present cyclic and oscillatory patterns and Poincaré sections could be used as an effective tool in analysis of such trajectories. In this research, a statistical modeling approach based on Gaussian mixture models (GMMs) is applied to Poincaré sections of speech RPS. A final pruned feature set is obtained by applying an efficient feature selection approach to the combination of the parameters of the GMM model and MFCC-based features. A hidden Markov model-based speech recognition system and TIMIT speech database are used to evaluate the performance of the proposed feature set by conducting isolated and continuous speech recognition experiments. By the proposed feature set, 5.7% absolute isolated phoneme recognition improvement is obtained against only MFCC-based features.

  6. Deep Learning For Smile Recognition

    OpenAIRE

    Glauner, Patrick O.

    2016-01-01

    Inspired by recent successes of deep learning in computer vision, we propose a novel application of deep convolutional neural networks to facial expression recognition, in particular smile recognition. A smile recognition test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action (DISFA) database, significantly outperforming existing approaches based on hand-crafted features with accuracies ranging from 65.55% to 79.67%. The novelty of this approach includes a c...

  7. Robust face recognition algorithm for identifition of disaster victims

    Science.gov (United States)

    Gevaert, Wouter J. R.; de With, Peter H. N.

    2013-02-01

    We present a robust face recognition algorithm for the identification of occluded, injured and mutilated faces with a limited training set per person. In such cases, the conventional face recognition methods fall short due to specific aspects in the classification. The proposed algorithm involves recursive Principle Component Analysis for reconstruction of afiected facial parts, followed by a feature extractor based on Gabor wavelets and uniform multi-scale Local Binary Patterns. As a classifier, a Radial Basis Neural Network is employed. In terms of robustness to facial abnormalities, tests show that the proposed algorithm outperforms conventional face recognition algorithms like, the Eigenfaces approach, Local Binary Patterns and the Gabor magnitude method. To mimic real-life conditions in which the algorithm would have to operate, specific databases have been constructed and merged with partial existing databases and jointly compiled. Experiments on these particular databases show that the proposed algorithm achieves recognition rates beyond 95%.

  8. Facial Expression Recognition

    NARCIS (Netherlands)

    Pantic, Maja; Li, S.; Jain, A.

    2009-01-01

    Facial expression recognition is a process performed by humans or computers, which consists of: 1. Locating faces in the scene (e.g., in an image; this step is also referred to as face detection), 2. Extracting facial features from the detected face region (e.g., detecting the shape of facial

  9. Speech Recognition

    Directory of Open Access Journals (Sweden)

    Adrian Morariu

    2009-01-01

    Full Text Available This paper presents a method of speech recognition by pattern recognition techniques. Learning consists in determining the unique characteristics of a word (cepstral coefficients by eliminating those characteristics that are different from one word to another. For learning and recognition, the system will build a dictionary of words by determining the characteristics of each word to be used in the recognition. Determining the characteristics of an audio signal consists in the following steps: noise removal, sampling it, applying Hamming window, switching to frequency domain through Fourier transform, calculating the magnitude spectrum, filtering data, determining cepstral coefficients.

  10. Word recognition in a segmentation-free approach to OCR

    Science.gov (United States)

    Mulgaonkar, Prasanna G.; Chen, Chien-Huei; DeCurtins, Jeff L.

    1994-02-01

    Segmentation is a key step in current OCR systems. It has been estimated that half the errors in character recognition are due to segmentation. We have developed a novel approach that performs OCR without the segmentation step. The approach starts by extracting significant geometric features from the input document image of the page. Each feature then `votes' for the character that could have generated that feature. Thus, even if some of the features are occluded or lost due to degradation, the remaining features can successfully identify the character. In extreme case, the degradation may be severe enough to prevent recognition of some of the characters in a word. In such cases, we use a lexicon-based word recognition technique to resolve ambiguity. Inexact matching and probabilistic evaluation used in the technique allow us to identify the correct word, by detecting a partial set of characters. This paper first presents an overview of our segmentation-free OCR system and then focuses on the word-recognition technique. Preliminary experimental results show that this is a very promising approach.

  11. Model-Based Comparison of Deep Brain Stimulation Array Functionality with Varying Number of Radial Electrodes and Machine Learning Feature Sets

    Science.gov (United States)

    Teplitzky, Benjamin A.; Zitella, Laura M.; Xiao, YiZi; Johnson, Matthew D.

    2016-01-01

    Deep brain stimulation (DBS) leads with radially distributed electrodes have potential to improve clinical outcomes through more selective targeting of pathways and networks within the brain. However, increasing the number of electrodes on clinical DBS leads by replacing conventional cylindrical shell electrodes with radially distributed electrodes raises practical design and stimulation programming challenges. We used computational modeling to investigate: (1) how the number of radial electrodes impact the ability to steer, shift, and sculpt a region of neural activation (RoA), and (2) which RoA features are best used in combination with machine learning classifiers to predict programming settings to target a particular area near the lead. Stimulation configurations were modeled using 27 lead designs with one to nine radially distributed electrodes. The computational modeling framework consisted of a three-dimensional finite element tissue conductance model in combination with a multi-compartment biophysical axon model. For each lead design, two-dimensional threshold-dependent RoAs were calculated from the computational modeling results. The models showed more radial electrodes enabled finer resolution RoA steering; however, stimulation amplitude, and therefore spatial extent of the RoA, was limited by charge injection and charge storage capacity constraints due to the small electrode surface area for leads with more than four radially distributed electrodes. RoA shifting resolution was improved by the addition of radial electrodes when using uniform multi-cathode stimulation, but non-uniform multi-cathode stimulation produced equivalent or better resolution shifting without increasing the number of radial electrodes. Robust machine learning classification of 15 monopolar stimulation configurations was achieved using as few as three geometric features describing a RoA. The results of this study indicate that, for a clinical-scale DBS lead, more than four radial

  12. Optimizing text-independent speaker recognition using an LSTM neural network

    OpenAIRE

    Larsson, Joel

    2014-01-01

    In this paper a novel speaker recognition system is introduced. Automated speaker recognition has become increasingly popular to aid in crime investigations and authorization processes with the advances in computer science. Here, a recurrent neural network approach is used to learn to identify ten speakers within a set of 21 audio books. Audio signals are processed via spectral analysis into Mel Frequency Cepstral Coefficients that serve as speaker specific features, which are input to the ne...

  13. Towards a smart glove: arousal recognition based on textile Electrodermal Response.

    Science.gov (United States)

    Valenza, Gaetano; Lanata, Antonio; Scilingo, Enzo Pasquale; De Rossi, Danilo

    2010-01-01

    This paper investigates the possibility of using Electrodermal Response, acquired by a sensing fabric glove with embedded textile electrodes, as reliable means for emotion recognition. Here, all the essential steps for an automatic recognition system are described, from the recording of physiological data set to a feature-based multiclass classification. Data were collected from 35 healthy volunteers during arousal elicitation by means of International Affective Picture System (IAPS) pictures. Experimental results show high discrimination after twenty steps of cross validation.

  14. A training program to enhance recognition of depression in nursing homes, assisted living, and other long-term care settings: Description and evaluation.

    Science.gov (United States)

    Abrams, Robert C; Nathanson, Mark; Silver, Stephanie; Ramirez, Mildred; Toner, John A; Teresi, Jeanne A

    2017-01-01

    Low levels of symptom recognition by staff have been "gateway" barriers to the management of depression in long-term care. The study aims were to refine a depression training program for front-line staff in long-term care and provide evaluative knowledge outcome data. Three primary training modules provide an overview of depression symptoms; a review of causes and situational and environmental contributing factors; and communication strategies, medications, and clinical treatment strategies. McNemar's chi-square tests and paired t-tests were used to examine change in knowledge. Data were analyzed for up to 143 staff members, the majority from nursing. Significant changes (p depressive disorder.

  15. Action and gait recognition from recovered 3-D human joints.

    Science.gov (United States)

    Gu, Junxia; Ding, Xiaoqing; Wang, Shengjin; Wu, Youshou

    2010-08-01

    A common viewpoint-free framework that fuses pose recovery and classification for action and gait recognition is presented in this paper. First, a markerless pose recovery method is adopted to automatically capture the 3-D human joint and pose parameter sequences from volume data. Second, multiple configuration features (combination of joints) and movement features (position, orientation, and height of the body) are extracted from the recovered 3-D human joint and pose parameter sequences. A hidden Markov model (HMM) and an exemplar-based HMM are then used to model the movement features and configuration features, respectively. Finally, actions are classified by a hierarchical classifier that fuses the movement features and the configuration features, and persons are recognized from their gait sequences with the configuration features. The effectiveness of the proposed approach is demonstrated with experiments on the Institut National de Recherche en Informatique et Automatique Xmas Motion Acquisition Sequences data set.

  16. Portal vein branching order helps in the recognition of anomalous right-sided round ligament: common features and variations in portal vein anatomy.

    Science.gov (United States)

    Yamashita, Rikiya; Yamaoka, Toshihide; Nishitai, Ryuta; Isoda, Hiroyoshi; Taura, Kojiro; Arizono, Shigeki; Furuta, Akihiro; Ohno, Tsuyoshi; Ono, Ayako; Togashi, Kaori

    2017-07-01

    This study aimed to evaluate the common features and variations of portal vein anatomy in right-sided round ligament (RSRL), which can help propose a method to detect and diagnose this anomaly. In this retrospective study of 14 patients with RSRL, the branching order of the portal tree was analyzed, with special focus on the relationship between the dorsal branch of the right anterior segmental portal vein (P A-D ) and the lateral segmental portal vein (P LL ), to determine the common features. The configuration of the portal vein from the main portal trunk to the right umbilical portion (RUP), the inclination of the RUP, and the number and thickness of the ramifications branching from the right anterior segmental portal vein (P A ) were evaluated for variations. In all subjects, the diverging point of the P A-D was constantly distal to that of the P LL . The portal vein configuration was I- and Z-shaped in nine and five subjects, respectively. The RUP was tilted to the right in all subjects. In Z-shaped subjects, the portal trunk between the branching point of the right posterior segmental portal vein and that of the P LL was tilted to the left in one subject and was almost parallel to the vertical plane in four subjects. Multiple ramifications were radially distributed from the P A in eight subjects, whereas one predominant P A-D branched from the P A in six subjects. Based on the diverging points of the P A-D and P LL , we proposed a three-step method for the detection and diagnosis of RSRL.

  17. Arabic Handwritten Word Recognition Using HMMs with Explicit State Duration

    Directory of Open Access Journals (Sweden)

    M. Sellami

    2008-05-01

    Full Text Available We describe an offline unconstrained Arabic handwritten word recognition system based on segmentation-free approach and discrete hidden Markov models (HMMs with explicit state duration. Character durations play a significant part in the recognition of cursive handwriting. The duration information is still mostly disregarded in HMM-based automatic cursive handwriting recognizers due to the fact that HMMs are deficient in modeling character durations properly. We will show experimentally that explicit state duration modeling in the HMM framework can significantly improve the discriminating capacity of the HMMs to deal with very difficult pattern recognition tasks such as unconstrained Arabic handwriting recognition. In order to carry out the letter and word model training and recognition more efficiently, we propose a new version of the Viterbi algorithm taking into account explicit state duration modeling. Three distributions (Gamma, Gauss, and Poisson for the explicit state duration modeling have been used, and a comparison between them has been reported. To perform word recognition, the described system uses an original sliding window approach based on vertical projection histogram analysis of the word and extracts a new pertinent set of statistical and structural features from the word image. Several experiments have been performed using the IFN/ENIT benchmark database and the best recognition performances achieved by our system outperform those reported recently on the same database.

  18. Parcels and Land Ownership, Blocks-The data set is a polygon feature consisting of 212 polygons representing city block boundaries. It was created to maintain land ownership., Published in 2008, Davis County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Parcels and Land Ownership dataset current as of 2008. Blocks-The data set is a polygon feature consisting of 212 polygons representing city block boundaries. It was...

  19. Hydrography, HydroBndy-The data set is a line feature containing representing the outline ponds and small reservoirs. It consists of more than 150 lines representing natural and engineered surface water bodies., Published in 2005, Davis County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Hydrography dataset current as of 2005. HydroBndy-The data set is a line feature containing representing the outline ponds and small reservoirs. It consists of more...

  20. Election Districts and Precincts, PrecinctBndy-The data set is a line feature consisting of 635 line strings representing voter precinct boundaries., Published in 1991, Davis County Government.

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Election Districts and Precincts dataset current as of 1991. PrecinctBndy-The data set is a line feature consisting of 635 line strings representing voter precinct...