Retinal Image Preprocessing: Background and Noise Segmentation

Directory of Open Access Journals (Sweden)

Usman Akram

2012-09-01

Full Text Available Retinal images are used for the automated screening and diagnosis of diabetic retinopathy. The retinal image quality must be improved for the detection of features and abnormalities and for this purpose preprocessing of retinal images is vital. In this paper, we present a novel automated approach for preprocessing of colored retinal images. The proposed technique improves the quality of input retinal image by separating the background and noisy area from the overall image. It contains coarse segmentation and fine segmentation. Standard retinal images databases Diaretdb0, Diaretdb1, DRIVE and STARE are used to test the validation of our preprocessing technique. The experimental results show the validity of proposed preprocessing technique.

Multiple-Fault Diagnosis Method Based on Multiscale Feature Extraction and MSVM_PPA

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Min Zhang

2018-01-01

Full Text Available Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is still a challenge in fault diagnosis. In this paper, a novel method called multiscale feature extraction (MFE and multiclass support vector machine (MSVM with particle parameter adaptive (PPA is proposed. MFE is used to preprocess the process signals, which decomposes the data into intrinsic mode function by empirical mode decomposition method, and instantaneous frequency of decomposed components was obtained by Hilbert transformation. Then, statistical features and principal component analysis are utilized to extract significant information from the features, to get effective data from multiple faults. MSVM method with PPA parameters optimization will classify the fault patterns. The results of a case study of the rolling bearings faults data from Case Western Reserve University show that (1 the proposed intelligent method (MFE_PPA_MSVM improves the classification recognition rate; (2 the accuracy will decline when the number of fault patterns increases; (3 prediction accuracy can be the best when the training set size is increased to 70% of the total sample set. It verifies the method is feasible and efficient for fault diagnosis.

Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality

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Fang Yang

2017-01-01

Full Text Available Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and K-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer.

LOCAL LINE BINARY PATTERN FOR FEATURE EXTRACTION ON PALM VEIN RECOGNITION

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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

Reliable RANSAC Using a Novel Preprocessing Model

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Xiaoyan Wang

2013-01-01

Full Text Available Geometric assumption and verification with RANSAC has become a crucial step for corresponding to local features due to its wide applications in biomedical feature analysis and vision computing. However, conventional RANSAC is very time-consuming due to redundant sampling times, especially dealing with the case of numerous matching pairs. This paper presents a novel preprocessing model to explore a reduced set with reliable correspondences from initial matching dataset. Both geometric model generation and verification are carried out on this reduced set, which leads to considerable speedups. Afterwards, this paper proposes a reliable RANSAC framework using preprocessing model, which was implemented and verified using Harris and SIFT features, respectively. Compared with traditional RANSAC, experimental results show that our method is more efficient.

The Effect of Preprocessing on Arabic Document Categorization

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Abdullah Ayedh

2016-04-01

Full Text Available Preprocessing is one of the main components in a conventional document categorization (DC framework. This paper aims to highlight the effect of preprocessing tasks on the efficiency of the Arabic DC system. In this study, three classification techniques are used, namely, naive Bayes (NB, k-nearest neighbor (KNN, and support vector machine (SVM. Experimental analysis on Arabic datasets reveals that preprocessing techniques have a significant impact on the classification accuracy, especially with complicated morphological structure of the Arabic language. Choosing appropriate combinations of preprocessing tasks provides significant improvement on the accuracy of document categorization depending on the feature size and classification techniques. Findings of this study show that the SVM technique has outperformed the KNN and NB techniques. The SVM technique achieved 96.74% micro-F1 value by using the combination of normalization and stemming as preprocessing tasks.

A survey of visual preprocessing and shape representation techniques

Science.gov (United States)

Olshausen, Bruno A.

1988-01-01

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention).

Feature Level Two -Dimensional Arrays Based Fusion in the Personal Authentication system using Physiological Biometric traits

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Jerusalin Carol .J

Full Text Available ABSTRACT The fingerprint, knuckle print and the retina are used to authenticate a person accurately because of the permanence in the features. These three biometric traits are fused for better security. The fingerprint and knuckle print images are pre-processed by morphological techniques and the features are extracted from the normalized image using gabor filter. The retinal image is converted to gray image and pre-processing is done using top hat and bottom hat filtering. Blood vessels are segmented and the features are extracted by locating the optic disk as the centre point. The extracted features from the fingerprint, knuckle print and the retina are fused together as one template and stored in the data base for authentication purpose, thus reducing the space and time complexity.

FEATURE EXTRACTION BASED WAVELET TRANSFORM IN BREAST CANCER DIAGNOSIS USING FUZZY AND NON-FUZZY CLASSIFICATION

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Pelin GORGEL

2013-01-01

Full Text Available This study helps to provide a second eye to the expert radiologists for the classification of manually extracted breast masses taken from 60 digital mammıgrams. These mammograms have been acquired from Istanbul University Faculty of Medicine Hospital and have 78 masses. The diagnosis is implemented with pre-processing by using feature extraction based Fast Wavelet Transform (FWT. Afterwards Adaptive Neuro-Fuzzy Inference System (ANFIS based fuzzy subtractive clustering and Support Vector Machines (SVM methods are used for the classification. It is a comparative study which uses these methods respectively. According to the results of the study, ANFIS based subtractive clustering produces ??% while SVM produces ??% accuracy in malignant-benign classification. The results demonstrate that the developed system could help the radiologists for a true diagnosis and decrease the number of the missing cancerous regions or unnecessary biopsies.

Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm.

Science.gov (United States)

Sinha, S K; Karray, F

2002-01-01

Pipeline surface defects such as holes and cracks cause major problems for utility managers, particularly when the pipeline is buried under the ground. Manual inspection for surface defects in the pipeline has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer utility managers an opportunity to significantly improve quality and reduce costs. A recognition and classification of pipe cracks using images analysis and neuro-fuzzy algorithm is proposed. In the preprocessing step the scanned images of pipe are analyzed and crack features are extracted. In the classification step the neuro-fuzzy algorithm is developed that employs a fuzzy membership function and error backpropagation algorithm. The idea behind the proposed approach is that the fuzzy membership function will absorb variation of feature values and the backpropagation network, with its learning ability, will show good classification efficiency.

Feature Extraction For Application of Heart Abnormalities Detection Through Iris Based on Mobile Devices

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Entin Martiana Kusumaningtyas

2018-01-01

Full Text Available As the WHO says, heart disease is the leading cause of death and examining it by current methods in hospitals is not cheap. Iridology is one of the most popular alternative ways to detect the condition of organs. Iridology is the science that enables a health practitioner or non-expert to study signs in the iris that are capable of showing abnormalities in the body, including basic genetics, toxin deposition, circulation of dams, and other weaknesses. Research on computer iridology has been done before. One is about the computer's iridology system to detect heart conditions. There are several stages such as capture eye base on target, pre-processing, cropping, segmentation, feature extraction and classification using Thresholding algorithms. In this study, feature extraction process performed using binarization method by transforming the image into black and white. In this process we compare the two approaches of binarization method, binarization based on grayscale images and binarization based on proximity. The system we proposed was tested at Mugi Barokah Clinic Surabaya.  We conclude that the image grayscale approach performs better classification than using proximity.

Historical feature pattern extraction based network attack situation sensing algorithm.

Science.gov (United States)

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

Directory of Open Access Journals (Sweden)

Yong Zeng

2014-01-01

Full Text Available The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE. First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

Research on pre-processing of QR Code

Science.gov (United States)

Sun, Haixing; Xia, Haojie; Dong, Ning

2013-10-01

QR code encodes many kinds of information because of its advantages: large storage capacity, high reliability, full arrange of utter-high-speed reading, small printing size and high-efficient representation of Chinese characters, etc. In order to obtain the clearer binarization image from complex background, and improve the recognition rate of QR code, this paper researches on pre-processing methods of QR code (Quick Response Code), and shows algorithms and results of image pre-processing for QR code recognition. Improve the conventional method by changing the Souvola's adaptive text recognition method. Additionally, introduce the QR code Extraction which adapts to different image size, flexible image correction approach, and improve the efficiency and accuracy of QR code image processing.

THE EFFECT OF IMAGE ENHANCEMENT METHODS DURING FEATURE DETECTION AND MATCHING OF THERMAL IMAGES

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O. Akcay

2017-05-01

Full Text Available A successful image matching is essential to provide an automatic photogrammetric process accurately. Feature detection, extraction and matching algorithms have performed on the high resolution images perfectly. However, images of cameras, which are equipped with low-resolution thermal sensors are problematic with the current algorithms. In this paper, some digital image processing techniques were applied to the low-resolution images taken with Optris PI 450 382 x 288 pixel optical resolution lightweight thermal camera to increase extraction and matching performance. Image enhancement methods that adjust low quality digital thermal images, were used to produce more suitable images for detection and extraction. Three main digital image process techniques: histogram equalization, high pass and low pass filters were considered to increase the signal-to-noise ratio, sharpen image, remove noise, respectively. Later on, the pre-processed images were evaluated using current image detection and feature extraction methods Maximally Stable Extremal Regions (MSER and Speeded Up Robust Features (SURF algorithms. Obtained results showed that some enhancement methods increased number of extracted features and decreased blunder errors during image matching. Consequently, the effects of different pre-process techniques were compared in the paper.

An Application for Data Preprocessing and Models Extractions in Web Usage Mining

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Claudia Elena DINUCA

2011-11-01

Full Text Available Web servers worldwide generate a vast amount of information on web users’ browsing activities. Several researchers have studied these so-called clickstream or web access log data to better understand and characterize web users. The goal of this application is to analyze user behaviour by mining enriched web access log data. With the continued growth and proliferation of e-commerce, Web services, and Web-based information systems, the volumes of click stream and user data collected by Web-based organizations in their daily operations has reached astronomical proportions. This information can be exploited in various ways, such as enhancing the effectiveness of websites or developing directed web marketing campaigns. The discovered patterns are usually represented as collections of pages, objects, or re-sources that are frequently accessed by groups of users with common needs or interests. In this paper we will focus on displaying the way how it was implemented the application for data preprocessing and extracting different data models from web logs data, finding association as a data mining technique to extract potentially useful knowledge from web usage data. We find different data models navigation patterns by analysing the log files of the web-site. I implemented the application in Java using NetBeans IDE. For exemplification, I used the log files data from a commercial web site www.nice-layouts.com.

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters.

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Brynolfsson, Patrik; Nilsson, David; Torheim, Turid; Asklund, Thomas; Karlsson, Camilla Thellenberg; Trygg, Johan; Nyholm, Tufve; Garpebring, Anders

2017-06-22

In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain

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Michael Rzanny

2017-11-01

Full Text Available Abstract Background Automated species identification is a long term research subject. Contrary to flowers and fruits, leaves are available throughout most of the year. Offering margin and texture to characterize a species, they are the most studied organ for automated identification. Substantially matured machine learning techniques generate the need for more training data (aka leaf images. Researchers as well as enthusiasts miss guidance on how to acquire suitable training images in an efficient way. Methods In this paper, we systematically study nine image types and three preprocessing strategies. Image types vary in terms of in-situ image recording conditions: perspective, illumination, and background, while the preprocessing strategies compare non-preprocessed, cropped, and segmented images to each other. Per image type-preprocessing combination, we also quantify the manual effort required for their implementation. We extract image features using a convolutional neural network, classify species using the resulting feature vectors and discuss classification accuracy in relation to the required effort per combination. Results The most effective, non-destructive way to record herbaceous leaves is to take an image of the leaf’s top side. We yield the highest classification accuracy using destructive back light images, i.e., holding the plucked leaf against the sky for image acquisition. Cropping the image to the leaf’s boundary substantially improves accuracy, while precise segmentation yields similar accuracy at a substantially higher effort. The permanent use or disuse of a flash light has negligible effects. Imaging the typically stronger textured backside of a leaf does not result in higher accuracy, but notably increases the acquisition cost. Conclusions In conclusion, the way in which leaf images are acquired and preprocessed does have a substantial effect on the accuracy of the classifier trained on them. For the first time, this

Iris recognition based on key image feature extraction.

Science.gov (United States)

Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

2008-01-01

In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

Extraction of ECG signal with adaptive filter for hearth abnormalities detection

Science.gov (United States)

Turnip, Mardi; Saragih, Rijois. I. E.; Dharma, Abdi; Esti Kusumandari, Dwi; Turnip, Arjon; Sitanggang, Delima; Aisyah, Siti

2018-04-01

This paper demonstrates an adaptive filter method for extraction ofelectrocardiogram (ECG) feature in hearth abnormalities detection. In particular, electrocardiogram (ECG) is a recording of the heart's electrical activity by capturing a tracingof cardiac electrical impulse as it moves from the atrium to the ventricles. The applied algorithm is to evaluate and analyze ECG signals for abnormalities detection based on P, Q, R and S peaks. In the first phase, the real-time ECG data is acquired and pre-processed. In the second phase, the procured ECG signal is subjected to feature extraction process. The extracted features detect abnormal peaks present in the waveform. Thus the normal and abnormal ECG signal could be differentiated based on the features extracted.

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

Science.gov (United States)

Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

2017-10-06

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

Biometric feature extraction using local fractal auto-correlation

International Nuclear Information System (INIS)

Chen Xi; Zhang Jia-Shu

2014-01-01

Image texture feature extraction is a classical means for biometric recognition. To extract effective texture feature for matching, we utilize local fractal auto-correlation to construct an effective image texture descriptor. Three main steps are involved in the proposed scheme: (i) using two-dimensional Gabor filter to extract the texture features of biometric images; (ii) calculating the local fractal dimension of Gabor feature under different orientations and scales using fractal auto-correlation algorithm; and (iii) linking the local fractal dimension of Gabor feature under different orientations and scales into a big vector for matching. Experiments and analyses show our proposed scheme is an efficient biometric feature extraction approach. (condensed matter: structural, mechanical, and thermal properties)

Extracting Information from Conventional AE Features for Fatigue Onset Damage Detection in Carbon Fiber Composites

DEFF Research Database (Denmark)

Unnthorsson, Runar; Pontoppidan, Niels Henrik Bohl; Jonsson, Magnus Thor

2005-01-01

We have analyzed simple data fusion and preprocessing methods on Acoustic Emission measurements of prosthetic feet made of carbon fiber reinforced composites. This paper presents the initial research steps; aiming at reducing the time spent on the fatigue test. With a simple single feature...... approaches can readily be investigated using the improved features, possibly improving the performance using multiple feature classifiers, e.g., Voting systems; Support Vector Machines and Gaussian Mixtures....

Text feature extraction based on deep learning: a review.

Science.gov (United States)

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

Enhancing facial features by using clear facial features

Science.gov (United States)

Rofoo, Fanar Fareed Hanna

2017-09-01

The similarity of features between individuals of same ethnicity motivated the idea of this project. The idea of this project is to extract features of clear facial image and impose them on blurred facial image of same ethnic origin as an approach to enhance a blurred facial image. A database of clear images containing 30 individuals equally divided to five different ethnicities which were Arab, African, Chines, European and Indian. Software was built to perform pre-processing on images in order to align the features of clear and blurred images. And the idea was to extract features of clear facial image or template built from clear facial images using wavelet transformation to impose them on blurred image by using reverse wavelet. The results of this approach did not come well as all the features did not align together as in most cases the eyes were aligned but the nose or mouth were not aligned. Then we decided in the next approach to deal with features separately but in the result in some cases a blocky effect was present on features due to not having close matching features. In general the available small database did not help to achieve the goal results, because of the number of available individuals. The color information and features similarity could be more investigated to achieve better results by having larger database as well as improving the process of enhancement by the availability of closer matches in each ethnicity.

Neural Online Filtering Based on Preprocessed Calorimeter Data

CERN Document Server

Torres, R C; The ATLAS collaboration; Simas Filho, E F; De Seixas, J M

2009-01-01

Among LHC detectors, ATLAS aims at coping with such high event rate by designing a three-level online triggering system. The first level trigger output will be ~75 kHz. This level will mark the regions where relevant events were found. The second level will validate LVL1 decision by looking only at the approved data using full granularity. At the level two output, the event rate will be reduced to ~2 kHz. Finally, the third level will look at full event information and a rate of ~200 Hz events is expected to be approved, and stored in persistent media for further offline analysis. Many interesting events decay into electrons, which have to be identified from the huge background noise (jets). This work proposes a high-efficient LVL2 electron / jet discrimination system based on neural networks fed from preprocessed calorimeter information. The feature extraction part of the proposed system performs a ring structure of data description. A set of concentric rings centered at the highest energy cell is generated ...

Classification of Textures Using Filter Based Local Feature Extraction

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Bocekci Veysel Gokhan

2016-01-01

Full Text Available In this work local features are used in feature extraction process in image processing for textures. The local binary pattern feature extraction method from textures are introduced. Filtering is also used during the feature extraction process for getting discriminative features. To show the effectiveness of the algorithm before the extraction process, three different noise are added to both train and test images. Wiener filter and median filter are used to remove the noise from images. We evaluate the performance of the method with Naïve Bayesian classifier. We conduct the comparative analysis on benchmark dataset with different filtering and size. Our experiments demonstrate that feature extraction process combine with filtering give promising results on noisy images.

Feature Extraction Using Fractal Codes

NARCIS (Netherlands)

B.A.M. Schouten (Ben); P.M. de Zeeuw (Paul)

1999-01-01

htmlabstractFast and successful searching for an object in a multimedia database is a highly desirable functionality. Several approaches to content based retrieval for multimedia databases can be found in the literature [9,10,12,14,17]. The approach we consider is feature extraction. A feature can

Characters Feature Extraction Based on Neat Oracle Bone Rubbings

OpenAIRE

Lei Guo

2013-01-01

In order to recognize characters on the neat oracle bone rubbings, a new mesh point feature extraction algorithm was put forward in this paper by researching and improving of the existing coarse mesh feature extraction algorithm and the point feature extraction algorithm. Some improvements of this algorithm were as followings: point feature was introduced into the coarse mesh feature, the absolute address was converted to relative address, and point features have been changed grid and positio...

Feature extraction using fractal codes

NARCIS (Netherlands)

B.A.M. Ben Schouten; Paul M. de Zeeuw

1999-01-01

Fast and successful searching for an object in a multimedia database is a highly desirable functionality. Several approaches to content based retrieval for multimedia databases can be found in the literature [9,10,12,14,17]. The approach we consider is feature extraction. A feature can be seen as a

Feature Extraction in Radar Target Classification

Directory of Open Access Journals (Sweden)

Z. Kus

1999-09-01

Full Text Available This paper presents experimental results of extracting features in the Radar Target Classification process using the J frequency band pulse radar. The feature extraction is based on frequency analysis methods, the discrete-time Fourier Transform (DFT and Multiple Signal Characterisation (MUSIC, based on the detection of Doppler effect. The analysis has turned to the preference of DFT with implemented Hanning windowing function. We assumed to classify targets-vehicles into two classes, the wheeled vehicle and tracked vehicle. The results show that it is possible to classify them only while moving. The feature of the class results from a movement of moving parts of the vehicle. However, we have not found any feature to classify the wheeled and tracked vehicles while non-moving, although their engines are on.

Linguistic Preprocessing and Tagging for Problem Report Trend Analysis

Science.gov (United States)

Beil, Robert J.; Malin, Jane T.

2012-01-01

Mr. Robert Beil, Systems Engineer at Kennedy Space Center (KSC), requested the NASA Engineering and Safety Center (NESC) develop a prototype tool suite that combines complementary software technology used at Johnson Space Center (JSC) and KSC for problem report preprocessing and semantic tag extraction, to improve input to data mining and trend analysis. This document contains the outcome of the assessment and the Findings, Observations and NESC Recommendations.

Object feature extraction and recognition model

International Nuclear Information System (INIS)

Wan Min; Xiang Rujian; Wan Yongxing

2001-01-01

The characteristics of objects, especially flying objects, are analyzed, which include characteristics of spectrum, image and motion. Feature extraction is also achieved. To improve the speed of object recognition, a feature database is used to simplify the data in the source database. The feature vs. object relationship maps are stored in the feature database. An object recognition model based on the feature database is presented, and the way to achieve object recognition is also explained

Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso

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Jin-Jia Wang

2015-01-01

Full Text Available Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs. Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped channel and feature selection method is then applied, which uses the logistical regression model with Sparse Group Lasso penalized function. The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method. The best parameters and feature subset are selected by using a 10-fold cross-validation. Finally, the test data is classified using the trained model. Compared with existing channel and feature selection methods, results show that the proposed method is more suitable, more stable, and faster for high-dimensional feature fusion. It can simultaneously achieve channel and feature selection with a lower error rate. The test accuracy on the data used from international BCI Competition IV reached 84.72%.

Real-time hypothesis driven feature extraction on parallel processing architectures

DEFF Research Database (Denmark)

Granmo, O.-C.; Jensen, Finn Verner

2002-01-01

the problem of higher-order feature-content/feature-feature correlation, causally complexly interacting features are identified through Bayesian network d-separation analysis and combined into joint features. When used on a moderately complex object-tracking case, the technique is able to select...... extraction, which selectively extract relevant features one-by-one, have in some cases achieved real-time performance on single processing element architectures. In this paperwe propose a novel technique which combines the above two approaches. Features are selectively extracted in parallelizable sets...

Development and integration of block operations for data invariant automation of digital preprocessing and analysis of biological and biomedical Raman spectra.

Science.gov (United States)

Schulze, H Georg; Turner, Robin F B

2015-06-01

High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

Uniform competency-based local feature extraction for remote sensing images

Science.gov (United States)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

Data pre-processing: a case study in predicting student's retention in ...

African Journals Online (AJOL)

dataset with features that are ready for data mining task. The study also proposed a process model and suggestions, which can be applied to support more comprehensible tools for educational domain who is the end user. Subsequently, the data pre-processing become more efficient for predicting student's retention in ...

Image segmentation-based robust feature extraction for color image watermarking

Science.gov (United States)

Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

2018-04-01

This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

RESEARCH ON FEATURE POINTS EXTRACTION METHOD FOR BINARY MULTISCALE AND ROTATION INVARIANT LOCAL FEATURE DESCRIPTOR

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Hongwei Ying

2014-08-01

Full Text Available An extreme point of scale space extraction method for binary multiscale and rotation invariant local feature descriptor is studied in this paper in order to obtain a robust and fast method for local image feature descriptor. Classic local feature description algorithms often select neighborhood information of feature points which are extremes of image scale space, obtained by constructing the image pyramid using certain signal transform method. But build the image pyramid always consumes a large amount of computing and storage resources, is not conducive to the actual applications development. This paper presents a dual multiscale FAST algorithm, it does not need to build the image pyramid, but can extract feature points of scale extreme quickly. Feature points extracted by proposed method have the characteristic of multiscale and rotation Invariant and are fit to construct the local feature descriptor.

Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting

KAUST Repository

Fernandes, José Antonio

2013-02-01

A multi-species approach to fisheries management requires taking into account the interactions between species in order to improve recruitment forecasting of the fish species. Recent advances in Bayesian networks direct the learning of models with several interrelated variables to be forecasted simultaneously. These models are known as multi-dimensional Bayesian network classifiers (MDBNs). Pre-processing steps are critical for the posterior learning of the model in these kinds of domains. Therefore, in the present study, a set of \\'state-of-the-art\\' uni-dimensional pre-processing methods, within the categories of missing data imputation, feature discretization and feature subset selection, are adapted to be used with MDBNs. A framework that includes the proposed multi-dimensional supervised pre-processing methods, coupled with a MDBN classifier, is tested with synthetic datasets and the real domain of fish recruitment forecasting. The correctly forecasting of three fish species (anchovy, sardine and hake) simultaneously is doubled (from 17.3% to 29.5%) using the multi-dimensional approach in comparison to mono-species models. The probability assessments also show high improvement reducing the average error (estimated by means of Brier score) from 0.35 to 0.27. Finally, these differences are superior to the forecasting of species by pairs. © 2012 Elsevier Ltd.

Facilitating Watermark Insertion by Preprocessing Media

Directory of Open Access Journals (Sweden)

Matt L. Miller

2004-10-01

Full Text Available There are several watermarking applications that require the deployment of a very large number of watermark embedders. These applications often have severe budgetary constraints that limit the computation resources that are available. Under these circumstances, only simple embedding algorithms can be deployed, which have limited performance. In order to improve performance, we propose preprocessing the original media. It is envisaged that this preprocessing occurs during content creation and has no budgetary or computational constraints. Preprocessing combined with simple embedding creates a watermarked Work, the performance of which exceeds that of simple embedding alone. However, this performance improvement is obtained without any increase in the computational complexity of the embedder. Rather, the additional computational burden is shifted to the preprocessing stage. A simple example of this procedure is described and experimental results confirm our assertions.

Integrated Phoneme Subspace Method for Speech Feature Extraction

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Park Hyunsin

2009-01-01

Full Text Available Speech feature extraction has been a key focus in robust speech recognition research. In this work, we discuss data-driven linear feature transformations applied to feature vectors in the logarithmic mel-frequency filter bank domain. Transformations are based on principal component analysis (PCA, independent component analysis (ICA, and linear discriminant analysis (LDA. Furthermore, this paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and reconstructs feature space for representing phonemic information efficiently. The proposed speech feature vector is generated by projecting an observed vector onto an integrated phoneme subspace (IPS based on PCA or ICA. The performance of the new feature was evaluated for isolated word speech recognition. The proposed method provided higher recognition accuracy than conventional methods in clean and reverberant environments.

Hand veins feature extraction using DT-CNNS

Science.gov (United States)

Malki, Suleyman; Spaanenburg, Lambert

2007-05-01

As the identification process is based on the unique patterns of the users, biometrics technologies are expected to provide highly secure authentication systems. The existing systems using fingerprints or retina patterns are, however, very vulnerable. One's fingerprints are accessible as soon as the person touches a surface, while a high resolution camera easily captures the retina pattern. Thus, both patterns can easily be "stolen" and forged. Beside, technical considerations decrease the usability for these methods. Due to the direct contact with the finger, the sensor gets dirty, which decreases the authentication success ratio. Aligning the eye with a camera to capture the retina pattern gives uncomfortable feeling. On the other hand, vein patterns of either a palm of the hand or a single finger offer stable, unique and repeatable biometrics features. A fingerprint-based identification system using Cellular Neural Networks has already been proposed by Gao. His system covers all stages of a typical fingerprint verification procedure from Image Preprocessing to Feature Matching. This paper performs a critical review of the individual algorithmic steps. Notably, the operation of False Feature Elimination is applied only once instead of 3 times. Furthermore, the number of iterations is limited to 1 for all used templates. Hence, the computational need of the feedback contribution is removed. Consequently the computational effort is drastically reduced without a notable chance in quality. This allows a full integration of the detection mechanism. The system is prototyped on a Xilinx Virtex II Pro P30 FPGA.

A biometric identification system based on eigenpalm and eigenfinger features.

Science.gov (United States)

Ribaric, Slobodan; Fratric, Ivan

2005-11-01

This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).

[Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

Science.gov (United States)

Li, Jing; Hong, Wenxue

2014-12-01

The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.

Feature extraction for classification in the data mining process

NARCIS (Netherlands)

Pechenizkiy, M.; Puuronen, S.; Tsymbal, A.

2003-01-01

Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of "the curse of dimensionality". Three different eigenvector-based feature extraction approaches

Application of preprocessing filtering on Decision Tree C4.5 and rough set theory

Science.gov (United States)

Chan, Joseph C. C.; Lin, Tsau Y.

2001-03-01

This paper compares two artificial intelligence methods: the Decision Tree C4.5 and Rough Set Theory on the stock market data. The Decision Tree C4.5 is reviewed with the Rough Set Theory. An enhanced window application is developed to facilitate the pre-processing filtering by introducing the feature (attribute) transformations, which allows users to input formulas and create new attributes. Also, the application produces three varieties of data set with delaying, averaging, and summation. The results prove the improvement of pre-processing by applying feature (attribute) transformations on Decision Tree C4.5. Moreover, the comparison between Decision Tree C4.5 and Rough Set Theory is based on the clarity, automation, accuracy, dimensionality, raw data, and speed, which is supported by the rules sets generated by both algorithms on three different sets of data.

Multistage feature extraction for accurate face alignment

NARCIS (Netherlands)

Zuo, F.; With, de P.H.N.

2004-01-01

We propose a novel multistage facial feature extraction approach using a combination of 'global' and 'local' techniques. At the first stage, we use template matching, based on an Edge-Orientation-Map for fast feature position estimation. Using this result, a statistical framework applying the Active

Prominent feature extraction for review analysis: an empirical study

Science.gov (United States)

Agarwal, Basant; Mittal, Namita

2016-05-01

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

Neural network-based preprocessing to estimate the parameters of the X-ray emission of a single-temperature thermal plasma

Science.gov (United States)

Ichinohe, Y.; Yamada, S.; Miyazaki, N.; Saito, S.

2018-04-01

We present data preprocessing based on an artificial neural network to estimate the parameters of the X-ray emission spectra of a single-temperature thermal plasma. The method finds appropriate parameters close to the global optimum. The neural network is designed to learn the parameters of the thermal plasma (temperature, abundance, normalization and redshift) of the input spectra. After training using 9000 simulated X-ray spectra, the network has grown to predict all the unknown parameters with uncertainties of about a few per cent. The performance dependence on the network structure has been studied. We applied the neural network to an actual high-resolution spectrum obtained with Hitomi. The predicted plasma parameters agree with the known best-fitting parameters of the Perseus cluster within uncertainties of ≲10 per cent. The result shows that neural networks trained by simulated data might possibly be used to extract a feature built in the data. This would reduce human-intensive preprocessing costs before detailed spectral analysis, and would help us make the best use of the large quantities of spectral data that will be available in the coming decades.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

Science.gov (United States)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTED CLASSIFICATION

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L. Ding

2018-04-01

Full Text Available In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

FEATURE EXTRACTION FOR EMG BASED PROSTHESES CONTROL

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R. Aishwarya

2013-01-01

Full Text Available The control of prosthetic limb would be more effective if it is based on Surface Electromyogram (SEMG signals from remnant muscles. The analysis of SEMG signals depend on a number of factors, such as amplitude as well as time- and frequency-domain properties. Time series analysis using Auto Regressive (AR model and Mean frequency which is tolerant to white Gaussian noise are used as feature extraction techniques. EMG Histogram is used as another feature vector that was seen to give more distinct classification. The work was done with SEMG dataset obtained from the NINAPRO DATABASE, a resource for bio robotics community. Eight classes of hand movements hand open, hand close, Wrist extension, Wrist flexion, Pointing index, Ulnar deviation, Thumbs up, Thumb opposite to little finger are taken into consideration and feature vectors are extracted. The feature vectors can be given to an artificial neural network for further classification in controlling the prosthetic arm which is not dealt in this paper.

An Effective Fault Feature Extraction Method for Gas Turbine Generator System Diagnosis

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Jian-Hua Zhong

2016-01-01

Full Text Available Fault diagnosis is very important to maintain the operation of a gas turbine generator system (GTGS in power plants, where any abnormal situations will interrupt the electricity supply. The fault diagnosis of the GTGS faces the main challenge that the acquired data, vibration or sound signals, contain a great deal of redundant information which extends the fault identification time and degrades the diagnostic accuracy. To improve the diagnostic performance in the GTGS, an effective fault feature extraction framework is proposed to solve the problem of the signal disorder and redundant information in the acquired signal. The proposed framework combines feature extraction with a general machine learning method, support vector machine (SVM, to implement an intelligent fault diagnosis. The feature extraction method adopts wavelet packet transform and time-domain statistical features to extract the features of faults from the vibration signal. To further reduce the redundant information in extracted features, kernel principal component analysis is applied in this study. Experimental results indicate that the proposed feature extracted technique is an effective method to extract the useful features of faults, resulting in improvement of the performance of fault diagnosis for the GTGS.

Statistical Feature Extraction and Recognition of Beverages Using Electronic Tongue

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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..

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

Science.gov (United States)

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano

2015-06-17

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

Data preprocessing in data mining

CERN Document Server

García, Salvador; Herrera, Francisco

2015-01-01

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying t...

A Comparison Study on Multidomain EEG Features for Sleep Stage Classification

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Yu Zhang

2017-01-01

Full Text Available Feature extraction from physiological signals of EEG (electroencephalogram is an essential part for sleep staging. In this study, multidomain feature extraction was investigated based on time domain analysis, nonlinear analysis, and frequency domain analysis. Unlike the traditional feature calculation in time domain, a sequence merging method was developed as a preprocessing procedure. The objective is to eliminate the clutter waveform and highlight the characteristic waveform for further analysis. The numbers of the characteristic activities were extracted as the features from time domain. The contributions of features from different domains to the sleep stages were compared. The effectiveness was further analyzed by automatic sleep stage classification and compared with the visual inspection. The overnight clinical sleep EEG recordings of 3 patients after the treatment of Continuous Positive Airway Pressure (CPAP were tested. The obtained results showed that the developed method can highlight the characteristic activity which is useful for both automatic sleep staging and visual inspection. Furthermore, it can be a training tool for better understanding the appearance of characteristic waveforms from raw sleep EEG which is mixed and complex in time domain.

Large datasets: Segmentation, feature extraction, and compression

Energy Technology Data Exchange (ETDEWEB)

Downing, D.J.; Fedorov, V.; Lawkins, W.F.; Morris, M.D.; Ostrouchov, G.

1996-07-01

Large data sets with more than several mission multivariate observations (tens of megabytes or gigabytes of stored information) are difficult or impossible to analyze with traditional software. The amount of output which must be scanned quickly dilutes the ability of the investigator to confidently identify all the meaningful patterns and trends which may be present. The purpose of this project is to develop both a theoretical foundation and a collection of tools for automated feature extraction that can be easily customized to specific applications. Cluster analysis techniques are applied as a final step in the feature extraction process, which helps make data surveying simple and effective.

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.

A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification

Directory of Open Access Journals (Sweden)

Jie Hu

2018-01-01

Full Text Available Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM, and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

Directory of Open Access Journals (Sweden)

Gang Hu

2018-01-01

Full Text Available The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.

A method for real-time implementation of HOG feature extraction

Science.gov (United States)

Luo, Hai-bo; Yu, Xin-rong; Liu, Hong-mei; Ding, Qing-hai

2011-08-01

Histogram of oriented gradient (HOG) is an efficient feature extraction scheme, and HOG descriptors are feature descriptors which is widely used in computer vision and image processing for the purpose of biometrics, target tracking, automatic target detection(ATD) and automatic target recognition(ATR) etc. However, computation of HOG feature extraction is unsuitable for hardware implementation since it includes complicated operations. In this paper, the optimal design method and theory frame for real-time HOG feature extraction based on FPGA were proposed. The main principle is as follows: firstly, the parallel gradient computing unit circuit based on parallel pipeline structure was designed. Secondly, the calculation of arctangent and square root operation was simplified. Finally, a histogram generator based on parallel pipeline structure was designed to calculate the histogram of each sub-region. Experimental results showed that the HOG extraction can be implemented in a pixel period by these computing units.

Feature extraction for magnetic domain images of magneto-optical recording films using gradient feature segmentation

International Nuclear Information System (INIS)

Quanqing, Zhu.; Xinsai, Wang; Xuecheng, Zou; Haihua, Li; Xiaofei, Yang

2002-01-01

In this paper, we present a method to realize feature extraction on low contrast magnetic domain images of magneto-optical recording films. The method is based on the following three steps: first, Lee-filtering method is adopted to realize pre-filtering and noise reduction; this is followed by gradient feature segmentation, which separates the object area from the background area; finally the common linking method is adopted and the characteristic parameters of magnetic domain are calculated. We describe these steps with particular emphasis on the gradient feature segmentation. The results show that this method has advantages over other traditional ones for feature extraction of low contrast images

Reproducible cancer biomarker discovery in SELDI-TOF MS using different pre-processing algorithms.

Directory of Open Access Journals (Sweden)

Jinfeng Zou

Full Text Available BACKGROUND: There has been much interest in differentiating diseased and normal samples using biomarkers derived from mass spectrometry (MS studies. However, biomarker identification for specific diseases has been hindered by irreproducibility. Specifically, a peak profile extracted from a dataset for biomarker identification depends on a data pre-processing algorithm. Until now, no widely accepted agreement has been reached. RESULTS: In this paper, we investigated the consistency of biomarker identification using differentially expressed (DE peaks from peak profiles produced by three widely used average spectrum-dependent pre-processing algorithms based on SELDI-TOF MS data for prostate and breast cancers. Our results revealed two important factors that affect the consistency of DE peak identification using different algorithms. One factor is that some DE peaks selected from one peak profile were not detected as peaks in other profiles, and the second factor is that the statistical power of identifying DE peaks in large peak profiles with many peaks may be low due to the large scale of the tests and small number of samples. Furthermore, we demonstrated that the DE peak detection power in large profiles could be improved by the stratified false discovery rate (FDR control approach and that the reproducibility of DE peak detection could thereby be increased. CONCLUSIONS: Comparing and evaluating pre-processing algorithms in terms of reproducibility can elucidate the relationship among different algorithms and also help in selecting a pre-processing algorithm. The DE peaks selected from small peak profiles with few peaks for a dataset tend to be reproducibly detected in large peak profiles, which suggests that a suitable pre-processing algorithm should be able to produce peaks sufficient for identifying useful and reproducible biomarkers.

Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images

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Chen Xing

2016-01-01

Full Text Available Deep learning methods have been successfully applied to learn feature representations for high-dimensional data, where the learned features are able to reveal the nonlinear properties exhibited in the data. In this paper, deep learning method is exploited for feature extraction of hyperspectral data, and the extracted features can provide good discriminability for classification task. Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning. We utilized stacked denoise autoencoder (SDAE method to pretrain the network, which is robust to noise. In the top layer of the network, logistic regression (LR approach is utilized to perform supervised fine-tuning and classification. Since sparsity of features might improve the separation capability, we utilized rectified linear unit (ReLU as activation function in SDAE to extract high level and sparse features. Experimental results using Hyperion, AVIRIS, and ROSIS hyperspectral data demonstrated that the SDAE pretraining in conjunction with the LR fine-tuning and classification (SDAE_LR can achieve higher accuracies than the popular support vector machine (SVM classifier.

Application of eigen value expansion to feature extraction from MRI images

International Nuclear Information System (INIS)

Kinosada, Yasutomi; Takeda, Kan; Nakagawa, Tsuyoshi

1991-01-01

The eigen value expansion technique was utilized for feature extraction of magnetic resonance (MR) images. The eigen value expansion is an orthonormal transformation method which decomposes a set of images into some statistically uncorrelated images. The technique was applied to MR images obtained with various imaging parameters at the same anatomical site. It generated one mean image and another set of images called bases for the images. Each basis corresponds to a feature in the images. A basis is, therefore, utilized for the feature extraction from MR images and a weighted sum of bases is also used for the feature enhancement. Furthermore, any MR image with specific feature can be obtained from a linear combination of the mean image and all of the bases. Images of hemorrhaged brain with a spin echo sequence and a series of cinematic cerebro spinal fluid flow images with ECG gated gradient refocused echo sequence were employed to estimate the ability of the feature extraction and the contrast enhancement. Results showed us that proposed application of an eigen value expansion technique to the feature extraction of MR images is good enough to clinical use and superior to other feature extraction methods such as producing a calculated MR image with a given TR and TE or the matched-filter method in processing speed and reproducibility of results. (author)

Parallel pipeline algorithm of real time star map preprocessing

Science.gov (United States)

Wang, Hai-yong; Qin, Tian-mu; Liu, Jia-qi; Li, Zhi-feng; Li, Jian-hua

2016-03-01

To improve the preprocessing speed of star map and reduce the resource consumption of embedded system of star tracker, a parallel pipeline real-time preprocessing algorithm is presented. The two characteristics, the mean and the noise standard deviation of the background gray of a star map, are firstly obtained dynamically by the means that the intervene of the star image itself to the background is removed in advance. The criterion on whether or not the following noise filtering is needed is established, then the extraction threshold value is assigned according to the level of background noise, so that the centroiding accuracy is guaranteed. In the processing algorithm, as low as two lines of pixel data are buffered, and only 100 shift registers are used to record the connected domain label, by which the problems of resources wasting and connected domain overflow are solved. The simulating results show that the necessary data of the selected bright stars could be immediately accessed in a delay time as short as 10us after the pipeline processing of a 496×496 star map in 50Mb/s is finished, and the needed memory and registers resource total less than 80kb. To verify the accuracy performance of the algorithm proposed, different levels of background noise are added to the processed ideal star map, and the statistic centroiding error is smaller than 1/23 pixel under the condition that the signal to noise ratio is greater than 1. The parallel pipeline algorithm of real time star map preprocessing helps to increase the data output speed and the anti-dynamic performance of star tracker.

A Feature Fusion Based Forecasting Model for Financial Time Series

Science.gov (United States)

Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie

2014-01-01

Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455

Structural health monitoring feature design by genetic programming

International Nuclear Information System (INIS)

Harvey, Dustin Y; Todd, Michael D

2014-01-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems. (paper)

Conversation on data mining strategies in LC-MS untargeted metabolomics: pre-processing and pre-treatment steps

CSIR Research Space (South Africa)

Tugizimana, F

2016-11-01

Full Text Available -MS)-based untargeted metabolomic dataset, this study explored the influence of collection parameters in the data pre-processing step, scaling and data transformation on the statistical models generated, and feature selection, thereafter. Data obtained in positive mode...

Micro-Analyzer: automatic preprocessing of Affymetrix microarray data.

Science.gov (United States)

Guzzi, Pietro Hiram; Cannataro, Mario

2013-08-01

A current trend in genomics is the investigation of the cell mechanism using different technologies, in order to explain the relationship among genes, molecular processes and diseases. For instance, the combined use of gene-expression arrays and genomic arrays has been demonstrated as an effective instrument in clinical practice. Consequently, in a single experiment different kind of microarrays may be used, resulting in the production of different types of binary data (images and textual raw data). The analysis of microarray data requires an initial preprocessing phase, that makes raw data suitable for use on existing analysis platforms, such as the TIGR M4 (TM4) Suite. An additional challenge to be faced by emerging data analysis platforms is the ability to treat in a combined way those different microarray formats coupled with clinical data. In fact, resulting integrated data may include both numerical and symbolic data (e.g. gene expression and SNPs regarding molecular data), as well as temporal data (e.g. the response to a drug, time to progression and survival rate), regarding clinical data. Raw data preprocessing is a crucial step in analysis but is often performed in a manual and error prone way using different software tools. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of different microarray data are needed. The paper presents Micro-Analyzer (Microarray Analyzer), a cross-platform tool for the automatic normalization, summarization and annotation of Affymetrix gene expression and SNP binary data. It represents the evolution of the μ-CS tool, extending the preprocessing to SNP arrays that were not allowed in μ-CS. The Micro-Analyzer is provided as a Java standalone tool and enables users to read, preprocess and analyse binary microarray data (gene expression and SNPs) by invoking TM4 platform. It avoids: (i) the manual invocation of external tools (e.g. the Affymetrix Power

PyEEG: an open source Python module for EEG/MEG feature extraction.

Science.gov (United States)

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.

Task-induced frequency modulation features for brain-computer interfacing.

Science.gov (United States)

Jayaram, Vinay; Hohmann, Matthias; Just, Jennifer; Schölkopf, Bernhard; Grosse-Wentrup, Moritz

2017-10-01

Task-induced amplitude modulation of neural oscillations is routinely used in brain-computer interfaces (BCIs) for decoding subjects' intents, and underlies some of the most robust and common methods in the field, such as common spatial patterns and Riemannian geometry. While there has been some interest in phase-related features for classification, both techniques usually presuppose that the frequencies of neural oscillations remain stable across various tasks. We investigate here whether features based on task-induced modulation of the frequency of neural oscillations enable decoding of subjects' intents with an accuracy comparable to task-induced amplitude modulation. We compare cross-validated classification accuracies using the amplitude and frequency modulated features, as well as a joint feature space, across subjects in various paradigms and pre-processing conditions. We show results with a motor imagery task, a cognitive task, and also preliminary results in patients with amyotrophic lateral sclerosis (ALS), as well as using common spatial patterns and Laplacian filtering. The frequency features alone do not significantly out-perform traditional amplitude modulation features, and in some cases perform significantly worse. However, across both tasks and pre-processing in healthy subjects the joint space significantly out-performs either the frequency or amplitude features alone. This result only does not hold for ALS patients, for whom the dataset is of insufficient size to draw any statistically significant conclusions. Task-induced frequency modulation is robust and straight forward to compute, and increases performance when added to standard amplitude modulation features across paradigms. This allows more information to be extracted from the EEG signal cheaply and can be used throughout the field of BCIs.

Task-induced frequency modulation features for brain-computer interfacing

Science.gov (United States)

Jayaram, Vinay; Hohmann, Matthias; Just, Jennifer; Schölkopf, Bernhard; Grosse-Wentrup, Moritz

2017-10-01

Objective. Task-induced amplitude modulation of neural oscillations is routinely used in brain-computer interfaces (BCIs) for decoding subjects’ intents, and underlies some of the most robust and common methods in the field, such as common spatial patterns and Riemannian geometry. While there has been some interest in phase-related features for classification, both techniques usually presuppose that the frequencies of neural oscillations remain stable across various tasks. We investigate here whether features based on task-induced modulation of the frequency of neural oscillations enable decoding of subjects’ intents with an accuracy comparable to task-induced amplitude modulation. Approach. We compare cross-validated classification accuracies using the amplitude and frequency modulated features, as well as a joint feature space, across subjects in various paradigms and pre-processing conditions. We show results with a motor imagery task, a cognitive task, and also preliminary results in patients with amyotrophic lateral sclerosis (ALS), as well as using common spatial patterns and Laplacian filtering. Main results. The frequency features alone do not significantly out-perform traditional amplitude modulation features, and in some cases perform significantly worse. However, across both tasks and pre-processing in healthy subjects the joint space significantly out-performs either the frequency or amplitude features alone. This result only does not hold for ALS patients, for whom the dataset is of insufficient size to draw any statistically significant conclusions. Significance. Task-induced frequency modulation is robust and straight forward to compute, and increases performance when added to standard amplitude modulation features across paradigms. This allows more information to be extracted from the EEG signal cheaply and can be used throughout the field of BCIs.

Practical Secure Computation with Pre-Processing

DEFF Research Database (Denmark)

Zakarias, Rasmus Winther

Secure Multiparty Computation has been divided between protocols best suited for binary circuits and protocols best suited for arithmetic circuits. With their MiniMac protocol in [DZ13], Damgård and Zakarias take an important step towards bridging these worlds with an arithmetic protocol tuned...... space for pre-processing material than computing the non-linear parts online (depends on the quality of circuit of course). Surprisingly, even for our optimized AES-circuit this is not the case. We further improve the design of the pre-processing material and end up with only 10 megabyes of pre...... a protocol for small field arithmetic to do fast large integer multipli- cations. This is achieved by devising pre-processing material that allows the Toom-Cook multiplication algorithm to run between the parties with linear communication complexity. With this result computation on the CPU by the parties...

Ensemble preprocessing of near-infrared (NIR) spectra for multivariate calibration

International Nuclear Information System (INIS)

Xu Lu; Zhou Yanping; Tang Lijuan; Wu Hailong; Jiang Jianhui; Shen Guoli; Yu Ruqin

2008-01-01

Preprocessing of raw near-infrared (NIR) spectral data is indispensable in multivariate calibration when the measured spectra are subject to significant noises, baselines and other undesirable factors. However, due to the lack of sufficient prior information and an incomplete knowledge of the raw data, NIR spectra preprocessing in multivariate calibration is still trial and error. How to select a proper method depends largely on both the nature of the data and the expertise and experience of the practitioners. This might limit the applications of multivariate calibration in many fields, where researchers are not very familiar with the characteristics of many preprocessing methods unique in chemometrics and have difficulties to select the most suitable methods. Another problem is many preprocessing methods, when used alone, might degrade the data in certain aspects or lose some useful information while improving certain qualities of the data. In order to tackle these problems, this paper proposes a new concept of data preprocessing, ensemble preprocessing method, where partial least squares (PLSs) models built on differently preprocessed data are combined by Monte Carlo cross validation (MCCV) stacked regression. Little or no prior information of the data and expertise are required. Moreover, fusion of complementary information obtained by different preprocessing methods often leads to a more stable and accurate calibration model. The investigation of two real data sets has demonstrated the advantages of the proposed method

Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images

Directory of Open Access Journals (Sweden)

Mei Yu

2012-01-01

Full Text Available The most critical step in grayscale medical image retrieval systems is feature extraction. Understanding the interrelatedness between the characteristics of lesion images and corresponding imaging features is crucial for image training, as well as for features extraction. A feature-extraction algorithm is developed based on different imaging properties of lesions and on the discrepancy in density between the lesions and their surrounding normal liver tissues in triple-phase contrast-enhanced computed tomographic (CT scans. The algorithm includes mainly two processes: (1 distance transformation, which is used to divide the lesion into distinct regions and represents the spatial structure distribution and (2 representation using bag of visual words (BoW based on regions. The evaluation of this system based on the proposed feature extraction algorithm shows excellent retrieval results for three types of liver lesions visible on triple-phase scans CT images. The results of the proposed feature extraction algorithm show that although single-phase scans achieve the average precision of 81.9%, 80.8%, and 70.2%, dual- and triple-phase scans achieve 86.3% and 88.0%.

Structure Crack Identification Based on Surface-mounted Active Sensor Network with Time-Domain Feature Extraction and Neural Network

Directory of Open Access Journals (Sweden)

Chunling DU

2012-03-01

Full Text Available In this work the condition of metallic structures are classified based on the acquired sensor data from a surface-mounted piezoelectric sensor/actuator network. The structures are aluminum plates with riveted holes and possible crack damage at these holes. A 400 kHz sine wave burst is used as diagnostic signals. The combination of time-domain S0 waves from received sensor signals is directly used as features and preprocessing is not needed for the dam age detection. Since the time sequence of the extracted S0 has a high dimension, principal component estimation is applied to reduce its dimension before entering NN (neural network training for classification. An LVQ (learning vector quantization NN is used to classify the conditions as healthy or damaged. A number of FEM (finite element modeling results are taken as inputs to the NN for training, since the simulated S0 waves agree well with the experimental results on real plates. The performance of the classification is then validated by using these testing results.

Shape adaptive, robust iris feature extraction from noisy iris images.

Science.gov (United States)

Ghodrati, Hamed; Dehghani, Mohammad Javad; Danyali, Habibolah

2013-10-01

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate.

A graph-Laplacian-based feature extraction algorithm for neural spike sorting.

Science.gov (United States)

Ghanbari, Yasser; Spence, Larry; Papamichalis, Panos

2009-01-01

Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.

Airborne LIDAR and high resolution satellite data for rapid 3D feature extraction

Science.gov (United States)

Jawak, S. D.; Panditrao, S. N.; Luis, A. J.

2014-11-01

This work uses the canopy height model (CHM) based workflow for individual tree crown delineation and 3D feature extraction approach (Overwatch Geospatial's proprietary algorithm) for building feature delineation from high-density light detection and ranging (LiDAR) point cloud data in an urban environment and evaluates its accuracy by using very high-resolution panchromatic (PAN) (spatial) and 8-band (multispectral) WorldView-2 (WV-2) imagery. LiDAR point cloud data over San Francisco, California, USA, recorded in June 2010, was used to detect tree and building features by classifying point elevation values. The workflow employed includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model (DTM), generation of a hill-shade image and an intensity image, extraction of digital surface model, generation of bare earth digital elevation model (DEM) and extraction of tree and building features. First, the optical WV-2 data and the LiDAR intensity image were co-registered using ground control points (GCPs). The WV-2 rational polynomial coefficients model (RPC) was executed in ERDAS Leica Photogrammetry Suite (LPS) using supplementary *.RPB file. In the second stage, ortho-rectification was carried out using ERDAS LPS by incorporating well-distributed GCPs. The root mean square error (RMSE) for the WV-2 was estimated to be 0.25 m by using more than 10 well-distributed GCPs. In the second stage, we generated the bare earth DEM from LiDAR point cloud data. In most of the cases, bare earth DEM does not represent true ground elevation. Hence, the model was edited to get the most accurate DEM/ DTM possible and normalized the LiDAR point cloud data based on DTM in order to reduce the effect of undulating terrain. We normalized the vegetation point cloud values by subtracting the ground points (DEM) from the LiDAR point cloud. A normalized digital surface model (nDSM) or CHM was calculated from the LiDAR data by subtracting the DEM from the DSM

Optimized Feature Extraction for Temperature-Modulated Gas Sensors

Directory of Open Access Journals (Sweden)

Alexander Vergara

2009-01-01

Full Text Available One of the most serious limitations to the practical utilization of solid-state gas sensors is the drift of their signal. Even if drift is rooted in the chemical and physical processes occurring in the sensor, improved signal processing is generally considered as a methodology to increase sensors stability. Several studies evidenced the augmented stability of time variable signals elicited by the modulation of either the gas concentration or the operating temperature. Furthermore, when time-variable signals are used, the extraction of features can be accomplished in shorter time with respect to the time necessary to calculate the usual features defined in steady-state conditions. In this paper, we discuss the stability properties of distinct dynamic features using an array of metal oxide semiconductors gas sensors whose working temperature is modulated with optimized multisinusoidal signals. Experiments were aimed at measuring the dispersion of sensors features in repeated sequences of a limited number of experimental conditions. Results evidenced that the features extracted during the temperature modulation reduce the multidimensional data dispersion among repeated measurements. In particular, the Energy Signal Vector provided an almost constant classification rate along the time with respect to the temperature modulation.

Multi-scale salient feature extraction on mesh models

KAUST Repository

Yang, Yongliang; Shen, ChaoHui

2012-01-01

We present a new method of extracting multi-scale salient features on meshes. It is based on robust estimation of curvature on multiple scales. The coincidence between salient feature and the scale of interest can be established straightforwardly, where detailed feature appears on small scale and feature with more global shape information shows up on large scale. We demonstrate this multi-scale description of features accords with human perception and can be further used for several applications as feature classification and viewpoint selection. Experiments exhibit that our method as a multi-scale analysis tool is very helpful for studying 3D shapes. © 2012 Springer-Verlag.

Compact and Hybrid Feature Description for Building Extraction

Science.gov (United States)

Li, Z.; Liu, Y.; Hu, Y.; Li, P.; Ding, Y.

2017-05-01

Building extraction in aerial orthophotos is crucial for various applications. Currently, deep learning has been shown to be successful in addressing building extraction with high accuracy and high robustness. However, quite a large number of samples is required in training a classifier when using deep learning model. In order to realize accurate and semi-interactive labelling, the performance of feature description is crucial, as it has significant effect on the accuracy of classification. In this paper, we bring forward a compact and hybrid feature description method, in order to guarantees desirable classification accuracy of the corners on the building roof contours. The proposed descriptor is a hybrid description of an image patch constructed from 4 sets of binary intensity tests. Experiments show that benefiting from binary description and making full use of color channels, this descriptor is not only computationally frugal, but also accurate than SURF for building extraction.

The effects of pre-processing strategies in sentiment analysis of online movie reviews

Science.gov (United States)

Zin, Harnani Mat; Mustapha, Norwati; Murad, Masrah Azrifah Azmi; Sharef, Nurfadhlina Mohd

2017-10-01

With the ever increasing of internet applications and social networking sites, people nowadays can easily express their feelings towards any products and services. These online reviews act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like sentiment analysis and classification to provide a meaningful information for future uses. In text analysis tasks, the appropriate selection of words/features will have a huge impact on the effectiveness of the classifier. Thus, this paper explores the effect of the pre-processing strategies in the sentiment analysis of online movie reviews. In this paper, supervised machine learning method was used to classify the reviews. The support vector machine (SVM) with linear and non-linear kernel has been considered as classifier for the classification of the reviews. The performance of the classifier is critically examined based on the results of precision, recall, f-measure, and accuracy. Two different features representations were used which are term frequency and term frequency-inverse document frequency. Results show that the pre-processing strategies give a significant impact on the classification process.

A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification

Science.gov (United States)

Khorshidtalab, Aida; Mesbah, Mostefa; Salami, Momoh J. E.

2015-01-01

In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$T^{2}$ \\end{document} statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%. PMID:27170898

Analysis of Feature Extraction Methods for Speaker Dependent Speech Recognition

Directory of Open Access Journals (Sweden)

Gurpreet Kaur

2017-02-01

Full Text Available Speech recognition is about what is being said, irrespective of who is saying. Speech recognition is a growing field. Major progress is taking place on the technology of automatic speech recognition (ASR. Still, there are lots of barriers in this field in terms of recognition rate, background noise, speaker variability, speaking rate, accent etc. Speech recognition rate mainly depends on the selection of features and feature extraction methods. This paper outlines the feature extraction techniques for speaker dependent speech recognition for isolated words. A brief survey of different feature extraction techniques like Mel-Frequency Cepstral Coefficients (MFCC, Linear Predictive Coding Coefficients (LPCC, Perceptual Linear Prediction (PLP, Relative Spectra Perceptual linear Predictive (RASTA-PLP analysis are presented and evaluation is done. Speech recognition has various applications from daily use to commercial use. We have made a speaker dependent system and this system can be useful in many areas like controlling a patient vehicle using simple commands.

Evaluating the impact of image preprocessing on iris segmentation

Directory of Open Access Journals (Sweden)

José F. Valencia-Murillo

2014-08-01

Full Text Available Segmentation is one of the most important stages in iris recognition systems. In this paper, image preprocessing algorithms are applied in order to evaluate their impact on successful iris segmentation. The preprocessing algorithms are based on histogram adjustment, Gaussian filters and suppression of specular reflections in human eye images. The segmentation method introduced by Masek is applied on 199 images acquired under unconstrained conditions, belonging to the CASIA-irisV3 database, before and after applying the preprocessing algorithms. Then, the impact of image preprocessing algorithms on the percentage of successful iris segmentation is evaluated by means of a visual inspection of images in order to determine if circumferences of iris and pupil were detected correctly. An increase from 59% to 73% in percentage of successful iris segmentation is obtained with an algorithm that combine elimination of specular reflections, followed by the implementation of a Gaussian filter having a 5x5 kernel. The results highlight the importance of a preprocessing stage as a previous step in order to improve the performance during the edge detection and iris segmentation processes.

A novel murmur-based heart sound feature extraction technique using envelope-morphological analysis

Science.gov (United States)

Yao, Hao-Dong; Ma, Jia-Li; Fu, Bin-Bin; Wang, Hai-Yang; Dong, Ming-Chui

2015-07-01

Auscultation of heart sound (HS) signals serves as an important primary approach to diagnose cardiovascular diseases (CVDs) for centuries. Confronting the intrinsic drawbacks of traditional HS auscultation, computer-aided automatic HS auscultation based on feature extraction technique has witnessed explosive development. Yet, most existing HS feature extraction methods adopt acoustic or time-frequency features which exhibit poor relationship with diagnostic information, thus restricting the performance of further interpretation and analysis. Tackling such a bottleneck problem, this paper innovatively proposes a novel murmur-based HS feature extraction method since murmurs contain massive pathological information and are regarded as the first indications of pathological occurrences of heart valves. Adapting discrete wavelet transform (DWT) and Shannon envelope, the envelope-morphological characteristics of murmurs are obtained and three features are extracted accordingly. Validated by discriminating normal HS and 5 various abnormal HS signals with extracted features, the proposed method provides an attractive candidate in automatic HS auscultation.

Analysis of progression of fatigue conditions in biceps brachii muscles using surface electromyography signals and complexity based features.

Science.gov (United States)

Karthick, P A; Makaram, Navaneethakrishna; Ramakrishnan, S

2014-01-01

Muscle fatigue is a neuromuscular condition where muscle performance decreases due to sustained or intense contraction. It is experienced by both normal and abnormal subjects. In this work, an attempt has been made to analyze the progression of muscle fatigue in biceps brachii muscles using surface electromyography (sEMG) signals. The sEMG signals are recorded from fifty healthy volunteers during dynamic contractions under well defined protocol. The acquired signals are preprocessed and segmented in to six equal parts for further analysis. The features, such as activity, mobility, complexity, sample entropy and spectral entropy are extracted from all six zones. The results are found showing that the extracted features except complexity feature have significant variations in differentiating non-fatigue and fatigue zone respectively. Thus, it appears that, these features are useful in automated analysis of various neuromuscular activities in normal and pathological conditions.

Opinion mining feature-level using Naive Bayes and feature extraction based analysis dependencies

Science.gov (United States)

Sanda, Regi; Baizal, Z. K. Abdurahman; Nhita, Fhira

2015-12-01

Development of internet and technology, has major impact and providing new business called e-commerce. Many e-commerce sites that provide convenience in transaction, and consumers can also provide reviews or opinions on products that purchased. These opinions can be used by consumers and producers. Consumers to know the advantages and disadvantages of particular feature of the product. Procuders can analyse own strengths and weaknesses as well as it's competitors products. Many opinions need a method that the reader can know the point of whole opinion. The idea emerged from review summarization that summarizes the overall opinion based on sentiment and features contain. In this study, the domain that become the main focus is about the digital camera. This research consisted of four steps 1) giving the knowledge to the system to recognize the semantic orientation of an opinion 2) indentify the features of product 3) indentify whether the opinion gives a positive or negative 4) summarizing the result. In this research discussed the methods such as Naï;ve Bayes for sentiment classification, and feature extraction algorithm based on Dependencies Analysis, which is one of the tools in Natural Language Processing (NLP) and knowledge based dictionary which is useful for handling implicit features. The end result of research is a summary that contains a bunch of reviews from consumers on the features and sentiment. With proposed method, accuration for sentiment classification giving 81.2 % for positive test data, 80.2 % for negative test data, and accuration for feature extraction reach 90.3 %.

Breast Cancer Detection with Gabor Features from Digital Mammograms

Directory of Open Access Journals (Sweden)

Yufeng Zheng

2010-01-01

Full Text Available A new breast cancer detection algorithm, named the “Gabor Cancer Detection” (GCD algorithm, utilizing Gabor features is proposed. Three major steps are involved in the GCD algorithm, preprocessing, segmentation (generating alarm segments, and classification (reducing false alarms. In preprocessing, a digital mammogram is down-sampled, quantized, denoised and enhanced. Nonlinear diffusion is used for noise suppression. In segmentation, a band-pass filter is formed by rotating a 1-D Gaussian filter (off center in frequency space, termed as “Circular Gaussian Filter” (CGF. A CGF can be uniquely characterized by specifying a central frequency and a frequency band. A mass or calcification is a space-occupying lesion and usually appears as a bright region on a mammogram. The alarm segments (suspicious to be masses/calcifications can be extracted out using a threshold that is adaptively decided upon the histogram analysis of the CGF-filtered mammogram. In classification, a Gabor filter bank is formed with five bands by four orientations (horizontal, vertical, 45 and 135 degree in Fourier frequency domain. For each mammographic image, twenty Gabor-filtered images are produced. A set of edge histogram descriptors (EHD are then extracted from 20 Gabor images for classification. An EHD signature is computed with four orientations of Gabor images along each band and five EHD signatures are then joined together to form an EHD feature vector of 20 dimensions. With the EHD features, the fuzzy C-means clustering technique and k-nearest neighbor (KNN classifier are used to reduce the number of false alarms. The experimental results tested on the DDSM database (University of South Florida show the promises of GCD algorithm in breast cancer detection, which achieved TP (true positive rate = 90% at FPI (false positives per image = 1.21 in mass detection; and TP = 93% at FPI = 1.19 in calcification detection.

Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

Directory of Open Access Journals (Sweden)

Yi An

2013-01-01

Full Text Available Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from [0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.

Preprocessing Moist Lignocellulosic Biomass for Biorefinery Feedstocks

Energy Technology Data Exchange (ETDEWEB)

Neal Yancey; Christopher T. Wright; Craig Conner; J. Richard Hess

2009-06-01

Biomass preprocessing is one of the primary operations in the feedstock assembly system of a lignocellulosic biorefinery. Preprocessing is generally accomplished using industrial grinders to format biomass materials into a suitable biorefinery feedstock for conversion to ethanol and other bioproducts. Many factors affect machine efficiency and the physical characteristics of preprocessed biomass. For example, moisture content of the biomass as received from the point of production has a significant impact on overall system efficiency and can significantly affect the characteristics (particle size distribution, flowability, storability, etc.) of the size-reduced biomass. Many different grinder configurations are available on the market, each with advantages under specific conditions. Ultimately, the capacity and/or efficiency of the grinding process can be enhanced by selecting the grinder configuration that optimizes grinder performance based on moisture content and screen size. This paper discusses the relationships of biomass moisture with respect to preprocessing system performance and product physical characteristics and compares data obtained on corn stover, switchgrass, and wheat straw as model feedstocks during Vermeer HG 200 grinder testing. During the tests, grinder screen configuration and biomass moisture content were varied and tested to provide a better understanding of their relative impact on machine performance and the resulting feedstock physical characteristics and uniformity relative to each crop tested.

Comparison of pre-processing methods for multiplex bead-based immunoassays.

Science.gov (United States)

Rausch, Tanja K; Schillert, Arne; Ziegler, Andreas; Lüking, Angelika; Zucht, Hans-Dieter; Schulz-Knappe, Peter

2016-08-11

High throughput protein expression studies can be performed using bead-based protein immunoassays, such as the Luminex® xMAP® technology. Technical variability is inherent to these experiments and may lead to systematic bias and reduced power. To reduce technical variability, data pre-processing is performed. However, no recommendations exist for the pre-processing of Luminex® xMAP® data. We compared 37 different data pre-processing combinations of transformation and normalization methods in 42 samples on 384 analytes obtained from a multiplex immunoassay based on the Luminex® xMAP® technology. We evaluated the performance of each pre-processing approach with 6 different performance criteria. Three performance criteria were plots. All plots were evaluated by 15 independent and blinded readers. Four different combinations of transformation and normalization methods performed well as pre-processing procedure for this bead-based protein immunoassay. The following combinations of transformation and normalization were suitable for pre-processing Luminex® xMAP® data in this study: weighted Box-Cox followed by quantile or robust spline normalization (rsn), asinh transformation followed by loess normalization and Box-Cox followed by rsn.

Nonredundant sparse feature extraction using autoencoders with receptive fields clustering.

Science.gov (United States)

Ayinde, Babajide O; Zurada, Jacek M

2017-09-01

This paper proposes new techniques for data representation in the context of deep learning using agglomerative clustering. Existing autoencoder-based data representation techniques tend to produce a number of encoding and decoding receptive fields of layered autoencoders that are duplicative, thereby leading to extraction of similar features, thus resulting in filtering redundancy. We propose a way to address this problem and show that such redundancy can be eliminated. This yields smaller networks and produces unique receptive fields that extract distinct features. It is also shown that autoencoders with nonnegativity constraints on weights are capable of extracting fewer redundant features than conventional sparse autoencoders. The concept is illustrated using conventional sparse autoencoder and nonnegativity-constrained autoencoders with MNIST digits recognition, NORB normalized-uniform object data and Yale face dataset. Copyright © 2017 Elsevier Ltd. All rights reserved.

Annotation-based feature extraction from sets of SBML models.

Science.gov (United States)

Alm, Rebekka; Waltemath, Dagmar; Wolfien, Markus; Wolkenhauer, Olaf; Henkel, Ron

2015-01-01

Model repositories such as BioModels Database provide computational models of biological systems for the scientific community. These models contain rich semantic annotations that link model entities to concepts in well-established bio-ontologies such as Gene Ontology. Consequently, thematically similar models are likely to share similar annotations. Based on this assumption, we argue that semantic annotations are a suitable tool to characterize sets of models. These characteristics improve model classification, allow to identify additional features for model retrieval tasks, and enable the comparison of sets of models. In this paper we discuss four methods for annotation-based feature extraction from model sets. We tested all methods on sets of models in SBML format which were composed from BioModels Database. To characterize each of these sets, we analyzed and extracted concepts from three frequently used ontologies, namely Gene Ontology, ChEBI and SBO. We find that three out of the methods are suitable to determine characteristic features for arbitrary sets of models: The selected features vary depending on the underlying model set, and they are also specific to the chosen model set. We show that the identified features map on concepts that are higher up in the hierarchy of the ontologies than the concepts used for model annotations. Our analysis also reveals that the information content of concepts in ontologies and their usage for model annotation do not correlate. Annotation-based feature extraction enables the comparison of model sets, as opposed to existing methods for model-to-keyword comparison, or model-to-model comparison.

A window-based time series feature extraction method.

Science.gov (United States)

Katircioglu-Öztürk, Deniz; Güvenir, H Altay; Ravens, Ursula; Baykal, Nazife

2017-10-01

This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Facial Feature Extraction Using Frequency Map Series in PCNN

Directory of Open Access Journals (Sweden)

Rencan Nie

2016-01-01

Full Text Available Pulse coupled neural network (PCNN has been widely used in image processing. The 3D binary map series (BMS generated by PCNN effectively describes image feature information such as edges and regional distribution, so BMS can be treated as the basis of extracting 1D oscillation time series (OTS for an image. However, the traditional methods using BMS did not consider the correlation of the binary sequence in BMS and the space structure for every map. By further processing for BMS, a novel facial feature extraction method is proposed. Firstly, consider the correlation among maps in BMS; a method is put forward to transform BMS into frequency map series (FMS, and the method lessens the influence of noncontinuous feature regions in binary images on OTS-BMS. Then, by computing the 2D entropy for every map in FMS, the 3D FMS is transformed into 1D OTS (OTS-FMS, which has good geometry invariance for the facial image, and contains the space structure information of the image. Finally, by analyzing the OTS-FMS, the standard Euclidean distance is used to measure the distances for OTS-FMS. Experimental results verify the effectiveness of OTS-FMS in facial recognition, and it shows better recognition performance than other feature extraction methods.

Automated Feature Extraction from Hyperspectral Imagery, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — The proposed activities will result in the development of a novel hyperspectral feature-extraction toolkit that will provide a simple, automated, and accurate...

On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery.

Science.gov (United States)

Qi, Baogui; Shi, Hao; Zhuang, Yin; Chen, He; Chen, Liang

2018-04-25

With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited.

On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery

Science.gov (United States)

Qi, Baogui; Zhuang, Yin; Chen, He; Chen, Liang

2018-01-01

With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited. PMID:29693585

Coding visual features extracted from video sequences.

Science.gov (United States)

Baroffio, Luca; Cesana, Matteo; Redondi, Alessandro; Tagliasacchi, Marco; Tubaro, Stefano

2014-05-01

Visual features are successfully exploited in several applications (e.g., visual search, object recognition and tracking, etc.) due to their ability to efficiently represent image content. Several visual analysis tasks require features to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required bit budget, while attaining a target level of efficiency. In this paper, we propose, for the first time, a coding architecture designed for local features (e.g., SIFT, SURF) extracted from video sequences. To achieve high coding efficiency, we exploit both spatial and temporal redundancy by means of intraframe and interframe coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. The proposed coding scheme can be conveniently adopted to implement the analyze-then-compress (ATC) paradigm in the context of visual sensor networks. That is, sets of visual features are extracted from video frames, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast to the traditional compress-then-analyze (CTA) paradigm, in which video sequences acquired at a node are compressed and then sent to a central unit for further processing. In this paper, we compare these coding paradigms using metrics that are routinely adopted to evaluate the suitability of visual features in the context of content-based retrieval, object recognition, and tracking. Experimental results demonstrate that, thanks to the significant coding gains achieved by the proposed coding scheme, ATC outperforms CTA with respect to all evaluation metrics.

Scientific data products and the data pre-processing subsystem of the Chang'e-3 mission

International Nuclear Information System (INIS)

Tan Xu; Liu Jian-Jun; Li Chun-Lai; Feng Jian-Qing; Ren Xin; Wang Fen-Fei; Yan Wei; Zuo Wei; Wang Xiao-Qian; Zhang Zhou-Bin

2014-01-01

The Chang'e-3 (CE-3) mission is China's first exploration mission on the surface of the Moon that uses a lander and a rover. Eight instruments that form the scientific payloads have the following objectives: (1) investigate the morphological features and geological structures at the landing site; (2) integrated in-situ analysis of minerals and chemical compositions; (3) integrated exploration of the structure of the lunar interior; (4) exploration of the lunar-terrestrial space environment, lunar surface environment and acquire Moon-based ultraviolet astronomical observations. The Ground Research and Application System (GRAS) is in charge of data acquisition and pre-processing, management of the payload in orbit, and managing the data products and their applications. The Data Pre-processing Subsystem (DPS) is a part of GRAS. The task of DPS is the pre-processing of raw data from the eight instruments that are part of CE-3, including channel processing, unpacking, package sorting, calibration and correction, identification of geographical location, calculation of probe azimuth angle, probe zenith angle, solar azimuth angle, and solar zenith angle and so on, and conducting quality checks. These processes produce Level 0, Level 1 and Level 2 data. The computing platform of this subsystem is comprised of a high-performance computing cluster, including a real-time subsystem used for processing Level 0 data and a post-time subsystem for generating Level 1 and Level 2 data. This paper describes the CE-3 data pre-processing method, the data pre-processing subsystem, data classification, data validity and data products that are used for scientific studies

Extraction and representation of common feature from uncertain facial expressions with cloud model.

Science.gov (United States)

Wang, Shuliang; Chi, Hehua; Yuan, Hanning; Geng, Jing

2017-12-01

Human facial expressions are key ingredient to convert an individual's innate emotion in communication. However, the variation of facial expressions affects the reliable identification of human emotions. In this paper, we present a cloud model to extract facial features for representing human emotion. First, the uncertainties in facial expression are analyzed in the context of cloud model. The feature extraction and representation algorithm is established under cloud generators. With forward cloud generator, facial expression images can be re-generated as many as we like for visually representing the extracted three features, and each feature shows different roles. The effectiveness of the computing model is tested on Japanese Female Facial Expression database. Three common features are extracted from seven facial expression images. Finally, the paper is concluded and remarked.

Feature Extraction and Fusion Using Deep Convolutional Neural Networks for Face Detection

Directory of Open Access Journals (Sweden)

Xiaojun Lu

2017-01-01

Full Text Available This paper proposes a method that uses feature fusion to represent images better for face detection after feature extraction by deep convolutional neural network (DCNN. First, with Clarifai net and VGG Net-D (16 layers, we learn features from data, respectively; then we fuse features extracted from the two nets. To obtain more compact feature representation and mitigate computation complexity, we reduce the dimension of the fused features by PCA. Finally, we conduct face classification by SVM classifier for binary classification. In particular, we exploit offset max-pooling to extract features with sliding window densely, which leads to better matches of faces and detection windows; thus the detection result is more accurate. Experimental results show that our method can detect faces with severe occlusion and large variations in pose and scale. In particular, our method achieves 89.24% recall rate on FDDB and 97.19% average precision on AFW.

Fixed kernel regression for voltammogram feature extraction

International Nuclear Information System (INIS)

Acevedo Rodriguez, F J; López-Sastre, R J; Gil-Jiménez, P; Maldonado Bascón, S; Ruiz-Reyes, N

2009-01-01

Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals

Effects of preprocessing method on TVOC emission of car mat

Science.gov (United States)

Wang, Min; Jia, Li

2013-02-01

The effects of the mat preprocessing method on total volatile organic compounds (TVOC) emission of car mat are studied in this paper. An appropriate TVOC emission period for car mat is suggested. The emission factors for total volatile organic compounds from three kinds of new car mats are discussed. The car mats are preprocessed by washing, baking and ventilation. When car mats are preprocessed by washing, the TVOC emission for all samples tested are lower than that preprocessed in other methods. The TVOC emission is in stable situation for a minimum of 4 days. The TVOC emitted from some samples may exceed 2500μg/kg. But the TVOC emitted from washed Polyamide (PA) and wool mat is less than 2500μg/kg. The emission factors of total volatile organic compounds (TVOC) are experimentally investigated in the case of different preprocessing methods. The air temperature in environment chamber and the water temperature for washing are important factors influencing on emission of car mats.

a Statistical Texture Feature for Building Collapse Information Extraction of SAR Image

Science.gov (United States)

Li, L.; Yang, H.; Chen, Q.; Liu, X.

2018-04-01

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

Dominant color and texture feature extraction for banknote discrimination

Science.gov (United States)

Wang, Junmin; Fan, Yangyu; Li, Ning

2017-07-01

Banknote discrimination with image recognition technology is significant in many applications. The traditional methods based on image recognition only recognize the banknote denomination without discriminating the counterfeit banknote. To solve this problem, we propose a systematical banknote discrimination approach with the dominant color and texture features. After capturing the visible and infrared images of the test banknote, we first implement the tilt correction based on the principal component analysis (PCA) algorithm. Second, we extract the dominant color feature of the visible banknote image to recognize the denomination. Third, we propose an adaptively weighted local binary pattern with "delta" tolerance algorithm to extract the texture features of the infrared banknote image. At last, we discriminate the genuine or counterfeit banknote by comparing the texture features between the test banknote and the benchmark banknote. The proposed approach is tested using 14,000 banknotes of six different denominations from Chinese yuan (CNY). The experimental results show 100% accuracy for denomination recognition and 99.92% accuracy for counterfeit banknote discrimination.

Feature extraction from multiple data sources using genetic programming.

Energy Technology Data Exchange (ETDEWEB)

Szymanski, J. J. (John J.); Brumby, Steven P.; Pope, P. A. (Paul A.); Eads, D. R. (Damian R.); Galassi, M. C. (Mark C.); Harvey, N. R. (Neal R.); Perkins, S. J. (Simon J.); Porter, R. B. (Reid B.); Theiler, J. P. (James P.); Young, A. C. (Aaron Cody); Bloch, J. J. (Jeffrey J.); David, N. A. (Nancy A.); Esch-Mosher, D. M. (Diana M.)

2002-01-01

Feature extration from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. The tool used is the GENetic Imagery Exploitation (GENIE) software, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land-cover features including towns, grasslands, wild fire burn scars, and several types of forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

Science.gov (United States)

Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel

2017-08-18

Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among

A Method to Measure the Bracelet Based on Feature Energy

Science.gov (United States)

Liu, Hongmin; Li, Lu; Wang, Zhiheng; Huo, Zhanqiang

2017-12-01

To measure the bracelet automatically, a novel method based on feature energy is proposed. Firstly, the morphological method is utilized to preprocess the image, and the contour consisting of a concentric circle is extracted. Then, a feature energy function, which is relevant to the distances from one pixel to the edge points, is defined taking into account the geometric properties of the concentric circle. The input image is subsequently transformed to the feature energy distribution map (FEDM) by computing the feature energy of each pixel. The center of the concentric circle is thus located by detecting the maximum on the FEDM; meanwhile, the radii of the concentric circle are determined according to the feature energy function of the center pixel. Finally, with the use of a calibration template, the internal diameter and thickness of the bracelet are measured. The experimental results show that the proposed method can measure the true sizes of the bracelet accurately with the simplicity, directness and robustness compared to the existing methods.

Preprocessing Algorithm for Deciphering Historical Inscriptions Using String Metric

Directory of Open Access Journals (Sweden)

Lorand Lehel Toth

2016-07-01

Full Text Available The article presents the improvements in the preprocessing part of the deciphering method (shortly preprocessing algorithm for historical inscriptions of unknown origin. Glyphs used in historical inscriptions changed through time; therefore, various versions of the same script may contain different glyphs for each grapheme. The purpose of the preprocessing algorithm is reducing the running time of the deciphering process by filtering out the less probable interpretations of the examined inscription. However, the first version of the preprocessing algorithm leads incorrect outcome or no result in the output in certain cases. Therefore, its improved version was developed to find the most similar words in the dictionary by relaying the search conditions more accurately, but still computationally effectively. Moreover, a sophisticated similarity metric used to determine the possible meaning of the unknown inscription is introduced. The results of the evaluations are also detailed.

Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

Directory of Open Access Journals (Sweden)

Xi Wenfei

2017-07-01

Full Text Available Feature point extraction technology has become a research hotspot in the photogrammetry and computer vision. The commonly used point feature extraction operators are SIFT operator, Forstner operator, Harris operator and Moravec operator, etc. With the high spatial resolution characteristics, UAV image is different from the traditional aviation image. Based on these characteristics of the unmanned aerial vehicle (UAV, this paper uses several operators referred above to extract feature points from the building images, grassland images, shrubbery images, and vegetable greenhouses images. Through the practical case analysis, the performance, advantages, disadvantages and adaptability of each algorithm are compared and analyzed by considering their speed and accuracy. Finally, the suggestions of how to adapt different algorithms in diverse environment are proposed.

Feature extraction for dynamic integration of classifiers

NARCIS (Netherlands)

Pechenizkiy, M.; Tsymbal, A.; Puuronen, S.; Patterson, D.W.

2007-01-01

Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique

UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES

Directory of Open Access Journals (Sweden)

A. Kianisarkaleh

2015-12-01

Full Text Available Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples exists. This paper illustrates the importance of selecting appropriate unlabeled samples that used in feature extraction methods. Also proposes a new method for unlabeled samples selection using spectral and spatial information. The proposed method has four parts including: PCA, prior classification, posterior classification and sample selection. As hyperspectral image passes these parts, selected unlabeled samples can be used in arbitrary feature extraction methods. The effectiveness of the proposed unlabeled selected samples in unsupervised and semisupervised feature extraction is demonstrated using two real hyperspectral datasets. Results show that through selecting appropriate unlabeled samples, the proposed method can improve the performance of feature extraction methods and increase classification accuracy.

DCTNet and PCANet for acoustic signal feature extraction

OpenAIRE

Xian, Yin; Thompson, Andrew; Sun, Xiaobai; Nowacek, Douglas; Nolte, Loren

2016-01-01

We introduce the use of DCTNet, an efficient approximation and alternative to PCANet, for acoustic signal classification. In PCANet, the eigenfunctions of the local sample covariance matrix (PCA) are used as filterbanks for convolution and feature extraction. When the eigenfunctions are well approximated by the Discrete Cosine Transform (DCT) functions, each layer of of PCANet and DCTNet is essentially a time-frequency representation. We relate DCTNet to spectral feature representation method...

Automatic feature extraction in large fusion databases by using deep learning approach

Energy Technology Data Exchange (ETDEWEB)

Farias, Gonzalo, E-mail: gonzalo.farias@ucv.cl [Pontificia Universidad Católica de Valparaíso, Valparaíso (Chile); Dormido-Canto, Sebastián [Departamento de Informática y Automática, UNED, Madrid (Spain); Vega, Jesús; Rattá, Giuseppe [Asociación EURATOM/CIEMAT Para Fusión, CIEMAT, Madrid (Spain); Vargas, Héctor; Hermosilla, Gabriel; Alfaro, Luis; Valencia, Agustín [Pontificia Universidad Católica de Valparaíso, Valparaíso (Chile)

2016-11-15

Highlights: • Feature extraction is a very critical stage in any machine learning algorithm. • The problem dimensionality can be reduced enormously when selecting suitable attributes. • Despite the importance of feature extraction, the process is commonly done manually by trial and error. • Fortunately, recent advances in deep learning approach have proposed an encouraging way to find a good feature representation automatically. • In this article, deep learning is applied to the TJ-II fusion database to get more robust and accurate classifiers in comparison to previous work. - Abstract: Feature extraction is one of the most important machine learning issues. Finding suitable attributes of datasets can enormously reduce the dimensionality of the input space, and from a computational point of view can help all of the following steps of pattern recognition problems, such as classification or information retrieval. However, the feature extraction step is usually performed manually. Moreover, depending on the type of data, we can face a wide range of methods to extract features. In this sense, the process to select appropriate techniques normally takes a long time. This work describes the use of recent advances in deep learning approach in order to find a good feature representation automatically. The implementation of a special neural network called sparse autoencoder and its application to two classification problems of the TJ-II fusion database is shown in detail. Results have shown that it is possible to get robust classifiers with a high successful rate, in spite of the fact that the feature space is reduced to less than 0.02% from the original one.

Automatic feature extraction in large fusion databases by using deep learning approach

International Nuclear Information System (INIS)

Farias, Gonzalo; Dormido-Canto, Sebastián; Vega, Jesús; Rattá, Giuseppe; Vargas, Héctor; Hermosilla, Gabriel; Alfaro, Luis; Valencia, Agustín

2016-01-01

Highlights: • Feature extraction is a very critical stage in any machine learning algorithm. • The problem dimensionality can be reduced enormously when selecting suitable attributes. • Despite the importance of feature extraction, the process is commonly done manually by trial and error. • Fortunately, recent advances in deep learning approach have proposed an encouraging way to find a good feature representation automatically. • In this article, deep learning is applied to the TJ-II fusion database to get more robust and accurate classifiers in comparison to previous work. - Abstract: Feature extraction is one of the most important machine learning issues. Finding suitable attributes of datasets can enormously reduce the dimensionality of the input space, and from a computational point of view can help all of the following steps of pattern recognition problems, such as classification or information retrieval. However, the feature extraction step is usually performed manually. Moreover, depending on the type of data, we can face a wide range of methods to extract features. In this sense, the process to select appropriate techniques normally takes a long time. This work describes the use of recent advances in deep learning approach in order to find a good feature representation automatically. The implementation of a special neural network called sparse autoencoder and its application to two classification problems of the TJ-II fusion database is shown in detail. Results have shown that it is possible to get robust classifiers with a high successful rate, in spite of the fact that the feature space is reduced to less than 0.02% from the original one.

Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

Science.gov (United States)

Khushaba, Rami N; Kodagoda, Sarath; Lal, Sara; Dissanayake, Gamini

2011-01-01

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.

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.

The 1996 ENDF pre-processing codes

International Nuclear Information System (INIS)

Cullen, D.E.

1996-01-01

The codes are named 'the Pre-processing' codes, because they are designed to pre-process ENDF/B data, for later, further processing for use in applications. This is a modular set of computer codes, each of which reads and writes evaluated nuclear data in the ENDF/B format. Each code performs one or more independent operations on the data, as described below. These codes are designed to be computer independent, and are presently operational on every type of computer from large mainframe computer to small personal computers, such as IBM-PC and Power MAC. The codes are available from the IAEA Nuclear Data Section, free of charge upon request. (author)

A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.

Science.gov (United States)

Zarei, Roozbeh; He, Jing; Siuly, Siuly; Zhang, Yanchun

2017-07-01

Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

Science.gov (United States)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

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.

A Study of Feature Extraction Using Divergence Analysis of Texture Features

Science.gov (United States)

Hallada, W. A.; Bly, B. G.; Boyd, R. K.; Cox, S.

1982-01-01

An empirical study of texture analysis for feature extraction and classification of high spatial resolution remotely sensed imagery (10 meters) is presented in terms of specific land cover types. The principal method examined is the use of spatial gray tone dependence (SGTD). The SGTD method reduces the gray levels within a moving window into a two-dimensional spatial gray tone dependence matrix which can be interpreted as a probability matrix of gray tone pairs. Haralick et al (1973) used a number of information theory measures to extract texture features from these matrices, including angular second moment (inertia), correlation, entropy, homogeneity, and energy. The derivation of the SGTD matrix is a function of: (1) the number of gray tones in an image; (2) the angle along which the frequency of SGTD is calculated; (3) the size of the moving window; and (4) the distance between gray tone pairs. The first three parameters were varied and tested on a 10 meter resolution panchromatic image of Maryville, Tennessee using the five SGTD measures. A transformed divergence measure was used to determine the statistical separability between four land cover categories forest, new residential, old residential, and industrial for each variation in texture parameters.

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach.

Science.gov (United States)

Nasir, Muhammad; Attique Khan, Muhammad; Sharif, Muhammad; Lali, Ikram Ullah; Saba, Tanzila; Iqbal, Tassawar

2018-02-21

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F-score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods. © 2018 Wiley Periodicals, Inc.

[Study of near infrared spectral preprocessing and wavelength selection methods for endometrial cancer tissue].

Science.gov (United States)

Zhao, Li-Ting; Xiang, Yu-Hong; Dai, Yin-Mei; Zhang, Zhuo-Yong

2010-04-01

Near infrared spectroscopy was applied to measure the tissue slice of endometrial tissues for collecting the spectra. A total of 154 spectra were obtained from 154 samples. The number of normal, hyperplasia, and malignant samples was 36, 60, and 58, respectively. Original near infrared spectra are composed of many variables, for example, interference information including instrument errors and physical effects such as particle size and light scatter. In order to reduce these influences, original spectra data should be performed with different spectral preprocessing methods to compress variables and extract useful information. So the methods of spectral preprocessing and wavelength selection have played an important role in near infrared spectroscopy technique. In the present paper the raw spectra were processed using various preprocessing methods including first derivative, multiplication scatter correction, Savitzky-Golay first derivative algorithm, standard normal variate, smoothing, and moving-window median. Standard deviation was used to select the optimal spectral region of 4 000-6 000 cm(-1). Then principal component analysis was used for classification. Principal component analysis results showed that three types of samples could be discriminated completely and the accuracy almost achieved 100%. This study demonstrated that near infrared spectroscopy technology and chemometrics method could be a fast, efficient, and novel means to diagnose cancer. The proposed methods would be a promising and significant diagnosis technique of early stage cancer.

DIFET: DISTRIBUTED FEATURE EXTRACTION TOOL FOR HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES

Directory of Open Access Journals (Sweden)

S. Eken

2017-11-01

Full Text Available In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

Feature extraction and sensor selection for NPP initiating event identification

International Nuclear Information System (INIS)

Lin, Ting-Han; Wu, Shun-Chi; Chen, Kuang-You; Chou, Hwai-Pwu

2017-01-01

Highlights: • A two-stage feature extraction scheme for NPP initiating event identification. • With stBP, interrelations among the sensors can be retained for identification. • With dSFS, sensors that are crucial for identification can be efficiently selected. • Efficacy of the scheme is illustrated with data from the Maanshan NPP simulator. - Abstract: Initiating event identification is essential in managing nuclear power plant (NPP) severe accidents. In this paper, a novel two-stage feature extraction scheme that incorporates the proposed sensor type-wise block projection (stBP) and deflatable sequential forward selection (dSFS) is used to elicit the discriminant information in the data obtained from various NPP sensors to facilitate event identification. With the stBP, the primal features can be extracted without eliminating the interrelations among the sensors of the same type. The extracted features are then subjected to a further dimensionality reduction by selecting the sensors that are most relevant to the events under consideration. This selection is not easy, and a combinatorial optimization technique is normally required. With the dSFS, an optimal sensor set can be found with less computational load. Moreover, its sensor deflation stage allows sensors in the preselected set to be iteratively refined to avoid being trapped into a local optimum. Results from detailed experiments containing data of 12 event categories and a total of 112 events generated with a Taiwan’s Maanshan NPP simulator are presented to illustrate the efficacy of the proposed scheme.

Human listening studies reveal insights into object features extracted by echolocating dolphins

Science.gov (United States)

Delong, Caroline M.; Au, Whitlow W. L.; Roitblat, Herbert L.

2004-05-01

Echolocating dolphins extract object feature information from the acoustic parameters of object echoes. However, little is known about which object features are salient to dolphins or how they extract those features. To gain insight into how dolphins might be extracting feature information, human listeners were presented with echoes from objects used in a dolphin echoic-visual cross-modal matching task. Human participants performed a task similar to the one the dolphin had performed; however, echoic samples consisting of 23-echo trains were presented via headphones. The participants listened to the echoic sample and then visually selected the correct object from among three alternatives. The participants performed as well as or better than the dolphin (M=88.0% correct), and reported using a combination of acoustic cues to extract object features (e.g., loudness, pitch, timbre). Participants frequently reported using the pattern of aural changes in the echoes across the echo train to identify the shape and structure of the objects (e.g., peaks in loudness or pitch). It is likely that dolphins also attend to the pattern of changes across echoes as objects are echolocated from different angles.

Arabic text preprocessing for the natural language processing applications

International Nuclear Information System (INIS)

Awajan, A.

2007-01-01

A new approach for processing vowelized and unvowelized Arabic texts in order to prepare them for Natural Language Processing (NLP) purposes is described. The developed approach is rule-based and made up of four phases: text tokenization, word light stemming, word's morphological analysis and text annotation. The first phase preprocesses the input text in order to isolate the words and represent them in a formal way. The second phase applies a light stemmer in order to extract the stem of each word by eliminating the prefixes and suffixes. The third phase is a rule-based morphological analyzer that determines the root and the morphological pattern for each extracted stem. The last phase produces an annotated text where each word is tagged with its morphological attributes. The preprocessor presented in this paper is capable of dealing with vowelized and unvowelized words, and provides the input words along with relevant linguistics information needed by different applications. It is designed to be used with different NLP applications such as machine translation text summarization, text correction, information retrieval and automatic vowelization of Arabic Text. (author)

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 Voron...

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

Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

Science.gov (United States)

Wang, Yongzhi; Ma, Yuqing; Zhu, A.-xing; Zhao, Hui; Liao, Lixia

2018-05-01

Facade features represent segmentations of building surfaces and can serve as a building framework. Extracting facade features from three-dimensional (3D) point cloud data (3D PCD) is an efficient method for 3D building modeling. By combining the advantages of 3D PCD and two-dimensional optical images, this study describes the creation of a highly accurate building facade feature extraction method from 3D PCD with a focus on structural information. The new extraction method involves three major steps: image feature extraction, exploration of the mapping method between the image features and 3D PCD, and optimization of the initial 3D PCD facade features considering structural information. Results show that the new method can extract the 3D PCD facade features of buildings more accurately and continuously. The new method is validated using a case study. In addition, the effectiveness of the new method is demonstrated by comparing it with the range image-extraction method and the optical image-extraction method in the absence of structural information. The 3D PCD facade features extracted by the new method can be applied in many fields, such as 3D building modeling and building information modeling.

The algorithm of fast image stitching based on multi-feature extraction

Science.gov (United States)

Yang, Chunde; Wu, Ge; Shi, Jing

2018-05-01

This paper proposed an improved image registration method combining Hu-based invariant moment contour information and feature points detection, aiming to solve the problems in traditional image stitching algorithm, such as time-consuming feature points extraction process, redundant invalid information overload and inefficiency. First, use the neighborhood of pixels to extract the contour information, employing the Hu invariant moment as similarity measure to extract SIFT feature points in those similar regions. Then replace the Euclidean distance with Hellinger kernel function to improve the initial matching efficiency and get less mismatching points, further, estimate affine transformation matrix between the images. Finally, local color mapping method is adopted to solve uneven exposure, using the improved multiresolution fusion algorithm to fuse the mosaic images and realize seamless stitching. Experimental results confirm high accuracy and efficiency of method proposed in this paper.

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

OpenAIRE

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting ...

Texture Feature Analysis for Different Resolution Level of Kidney Ultrasound Images

Science.gov (United States)

Kairuddin, Wan Nur Hafsha Wan; Mahmud, Wan Mahani Hafizah Wan

2017-08-01

Image feature extraction is a technique to identify the characteristic of the image. The objective of this work is to discover the texture features that best describe a tissue characteristic of a healthy kidney from ultrasound (US) image. Three ultrasound machines that have different specifications are used in order to get a different quality (different resolution) of the image. Initially, the acquired images are pre-processed to de-noise the speckle to ensure the image preserve the pixels in a region of interest (ROI) for further extraction. Gaussian Low- pass Filter is chosen as the filtering method in this work. 150 of enhanced images then are segmented by creating a foreground and background of image where the mask is created to eliminate some unwanted intensity values. Statistical based texture features method is used namely Intensity Histogram (IH), Gray-Level Co-Occurance Matrix (GLCM) and Gray-level run-length matrix (GLRLM).This method is depends on the spatial distribution of intensity values or gray levels in the kidney region. By using One-Way ANOVA in SPSS, the result indicated that three features (Contrast, Difference Variance and Inverse Difference Moment Normalized) from GLCM are not statistically significant; this concludes that these three features describe a healthy kidney characteristics regardless of the ultrasound image quality.

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface

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Charles Yaacoub

2017-01-01

Full Text Available Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5% while improving the accuracy, sensitivity, specificity, and precision of the classifier.

A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface.

Science.gov (United States)

Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

2017-01-23

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.

Research on feature extraction techniques of Hainan Li brocade pattern

Science.gov (United States)

Zhou, Yuping; Chen, Fuqiang; Zhou, Yuhua

2016-03-01

Hainan Li brocade skills has been listed as world non-material cultural heritage preservation, therefore, the research on Hainan Li brocade patterns plays an important role in Li brocade culture inheritance. The meaning of Li brocade patterns was analyzed and the shape feature extraction techniques to original Li brocade patterns were advanced in this paper, based on the contour tracking algorithm. First, edge detection was made on the design patterns, and then the morphological closing operation was used to smooth the image, and finally contour tracking was used to extract the outer contours of Li brocade patterns. The extracted contour features were processed by means of morphology, and digital characteristics of contours are obtained by invariant moments. At last, different patterns of Li brocade design are briefly analyzed according to the digital characteristics. The results showed that the pattern extraction method to Li brocade pattern shapes is feasible and effective according to above method.

Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

Directory of Open Access Journals (Sweden)

Seungsoo Nam

2018-01-01

Full Text Available This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector, which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures.

Image feature extraction based on the camouflage effectiveness evaluation

Science.gov (United States)

Yuan, Xin; Lv, Xuliang; Li, Ling; Wang, Xinzhu; Zhang, Zhi

2018-04-01

The key step of camouflage effectiveness evaluation is how to combine the human visual physiological features, psychological features to select effectively evaluation indexes. Based on the predecessors' camo comprehensive evaluation method, this paper chooses the suitable indexes combining with the image quality awareness, and optimizes those indexes combining with human subjective perception. Thus, it perfects the theory of index extraction.

Feature-extraction algorithms for the PANDA electromagnetic calorimeter

NARCIS (Netherlands)

Kavatsyuk, M.; Guliyev, E.; Lemmens, P. J. J.; Loehner, H.; Poelman, T. P.; Tambave, G.; Yu, B

2009-01-01

The feature-extraction algorithms are discussed which have been developed for the digital front-end electronics of the electromagnetic calorimeter of the PANDA detector at the future FAIR facility. Performance parameters have been derived in test measurements with cosmic rays, particle and photon

A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis: stem cell and melanoma cancer studies

Directory of Open Access Journals (Sweden)

Tong Dong L

2011-09-01

Full Text Available Abstract Introduction Raw spectral data from matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF with MS profiling techniques usually contains complex information not readily providing biological insight into disease. The association of identified features within raw data to a known peptide is extremely difficult. Data preprocessing to remove uncertainty characteristics in the data is normally required before performing any further analysis. This study proposes an alternative yet simple solution to preprocess raw MALDI-TOF-MS data for identification of candidate marker ions. Two in-house MALDI-TOF-MS data sets from two different sample sources (melanoma serum and cord blood plasma are used in our study. Method Raw MS spectral profiles were preprocessed using the proposed approach to identify peak regions in the spectra. The preprocessed data was then analysed using bespoke machine learning algorithms for data reduction and ion selection. Using the selected ions, an ANN-based predictive model was constructed to examine the predictive power of these ions for classification. Results Our model identified 10 candidate marker ions for both data sets. These ion panels achieved over 90% classification accuracy on blind validation data. Receiver operating characteristics analysis was performed and the area under the curve for melanoma and cord blood classifiers was 0.991 and 0.986, respectively. Conclusion The results suggest that our data preprocessing technique removes unwanted characteristics of the raw data, while preserving the predictive components of the data. Ion identification analysis can be carried out using MALDI-TOF-MS data with the proposed data preprocessing technique coupled with bespoke algorithms for data reduction and ion selection.

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.

CSS Preprocessing: Tools and Automation Techniques

Directory of Open Access Journals (Sweden)

Ricardo Queirós

2018-01-01

Full Text Available Cascading Style Sheets (CSS is a W3C specification for a style sheet language used for describing the presentation of a document written in a markup language, more precisely, for styling Web documents. However, in the last few years, the landscape for CSS development has changed dramatically with the appearance of several languages and tools aiming to help developers build clean, modular and performance-aware CSS. These new approaches give developers mechanisms to preprocess CSS rules through the use of programming constructs, defined as CSS preprocessors, with the ultimate goal to bring those missing constructs to the CSS realm and to foster stylesheets structured programming. At the same time, a new set of tools appeared, defined as postprocessors, for extension and automation purposes covering a broad set of features ranging from identifying unused and duplicate code to applying vendor prefixes. With all these tools and techniques in hands, developers need to provide a consistent workflow to foster CSS modular coding. This paper aims to present an introductory survey on the CSS processors. The survey gathers information on a specific set of processors, categorizes them and compares their features regarding a set of predefined criteria such as: maturity, coverage and performance. Finally, we propose a basic set of best practices in order to setup a simple and pragmatic styling code workflow.

Real-time topic-aware influence maximization using preprocessing.

Science.gov (United States)

Chen, Wei; Lin, Tian; Yang, Cheng

2016-01-01

Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated in networks are typically mixtures of topics. In this paper, we focus on the topic-aware influence maximization task. In particular, we study preprocessing methods to avoid redoing influence maximization for each mixture from scratch. We explore two preprocessing algorithms with theoretical justifications. Our empirical results on data obtained in a couple of existing studies demonstrate that one of our algorithms stands out as a strong candidate providing microsecond online response time and competitive influence spread, with reasonable preprocessing effort.

A Generic multi-dimensional feature extraction method using multiobjective genetic programming.

Science.gov (United States)

Zhang, Yang; Rockett, Peter I

2009-01-01

In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.

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.

An Accurate Integral Method for Vibration Signal Based on Feature Information Extraction

Directory of Open Access Journals (Sweden)

Yong Zhu

2015-01-01

Full Text Available After summarizing the advantages and disadvantages of current integral methods, a novel vibration signal integral method based on feature information extraction was proposed. This method took full advantage of the self-adaptive filter characteristic and waveform correction feature of ensemble empirical mode decomposition in dealing with nonlinear and nonstationary signals. This research merged the superiorities of kurtosis, mean square error, energy, and singular value decomposition on signal feature extraction. The values of the four indexes aforementioned were combined into a feature vector. Then, the connotative characteristic components in vibration signal were accurately extracted by Euclidean distance search, and the desired integral signals were precisely reconstructed. With this method, the interference problem of invalid signal such as trend item and noise which plague traditional methods is commendably solved. The great cumulative error from the traditional time-domain integral is effectively overcome. Moreover, the large low-frequency error from the traditional frequency-domain integral is successfully avoided. Comparing with the traditional integral methods, this method is outstanding at removing noise and retaining useful feature information and shows higher accuracy and superiority.

Compact Circuit Preprocesses Accelerometer Output

Science.gov (United States)

Bozeman, Richard J., Jr.

1993-01-01

Compact electronic circuit transfers dc power to, and preprocesses ac output of, accelerometer and associated preamplifier. Incorporated into accelerometer case during initial fabrication or retrofit onto commercial accelerometer. Made of commercial integrated circuits and other conventional components; made smaller by use of micrologic and surface-mount technology.

An enhanced PSO-DEFS based feature selection with biometric authentication for identification of diabetic retinopathy

Directory of Open Access Journals (Sweden)

Umarani Balakrishnan

2016-11-01

Full Text Available Recently, automatic diagnosis of diabetic retinopathy (DR from the retinal image is the most significant research topic in the medical applications. Diabetic macular edema (DME is the major reason for the loss of vision in patients suffering from DR. Early identification of the DR enables to prevent the vision loss and encourage diabetic control activities. Many techniques are developed to diagnose the DR. The major drawbacks of the existing techniques are low accuracy and high time complexity. To overcome these issues, this paper proposes an enhanced particle swarm optimization-differential evolution feature selection (PSO-DEFS based feature selection approach with biometric authentication for the identification of DR. Initially, a hybrid median filter (HMF is used for pre-processing the input images. Then, the pre-processed images are embedded with each other by using least significant bit (LSB for authentication purpose. Simultaneously, the image features are extracted using convoluted local tetra pattern (CLTrP and Tamura features. Feature selection is performed using PSO-DEFS and PSO-gravitational search algorithm (PSO-GSA to reduce time complexity. Based on some performance metrics, the PSO-DEFS is chosen as a better choice for feature selection. The feature selection is performed based on the fitness value. A multi-relevance vector machine (M-RVM is introduced to classify the 13 normal and 62 abnormal images among 75 images from 60 patients. Finally, the DR patients are further classified by M-RVM. The experimental results exhibit that the proposed approach achieves better accuracy, sensitivity, and specificity than the existing techniques.

Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI

Science.gov (United States)

Chirra, Prathyush; Leo, Patrick; Yim, Michael; Bloch, B. Nicolas; Rastinehad, Ardeshir R.; Purysko, Andrei; Rosen, Mark; Madabhushi, Anant; Viswanath, Satish

2018-02-01

The recent advent of radiomics has enabled the development of prognostic and predictive tools which use routine imaging, but a key question that still remains is how reproducible these features may be across multiple sites and scanners. This is especially relevant in the context of MRI data, where signal intensity values lack tissue specific, quantitative meaning, as well as being dependent on acquisition parameters (magnetic field strength, image resolution, type of receiver coil). In this paper we present the first empirical study of the reproducibility of 5 different radiomic feature families in a multi-site setting; specifically, for characterizing prostate MRI appearance. Our cohort comprised 147 patient T2w MRI datasets from 4 different sites, all of which were first pre-processed to correct acquisition-related for artifacts such as bias field, differing voxel resolutions, as well as intensity drift (non-standardness). 406 3D voxel wise radiomic features were extracted and evaluated in a cross-site setting to determine how reproducible they were within a relatively homogeneous non-tumor tissue region; using 2 different measures of reproducibility: Multivariate Coefficient of Variation and Instability Score. Our results demonstrated that Haralick features were most reproducible between all 4 sites. By comparison, Laws features were among the least reproducible between sites, as well as performing highly variably across their entire parameter space. Similarly, the Gabor feature family demonstrated good cross-site reproducibility, but for certain parameter combinations alone. These trends indicate that despite extensive pre-processing, only a subset of radiomic features and associated parameters may be reproducible enough for use within radiomics-based machine learning classifier schemes.

Towards Home-Made Dictionaries for Musical Feature Extraction

DEFF Research Database (Denmark)

Harbo, Anders La-Cour

2003-01-01

arguably unnecessary limitations on the ability of the transform to extract and identify features. However, replacing the nicely structured dictionary of the Fourier transform (or indeed other nice transform such as the wavelet transform) with a home-made dictionary is a dangerous task, since even the most...

A threshold auto-adjustment algorithm of feature points extraction based on grid

Science.gov (United States)

Yao, Zili; Li, Jun; Dong, Gaojie

2018-02-01

When dealing with high-resolution digital images, detection of feature points is usually the very first important step. Valid feature points depend on the threshold. If the threshold is too low, plenty of feature points will be detected, and they may be aggregated in the rich texture regions, which consequently not only affects the speed of feature description, but also aggravates the burden of following processing; if the threshold is set high, the feature points in poor texture area will lack. To solve these problems, this paper proposes a threshold auto-adjustment method of feature extraction based on grid. By dividing the image into numbers of grid, threshold is set in every local grid for extracting the feature points. When the number of feature points does not meet the threshold requirement, the threshold will be adjusted automatically to change the final number of feature points The experimental results show that feature points produced by our method is more uniform and representative, which avoids the aggregation of feature points and greatly reduces the complexity of following work.

Preprocessing of emotional visual information in the human piriform cortex.

Science.gov (United States)

Schulze, Patrick; Bestgen, Anne-Kathrin; Lech, Robert K; Kuchinke, Lars; Suchan, Boris

2017-08-23

This study examines the processing of visual information by the olfactory system in humans. Recent data point to the processing of visual stimuli by the piriform cortex, a region mainly known as part of the primary olfactory cortex. Moreover, the piriform cortex generates predictive templates of olfactory stimuli to facilitate olfactory processing. This study fills the gap relating to the question whether this region is also capable of preprocessing emotional visual information. To gain insight into the preprocessing and transfer of emotional visual information into olfactory processing, we recorded hemodynamic responses during affective priming using functional magnetic resonance imaging (fMRI). Odors of different valence (pleasant, neutral and unpleasant) were primed by images of emotional facial expressions (happy, neutral and disgust). Our findings are the first to demonstrate that the piriform cortex preprocesses emotional visual information prior to any olfactory stimulation and that the emotional connotation of this preprocessing is subsequently transferred and integrated into an extended olfactory network for olfactory processing.

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.

Extracted facial feature of racial closely related faces

Science.gov (United States)

Liewchavalit, Chalothorn; Akiba, Masakazu; Kanno, Tsuneo; Nagao, Tomoharu

2010-02-01

Human faces contain a lot of demographic information such as identity, gender, age, race and emotion. Human being can perceive these pieces of information and use it as an important clue in social interaction with other people. Race perception is considered the most delicacy and sensitive parts of face perception. There are many research concerning image-base race recognition, but most of them are focus on major race group such as Caucasoid, Negroid and Mongoloid. This paper focuses on how people classify race of the racial closely related group. As a sample of racial closely related group, we choose Japanese and Thai face to represents difference between Northern and Southern Mongoloid. Three psychological experiment was performed to study the strategies of face perception on race classification. As a result of psychological experiment, it can be suggested that race perception is an ability that can be learn. Eyes and eyebrows are the most attention point and eyes is a significant factor in race perception. The Principal Component Analysis (PCA) was performed to extract facial features of sample race group. Extracted race features of texture and shape were used to synthesize faces. As the result, it can be suggested that racial feature is rely on detailed texture rather than shape feature. This research is a indispensable important fundamental research on the race perception which are essential in the establishment of human-like race recognition system.

Feature extraction using convolutional neural network for classifying breast density in mammographic images

Science.gov (United States)

Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

2017-03-01

Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is

Image Processing and Features Extraction of Fingerprint Images ...

African Journals Online (AJOL)

To demonstrate the importance of the image processing of fingerprint images prior to image enrolment or comparison, the set of fingerprint images in databases (a) and (b) of the FVC (Fingerprint Verification Competition) 2000 database were analyzed using a features extraction algorithm. This paper presents the results of ...

Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

Science.gov (United States)

Chan, Yi-Tung; Wang, Shuenn-Jyi; Tsai, Chung-Hsien

2017-09-01

Public safety is a matter of national security and people's livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method's performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.

Javanese Character Feature Extraction Based on Shape Energy

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Galih Hendra Wibowo

2017-07-01

Full Text Available Javanese character is one of Indonesia's noble culture, especially in Java. However, the number of Javanese people who are able to read the letter has decreased so that there need to be conservation efforts in the form of a system that is able to recognize the characters. One solution to these problem lies in Optical Character Recognition (OCR studies, where one of its heaviest points lies in feature extraction which is to distinguish each character. Shape Energy is one of feature extraction method with the basic idea of how the character can be distinguished simply through its skeleton. Based on the basic idea, then the development of feature extraction is done based on its components to produce an angular histogram with various variations of multiples angle. Furthermore, the performance test of this method and its basic method is performed in Javanese character dataset, which has been obtained from various images, is 240 data with 19 labels by using K-Nearest Neighbors as its classification method. Performance values were obtained based on the accuracy which is generated through the Cross-Validation process of 80.83% in the angular histogram with an angle of 20 degrees, 23% better than Shape Energy. In addition, other test results show that this method is able to recognize rotated character with the lowest performance value of 86% at 180-degree rotation and the highest performance value of 96.97% at 90-degree rotation. It can be concluded that this method is able to improve the performance of Shape Energy in the form of recognition of Javanese characters as well as robust to the rotation.

Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features

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Eman Magdy

2015-01-01

Full Text Available Computer-aided diagnostic (CAD systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients’ lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR for classification step. Finally, K-nearest neighbour (KNN, support vector machine (SVM, naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.

A feature extraction algorithm based on corner and spots in self-driving vehicles

Directory of Open Access Journals (Sweden)

Yupeng FENG

2017-06-01

Full Text Available To solve the poor real-time performance problem of the visual odometry based on embedded system with limited computing resources, an image matching method based on Harris and SIFT is proposed, namely the Harris-SIFT algorithm. On the basis of the review of SIFT algorithm, the principle of Harris-SIFT algorithm is provided. First, Harris algorithm is used to extract the corners of the image as candidate feature points, and scale invariant feature transform (SIFT features are extracted from those candidate feature points. At last, through an example, the algorithm is simulated by Matlab, then the complexity and other performance of the algorithm are analyzed. The experimental results show that the proposed method reduces the computational complexity and improves the speed of feature extraction. Harris-SIFT algorithm can be used in the real-time vision odometer system, and will bring about a wide application of visual odometry in embedded navigation system.

Chemical-induced disease relation extraction with various linguistic features.

Science.gov (United States)

Gu, Jinghang; Qian, Longhua; Zhou, Guodong

2016-01-01

Understanding the relations between chemicals and diseases is crucial in various biomedical tasks such as new drug discoveries and new therapy developments. While manually mining these relations from the biomedical literature is costly and time-consuming, such a procedure is often difficult to keep up-to-date. To address these issues, the BioCreative-V community proposed a challenging task of automatic extraction of chemical-induced disease (CID) relations in order to benefit biocuration. This article describes our work on the CID relation extraction task on the BioCreative-V tasks. We built a machine learning based system that utilized simple yet effective linguistic features to extract relations with maximum entropy models. In addition to leveraging various features, the hypernym relations between entity concepts derived from the Medical Subject Headings (MeSH)-controlled vocabulary were also employed during both training and testing stages to obtain more accurate classification models and better extraction performance, respectively. We demoted relation extraction between entities in documents to relation extraction between entity mentions. In our system, pairs of chemical and disease mentions at both intra- and inter-sentence levels were first constructed as relation instances for training and testing, then two classification models at both levels were trained from the training examples and applied to the testing examples. Finally, we merged the classification results from mention level to document level to acquire final relations between chemicals and diseases. Our system achieved promisingF-scores of 60.4% on the development dataset and 58.3% on the test dataset using gold-standard entity annotations, respectively. Database URL:https://github.com/JHnlp/BC5CIDTask. © The Author(s) 2016. Published by Oxford University Press.

Analysis of Time n Frequency EEG Feature Extraction Methods for Mental Task Classification

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Caglar Uyulan

2017-01-01

Full Text Available Many endogenous and external components may affect the physiological, mental and behavioral states in humans. Monitoring tools are required to evaluate biomarkers, identify biological events, and predict their outcomes. Being one of the valuable indicators, brain biomarkers derived from temporal or spectral electroencephalography (EEG signals processing, allow for the classification of mental disorders and mental tasks. An EEG signal has a nonstationary nature and individual frequency feature, hence it can be concluded that each subject has peculiar timing and data to extract unique features. In order to classify data, which are collected by performing four mental task (reciting the alphabet backwards, imagination of rotation of a cube, imagination of right hand movements (open/close and performing mathematical operations, discriminative features were extracted using four competitive time-frequency techniques; Wavelet Packet Decomposition (WPD, Morlet Wavelet Transform (MWT, Short Time Fourier Transform (STFT and Wavelet Filter Bank (WFB, respectively. The extracted features using both time and frequency domain information were then reduced using a principal component analysis for subset reduction. Finally, the reduced subsets were fed into a multi-layer perceptron neural network (MP-NN trained with back propagation (BP algorithm to generate a predictive model. This study mainly focuses on comparing the relative performance of time-frequency feature extraction methods that are used to classify mental tasks. The real-time (RT conducted experimental results underlined that the WPD feature extraction method outperforms with 92% classification accuracy compared to three other aforementioned methods for four different mental tasks.

An Effective Measured Data Preprocessing Method in Electrical Impedance Tomography

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Chenglong Yu

2014-01-01

Full Text Available As an advanced process detection technology, electrical impedance tomography (EIT has widely been paid attention to and studied in the industrial fields. But the EIT techniques are greatly limited to the low spatial resolutions. This problem may result from the incorrect preprocessing of measuring data and lack of general criterion to evaluate different preprocessing processes. In this paper, an EIT data preprocessing method is proposed by all rooting measured data and evaluated by two constructed indexes based on all rooted EIT measured data. By finding the optimums of the two indexes, the proposed method can be applied to improve the EIT imaging spatial resolutions. In terms of a theoretical model, the optimal rooting times of the two indexes range in [0.23, 0.33] and in [0.22, 0.35], respectively. Moreover, these factors that affect the correctness of the proposed method are generally analyzed. The measuring data preprocessing is necessary and helpful for any imaging process. Thus, the proposed method can be generally and widely used in any imaging process. Experimental results validate the two proposed indexes.

SAR Data Fusion Imaging Method Oriented to Target Feature Extraction

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Yang Wei

2015-02-01

Full Text Available To deal with the difficulty for target outlines extracting precisely due to neglect of target scattering characteristic variation during the processing of high-resolution space-borne SAR data, a novel fusion imaging method is proposed oriented to target feature extraction. Firstly, several important aspects that affect target feature extraction and SAR image quality are analyzed, including curved orbit, stop-and-go approximation, atmospheric delay, and high-order residual phase error. Furthermore, the corresponding compensation methods are addressed as well. Based on the analysis, the mathematical model of SAR echo combined with target space-time spectrum is established for explaining the space-time-frequency change rule of target scattering characteristic. Moreover, a fusion imaging strategy and method under high-resolution and ultra-large observation angle range conditions are put forward to improve SAR quality by fusion processing in range-doppler and image domain. Finally, simulations based on typical military targets are used to verify the effectiveness of the fusion imaging method.

Sensor-Based Auto-Focusing System Using Multi-Scale Feature Extraction and Phase Correlation Matching

Directory of Open Access Journals (Sweden)

Jinbeum Jang

2015-03-01

Full Text Available This paper presents a novel auto-focusing system based on a CMOS sensor containing pixels with different phases. Robust extraction of features in a severely defocused image is the fundamental problem of a phase-difference auto-focusing system. In order to solve this problem, a multi-resolution feature extraction algorithm is proposed. Given the extracted features, the proposed auto-focusing system can provide the ideal focusing position using phase correlation matching. The proposed auto-focusing (AF algorithm consists of four steps: (i acquisition of left and right images using AF points in the region-of-interest; (ii feature extraction in the left image under low illumination and out-of-focus blur; (iii the generation of two feature images using the phase difference between the left and right images; and (iv estimation of the phase shifting vector using phase correlation matching. Since the proposed system accurately estimates the phase difference in the out-of-focus blurred image under low illumination, it can provide faster, more robust auto focusing than existing systems.

Analysis of muscle fatigue conditions using time-frequency images and GLCM features

Directory of Open Access Journals (Sweden)

Karthick P.A.

2016-09-01

Full Text Available In this work, an attempt has been made to differentiate muscle non-fatigue and fatigue conditions using sEMG signals and texture representation of the time-frequency images. The sEMG signals are recorded from the biceps brachii muscle of 25 healthy adult volunteers during dynamic fatiguing contraction. The first and last curls of these signals are considered as the non-fatigue and fatigue zones, respectively. These signals are preprocessed and the time-frequency spectrum is computed using short time fourier transform (STFT. Gray-Level Co-occurrence Matrix (GLCM is extracted from low (15–45 Hz, medium (46–95 Hz and high (96–150 Hz frequency bands of the time-frequency images. Further, the features such as contrast, correlation, energy and homogeneity are calculated from the resultant matrices. The results show that the high frequency band based features are able to differentiate non-fatigue and fatigue conditions. The features such as correlation, contrast and homogeneity extracted at angles 0°, 45°, 90°, and 135° are found to be distinct with high statistical significance (p < 0.0001. Hence, this framework can be used for analysis of neuromuscular disorders.

Feature extraction from mammographic images using fast marching methods

International Nuclear Information System (INIS)

Bottigli, U.; Golosio, B.

2002-01-01

Features extraction from medical images represents a fundamental step for shape recognition and diagnostic support. The present work faces the problem of the detection of large features, such as massive lesions and organ contours, from mammographic images. The regions of interest are often characterized by an average grayness intensity that is different from the surrounding. In most cases, however, the desired features cannot be extracted by simple gray level thresholding, because of image noise and non-uniform density of the surrounding tissue. In this work, edge detection is achieved through the fast marching method (Level Set Methods and Fast Marching Methods, Cambridge University Press, Cambridge, 1999), which is based on the theory of interface evolution. Starting from a seed point in the shape of interest, a front is generated which evolves according to an appropriate speed function. Such function is expressed in terms of geometric properties of the evolving interface and of image properties, and should become zero when the front reaches the desired boundary. Some examples of application of such method to mammographic images from the CALMA database (Nucl. Instr. and Meth. A 460 (2001) 107) are presented here and discussed

Feature extraction for SAR target recognition based on supervised manifold learning

International Nuclear Information System (INIS)

Du, C; Zhou, S; Sun, J; Zhao, J

2014-01-01

On the basis of manifold learning theory, a new feature extraction method for Synthetic aperture radar (SAR) target recognition is proposed. First, the proposed algorithm estimates the within-class and between-class local neighbourhood surrounding each SAR sample. After computing the local tangent space for each neighbourhood, the proposed algorithm seeks for the optimal projecting matrix by preserving the local within-class property and simultaneously maximizing the local between-class separability. The use of uncorrelated constraint can also enhance the discriminating power of the optimal projecting matrix. Finally, the nearest neighbour classifier is applied to recognize SAR targets in the projected feature subspace. Experimental results on MSTAR datasets demonstrate that the proposed method can provide a higher recognition rate than traditional feature extraction algorithms in SAR target recognition

Effect of packaging on physicochemical characteristics of irradiated pre-processed chicken

International Nuclear Information System (INIS)

Jiang Xiujie; Zhang Dongjie; Zhang Dequan; Li Shurong; Gao Meixu; Wang Zhidong

2011-01-01

To explore the effect of modified atmosphere packaging and antioxidants on the physicochemical characteristics of irradiated pre-processed chicken, the pre-processed chicken was added antioxidants first, and then packaged in common, vacuum and gas respectively, and finally irradiated at 5 kGy dosage. All samples was stored at 4 ℃. The pH, TBA, TVB-N and color deviation were evaluated after 0, 3, 7, 10, 14, 18 and 21 d of storage. The results showed that pH value of pre-processed chicken with antioxidants and vacuum packaged increased with the storage time but not significantly among different treatments. The TBA value was also increased but not significantly (P > 0.05), which indicated that vacuum package inhibited the lipid oxidation. TVB-N value increased with storage time, TVB-N value of vacuum package samples reached 14.29 mg/100 g at 21 d storage, which did not exceeded the reference indexes of fresh meat. a * value of the pre-processed chicken of vacuum package and non-oxygen package samples increased significantly during storage (P > 0.05), and chicken color kept bright red after 21 d storage with vacuum package It is concluded that vacuum packaging of irradiated pre-processed chicken is effective on ensuring its physical and chemical properties during storage. (authors)

Examination of Speed Contribution of Parallelization for Several Fingerprint Pre-Processing Algorithms

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GORGUNOGLU, S.

2014-05-01

Full Text Available In analysis of minutiae based fingerprint systems, fingerprints needs to be pre-processed. The pre-processing is carried out to enhance the quality of the fingerprint and to obtain more accurate minutiae points. Reducing the pre-processing time is important for identification and verification in real time systems and especially for databases holding large fingerprints information. Parallel processing and parallel CPU computing can be considered as distribution of processes over multi core processor. This is done by using parallel programming techniques. Reducing the execution time is the main objective in parallel processing. In this study, pre-processing of minutiae based fingerprint system is implemented by parallel processing on multi core computers using OpenMP and on graphics processor using CUDA to improve execution time. The execution times and speedup ratios are compared with the one that of single core processor. The results show that by using parallel processing, execution time is substantially improved. The improvement ratios obtained for different pre-processing algorithms allowed us to make suggestions on the more suitable approaches for parallelization.

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

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Kai Liu

2018-03-01

Full Text Available The separation of coal and gangue is an important process of the coal preparation technology. The conventional way of manual selection and separation of gangue from the raw coal can be replaced by computer vision technology. In the literature, research on image recognition and classification of coal and gangue is mainly based on the grayscale and texture features of the coal and gangue. However, there are few studies on characteristics of coal and gangue from the perspective of their outline differences. Therefore, the multifractal detrended fluctuation analysis (MFDFA method is introduced in this paper to extract the geometric features of coal and gangue. Firstly, the outline curves of coal and gangue in polar coordinates are detected and achieved along the centroid, thereby the multifractal characteristics of the series are analyzed and compared. Subsequently, the modified local singular spectrum widths Δ h of the outline curve series are extracted as the characteristic variables of the coal and gangue for pattern recognition. Finally, the extracted geometric features by MFDFA combined with the grayscale and texture features of the images are compared with other methods, indicating that the recognition rate of coal gangue images can be increased by introducing the geometric features.

PRACTICAL RECOMMENDATIONS OF DATA PREPROCESSING AND GEOSPATIAL MEASURES FOR OPTIMIZING THE NEUROLOGICAL AND OTHER PEDIATRIC EMERGENCIES MANAGEMENT

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Ionela MANIU

2017-08-01

Full Text Available Time management, optimal and timed determination of emergency severity as well as optimizing the use of available human and material resources are crucial areas of emergency services. A starting point for achieving these optimizations can be considered the analysis and preprocess of real data from the emergency services. The benefits of performing this method consist in exposing more useful structures to data modelling algorithms which consequently will reduce overfitting and improves accuracy. This paper aims to offer practical recommendations for data preprocessing measures including feature selection and discretization of numeric attributes regarding age, duration of the case, season, period, week period (workday, weekend and geospatial location of neurological and other pediatric emergencies. An analytical, retrospective study was conducted on a sample consisting of 933 pediatric cases, from UPU-SMURD Sibiu, 01.01.2014 – 27.02.2017 period.

Normalization: A Preprocessing Stage

OpenAIRE

Patro, S. Gopal Krishna; Sahu, Kishore Kumar

2015-01-01

As we know that the normalization is a pre-processing stage of any type problem statement. Especially normalization takes important role in the field of soft computing, cloud computing etc. for manipulation of data like scale down or scale up the range of data before it becomes used for further stage. There are so many normalization techniques are there namely Min-Max normalization, Z-score normalization and Decimal scaling normalization. So by referring these normalization techniques we are ...

A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

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Reinders Marcel JT

2009-11-01

Full Text Available Abstract Background Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for the observed discrepancies are the measurement error associated with each feature and the choice of preprocessing method. Microarray data are known to be subject to technical variation and the confidence intervals around individual point estimates of expression levels can be wide. Furthermore, the estimated expression values also vary depending on the selected preprocessing scheme. In microarray breast cancer classification studies, however, these two forms of feature variability are almost always ignored and hence their exact role is unclear. Results We have performed a comprehensive sensitivity analysis of microarray breast cancer classification under the two types of feature variability mentioned above. We used data from six state of the art preprocessing methods, using a compendium consisting of eight diferent datasets, involving 1131 hybridizations, containing data from both one and two-color array technology. For a wide range of classifiers, we performed a joint study on performance, concordance and stability. In the stability analysis we explicitly tested classifiers for their noise tolerance by using perturbed expression profiles that are based on uncertainty information directly related to the preprocessing methods. Our results indicate that signature composition is strongly influenced by feature variability, even if the array platform and the stratification of patient samples are identical. In addition, we show that there is often a high level of discordance between individual class assignments for signatures constructed on data coming from different preprocessing schemes, even if the actual signature composition is identical

An Automated, Adaptive Framework for Optimizing Preprocessing Pipelines in Task-Based Functional MRI.

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Nathan W Churchill

Full Text Available BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the "pipeline" significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard "fixed" preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each, demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets.

Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces.

Science.gov (United States)

Yang, Banghua; Li, Huarong; Wang, Qian; Zhang, Yunyuan

2016-06-01

Feature extraction of electroencephalogram (EEG) plays a vital role in brain-computer interfaces (BCIs). In recent years, common spatial pattern (CSP) has been proven to be an effective feature extraction method. However, the traditional CSP has disadvantages of requiring a lot of input channels and the lack of frequency information. In order to remedy the defects of CSP, wavelet packet decomposition (WPD) and CSP are combined to extract effective features. But WPD-CSP method considers less about extracting specific features that are fitted for the specific subject. So a subject-based feature extraction method using fisher WPD-CSP is proposed in this paper. The idea of proposed method is to adapt fisher WPD-CSP to each subject separately. It mainly includes the following six steps: (1) original EEG signals from all channels are decomposed into a series of sub-bands using WPD; (2) average power values of obtained sub-bands are computed; (3) the specified sub-bands with larger values of fisher distance according to average power are selected for that particular subject; (4) each selected sub-band is reconstructed to be regarded as a new EEG channel; (5) all new EEG channels are used as input of the CSP and a six-dimensional feature vector is obtained by the CSP. The subject-based feature extraction model is so formed; (6) the probabilistic neural network (PNN) is used as the classifier and the classification accuracy is obtained. Data from six subjects are processed by the subject-based fisher WPD-CSP, the non-subject-based fisher WPD-CSP and WPD-CSP, respectively. Compared with non-subject-based fisher WPD-CSP and WPD-CSP, the results show that the proposed method yields better performance (sensitivity: 88.7±0.9%, and specificity: 91±1%) and the classification accuracy from subject-based fisher WPD-CSP is increased by 6-12% and 14%, respectively. The proposed subject-based fisher WPD-CSP method can not only remedy disadvantages of CSP by WPD but also discriminate

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

International Nuclear Information System (INIS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-01-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification. (paper)

Breast cancer mitosis detection in histopathological images with spatial feature extraction

Science.gov (United States)

Albayrak, Abdülkadir; Bilgin, Gökhan

2013-12-01

In this work, cellular mitosis detection in histopathological images has been investigated. Mitosis detection is very expensive and time consuming process. Development of digital imaging in pathology has enabled reasonable and effective solution to this problem. Segmentation of digital images provides easier analysis of cell structures in histopathological data. To differentiate normal and mitotic cells in histopathological images, feature extraction step is very crucial step for the system accuracy. A mitotic cell has more distinctive textural dissimilarities than the other normal cells. Hence, it is important to incorporate spatial information in feature extraction or in post-processing steps. As a main part of this study, Haralick texture descriptor has been proposed with different spatial window sizes in RGB and La*b* color spaces. So, spatial dependencies of normal and mitotic cellular pixels can be evaluated within different pixel neighborhoods. Extracted features are compared with various sample sizes by Support Vector Machines using k-fold cross validation method. According to the represented results, it has been shown that separation accuracy on mitotic and non-mitotic cellular pixels gets better with the increasing size of spatial window.

Sparse kernel orthonormalized PLS for feature extraction in large datasets

DEFF Research Database (Denmark)

Arenas-García, Jerónimo; Petersen, Kaare Brandt; Hansen, Lars Kai

2006-01-01

In this paper we are presenting a novel multivariate analysis method for large scale problems. Our scheme is based on a novel kernel orthonormalized partial least squares (PLS) variant for feature extraction, imposing sparsity constrains in the solution to improve scalability. The algorithm...... is tested on a benchmark of UCI data sets, and on the analysis of integrated short-time music features for genre prediction. The upshot is that the method has strong expressive power even with rather few features, is clearly outperforming the ordinary kernel PLS, and therefore is an appealing method...

Extraction of multi-scale landslide morphological features based on local Gi* using airborne LiDAR-derived DEM

Science.gov (United States)

Shi, Wenzhong; Deng, Susu; Xu, Wenbing

2018-02-01

For automatic landslide detection, landslide morphological features should be quantitatively expressed and extracted. High-resolution Digital Elevation Models (DEMs) derived from airborne Light Detection and Ranging (LiDAR) data allow fine-scale morphological features to be extracted, but noise in DEMs influences morphological feature extraction, and the multi-scale nature of landslide features should be considered. This paper proposes a method to extract landslide morphological features characterized by homogeneous spatial patterns. Both profile and tangential curvature are utilized to quantify land surface morphology, and a local Gi* statistic is calculated for each cell to identify significant patterns of clustering of similar morphometric values. The method was tested on both synthetic surfaces simulating natural terrain and airborne LiDAR data acquired over an area dominated by shallow debris slides and flows. The test results of the synthetic data indicate that the concave and convex morphologies of the simulated terrain features at different scales and distinctness could be recognized using the proposed method, even when random noise was added to the synthetic data. In the test area, cells with large local Gi* values were extracted at a specified significance level from the profile and the tangential curvature image generated from the LiDAR-derived 1-m DEM. The morphologies of landslide main scarps, source areas and trails were clearly indicated, and the morphological features were represented by clusters of extracted cells. A comparison with the morphological feature extraction method based on curvature thresholds proved the proposed method's robustness to DEM noise. When verified against a landslide inventory, the morphological features of almost all recent (historical (> 10 years) landslides were extracted. This finding indicates that the proposed method can facilitate landslide detection, although the cell clusters extracted from curvature images should

Effect of microaerobic fermentation in preprocessing fibrous lignocellulosic materials.

Science.gov (United States)

Alattar, Manar Arica; Green, Terrence R; Henry, Jordan; Gulca, Vitalie; Tizazu, Mikias; Bergstrom, Robby; Popa, Radu

2012-06-01

Amending soil with organic matter is common in agricultural and logging practices. Such amendments have benefits to soil fertility and crop yields. These benefits may be increased if material is preprocessed before introduction into soil. We analyzed the efficiency of microaerobic fermentation (MF), also referred to as Bokashi, in preprocessing fibrous lignocellulosic (FLC) organic materials using varying produce amendments and leachate treatments. Adding produce amendments increased leachate production and fermentation rates and decreased the biological oxygen demand of the leachate. Continuously draining leachate without returning it to the fermentors led to acidification and decreased concentrations of polysaccharides (PS) in leachates. PS fragmentation and the production of soluble metabolites and gases stabilized in fermentors in about 2-4 weeks. About 2 % of the carbon content was lost as CO(2). PS degradation rates, upon introduction of processed materials into soil, were similar to unfermented FLC. Our results indicate that MF is insufficient for adequate preprocessing of FLC material.

A Novel Technique for Shape Feature Extraction Using Content Based Image Retrieval

Directory of Open Access Journals (Sweden)

Dhanoa Jaspreet Singh

2016-01-01

Full Text Available With the advent of technology and multimedia information, digital images are increasing very quickly. Various techniques are being developed to retrieve/search digital information or data contained in the image. Traditional Text Based Image Retrieval System is not plentiful. Since it is time consuming as it require manual image annotation. Also, the image annotation differs with different peoples. An alternate to this is Content Based Image Retrieval (CBIR system. It retrieves/search for image using its contents rather the text, keywords etc. A lot of exploration has been compassed in the range of Content Based Image Retrieval (CBIR with various feature extraction techniques. Shape is a significant image feature as it reflects the human perception. Moreover, Shape is quite simple to use by the user to define object in an image as compared to other features such as Color, texture etc. Over and above, if applied alone, no descriptor will give fruitful results. Further, by combining it with an improved classifier, one can use the positive features of both the descriptor and classifier. So, a tryout will be made to establish an algorithm for accurate feature (Shape extraction in Content Based Image Retrieval (CBIR. The main objectives of this project are: (a To propose an algorithm for shape feature extraction using CBIR, (b To evaluate the performance of proposed algorithm and (c To compare the proposed algorithm with state of art techniques.

Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

Science.gov (United States)

Shah, Syed Muhammad Saqlain; Batool, Safeera; Khan, Imran; Ashraf, Muhammad Usman; Abbas, Syed Hussnain; Hussain, Syed Adnan

2017-09-01

Automatic diagnosis of human diseases are mostly achieved through decision support systems. The performance of these systems is mainly dependent on the selection of the most relevant features. This becomes harder when the dataset contains missing values for the different features. Probabilistic Principal Component Analysis (PPCA) has reputation to deal with the problem of missing values of attributes. This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease. The proposed methodology extracts high impact features in new projection by using Probabilistic Principal Component Analysis (PPCA). PPCA extracts projection vectors which contribute in highest covariance and these projection vectors are used to reduce feature dimension. The selection of projection vectors is done through Parallel Analysis (PA). The feature subset with the reduced dimension is provided to radial basis function (RBF) kernel based Support Vector Machines (SVM). The RBF based SVM serves the purpose of classification into two categories i.e., Heart Patient (HP) and Normal Subject (NS). The proposed methodology is evaluated through accuracy, specificity and sensitivity over the three datasets of UCI i.e., Cleveland, Switzerland and Hungarian. The statistical results achieved through the proposed technique are presented in comparison to the existing research showing its impact. The proposed technique achieved an accuracy of 82.18%, 85.82% and 91.30% for Cleveland, Hungarian and Switzerland dataset respectively.

The optimal extraction of feature algorithm based on KAZE

Science.gov (United States)

Yao, Zheyi; Gu, Guohua; Qian, Weixian; Wang, Pengcheng

2015-10-01

As a novel method of 2D features extraction algorithm over the nonlinear scale space, KAZE provide a special method. However, the computation of nonlinear scale space and the construction of KAZE feature vectors are more expensive than the SIFT and SURF significantly. In this paper, the given image is used to build the nonlinear space up to a maximum evolution time through the efficient Additive Operator Splitting (AOS) techniques and the variable conductance diffusion. Changing the parameter can improve the construction of nonlinear scale space and simplify the image conductivities for each dimension space, with the predigest computation. Then, the detection for points of interest can exhibit a maxima of the scale-normalized determinant with the Hessian response in the nonlinear scale space. At the same time, the detection of feature vectors is optimized by the Wavelet Transform method, which can avoid the second Gaussian smoothing in the KAZE Features and cut down the complexity of the algorithm distinctly in the building and describing vectors steps. In this way, the dominant orientation is obtained, similar to SURF, by summing the responses within a sliding circle segment covering an angle of π/3 in the circular area of radius 6σ with a sampling step of size σ one by one. Finally, the extraction in the multidimensional patch at the given scale, centered over the points of interest and rotated to align its dominant orientation to a canonical direction, is able to simplify the description of feature by reducing the description dimensions, just as the PCA-SIFT method. Even though the features are somewhat more expensive to compute than SIFT due to the construction of nonlinear scale space, but compared to SURF, the result revels a step forward in performance in detection, description and application against the previous ways by the following contrast experiments.

Automated Feature Extraction of Foredune Morphology from Terrestrial Lidar Data

Science.gov (United States)

Spore, N.; Brodie, K. L.; Swann, C.

2014-12-01

Foredune morphology is often described in storm impact prediction models using the elevation of the dune crest and dune toe and compared with maximum runup elevations to categorize the storm impact and predicted responses. However, these parameters do not account for other foredune features that may make them more or less erodible, such as alongshore variations in morphology, vegetation coverage, or compaction. The goal of this work is to identify other descriptive features that can be extracted from terrestrial lidar data that may affect the rate of dune erosion under wave attack. Daily, mobile-terrestrial lidar surveys were conducted during a 6-day nor'easter (Hs = 4 m in 6 m water depth) along 20km of coastline near Duck, North Carolina which encompassed a variety of foredune forms in close proximity to each other. This abstract will focus on the tools developed for the automated extraction of the morphological features from terrestrial lidar data, while the response of the dune will be presented by Brodie and Spore as an accompanying abstract. Raw point cloud data can be dense and is often under-utilized due to time and personnel constraints required for analysis, since many algorithms are not fully automated. In our approach, the point cloud is first projected into a local coordinate system aligned with the coastline, and then bare earth points are interpolated onto a rectilinear 0.5 m grid creating a high resolution digital elevation model. The surface is analyzed by identifying features along each cross-shore transect. Surface curvature is used to identify the position of the dune toe, and then beach and berm morphology is extracted shoreward of the dune toe, and foredune morphology is extracted landward of the dune toe. Changes in, and magnitudes of, cross-shore slope, curvature, and surface roughness are used to describe the foredune face and each cross-shore transect is then classified using its pre-storm morphology for storm-response analysis.

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

Directory of Open Access Journals (Sweden)

Chen Liu

2018-05-01

Full Text Available How to acquire useful information intelligently in the age of information explosion has become an important issue. In this context, sentiment analysis emerges with the growth of the need of information extraction. One of the most important tasks of sentiment analysis is feature extraction of entities in consumer reviews. This paper first constitutes a directed bipartite feature-sentiment relation network with a set of candidate features-sentiment pairs that is extracted by dependency syntax analysis from consumer reviews. Then, a novel method called MHITS which combines PMI with weighted HITS algorithm is proposed to rank these candidate product features to find out real product features. Empirical experiments indicate the effectiveness of our approach across different kinds and various data sizes of product. In addition, the effect of the proposed algorithm is not the same for the corpus with different proportions of the word pair that includes the “bad”, “good”, “poor”, “pretty good”, “not bad” these general collocation words.

Performance of Pre-processing Schemes with Imperfect Channel State Information

DEFF Research Database (Denmark)

Christensen, Søren Skovgaard; Kyritsi, Persa; De Carvalho, Elisabeth

2006-01-01

Pre-processing techniques have several benefits when the CSI is perfect. In this work we investigate three linear pre-processing filters, assuming imperfect CSI caused by noise degradation and channel temporal variation. Results indicate, that the LMMSE filter achieves the lowest BER and the high......Pre-processing techniques have several benefits when the CSI is perfect. In this work we investigate three linear pre-processing filters, assuming imperfect CSI caused by noise degradation and channel temporal variation. Results indicate, that the LMMSE filter achieves the lowest BER...... and the highest SINR when the CSI is perfect, whereas the simple matched filter may be a good choice when the CSI is imperfect. Additionally the results give insight into the inherent trade-off between robustness against CSI imperfections and spatial focusing ability....

The 1989 ENDF pre-processing codes

International Nuclear Information System (INIS)

Cullen, D.E.; McLaughlin, P.K.

1989-12-01

This document summarizes the 1989 version of the ENDF pre-processing codes which are required for processing evaluated nuclear data coded in the format ENDF-4, ENDF-5, or ENDF-6. The codes are available from the IAEA Nuclear Data Section, free of charge upon request. (author)

Semantic feature extraction for interior environment understanding and retrieval

Science.gov (United States)

Lei, Zhibin; Liang, Yufeng

1998-12-01

In this paper, we propose a novel system of semantic feature extraction and retrieval for interior design and decoration application. The system, V2ID(Virtual Visual Interior Design), uses colored texture and spatial edge layout to obtain simple information about global room environment. We address the domain-specific segmentation problem in our application and present techniques for obtaining semantic features from a room environment. We also discuss heuristics for making use of these features (color, texture, edge layout, and shape), to retrieve objects from an existing database. The final resynthesized room environment, with the original scene and objects from the database, is created for the purpose of animation and virtual walk-through.

WATERSHED ALGORITHM BASED SEGMENTATION FOR HANDWRITTEN TEXT IDENTIFICATION

Directory of Open Access Journals (Sweden)

P. Mathivanan

2014-02-01

Full Text Available In this paper we develop a system for writer identification which involves four processing steps like preprocessing, segmentation, feature extraction and writer identification using neural network. In the preprocessing phase the handwritten text is subjected to slant removal process for segmentation and feature extraction. After this step the text image enters into the process of noise removal and gray level conversion. The preprocessed image is further segmented by using morphological watershed algorithm, where the text lines are segmented into single words and then into single letters. The segmented image is feature extracted by Daubechies’5/3 integer wavelet transform to reduce training complexity [1, 6]. This process is lossless and reversible [10], [14]. These extracted features are given as input to our neural network for writer identification process and a target image is selected for each training process in the 2-layer neural network. With the several trained output data obtained from different target help in text identification. It is a multilingual text analysis which provides simple and efficient text segmentation.

Cloudy Solar Software - Enhanced Capabilities for Finding, Pre-processing, and Visualizing Solar Data

Science.gov (United States)

Istvan Etesi, Laszlo; Tolbert, K.; Schwartz, R.; Zarro, D.; Dennis, B.; Csillaghy, A.

2010-05-01

In our project "Extending the Virtual Solar Observatory (VSO)” we have combined some of the features available in Solar Software (SSW) to produce an integrated environment for data analysis, supporting the complete workflow from data location, retrieval, preparation, and analysis to creating publication-quality figures. Our goal is an integrated analysis experience in IDL, easy-to-use but flexible enough to allow more sophisticated procedures such as multi-instrument analysis. To that end, we have made the transition from a locally oriented setting where all the analysis is done on the user's computer, to an extended analysis environment where IDL has access to services available on the Internet. We have implemented a form of Cloud Computing that uses the VSO search and a new data retrieval and pre-processing server (PrepServer) that provides remote execution of instrument-specific data preparation. We have incorporated the interfaces to the VSO search and the PrepServer into an IDL widget (SHOW_SYNOP) that provides user-friendly searching and downloading of raw solar data and optionally sends search results for pre-processing to the PrepServer prior to downloading the data. The raw and pre-processed data can be displayed with our plotting suite, PLOTMAN, which can handle different data types (light curves, images, and spectra) and perform basic data operations such as zooming, image overlays, solar rotation, etc. PLOTMAN is highly configurable and suited for visual data analysis and for creating publishable figures. PLOTMAN and SHOW_SYNOP work hand-in-hand for a convenient working environment. Our environment supports a growing number of solar instruments that currently includes RHESSI, SOHO/EIT, TRACE, SECCHI/EUVI, HINODE/XRT, and HINODE/EIS.

Homomorphic encryption-based secure SIFT for privacy-preserving feature extraction

Science.gov (United States)

Hsu, Chao-Yung; Lu, Chun-Shien; Pei, Soo-Chang

2011-02-01

Privacy has received much attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario, where the server is resource-abundant and is capable of finishing the designated tasks, it is envisioned that secure media retrieval and search with privacy-preserving will be seriously treated. In view of the fact that scale-invariant feature transform (SIFT) has been widely adopted in various fields, this paper is the first to address the problem of secure SIFT feature extraction and representation in the encrypted domain. Since all the operations in SIFT must be moved to the encrypted domain, we propose a homomorphic encryption-based secure SIFT method for privacy-preserving feature extraction and representation based on Paillier cryptosystem. In particular, homomorphic comparison is a must for SIFT feature detection but is still a challenging issue for homomorphic encryption methods. To conquer this problem, we investigate a quantization-like secure comparison strategy in this paper. Experimental results demonstrate that the proposed homomorphic encryption-based SIFT performs comparably to original SIFT on image benchmarks, while preserving privacy additionally. We believe that this work is an important step toward privacy-preserving multimedia retrieval in an environment, where privacy is a major concern.

Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit

OpenAIRE

Yuehai Wang; Yongzheng Yan; Qinyong Wang

2016-01-01

Fault diagnosis for analog circuit has become a prominent factor in improving the reliability of integrated circuit due to its irreplaceability in modern integrated circuits. In fact fault diagnosis based on intelligent algorithms has become a popular research topic as efficient feature extraction and selection are a critical and intricate task in analog fault diagnosis. Further, it is extremely important to propose some general guidelines for the optimal feature extraction and selection. In ...

A novel automated spike sorting algorithm with adaptable feature extraction.

Science.gov (United States)

Bestel, Robert; Daus, Andreas W; Thielemann, Christiane

2012-10-15

To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach. Copyright © 2012 Elsevier B.V. All rights reserved.

Effects of Feature Extraction and Classification Methods on Cyberbully Detection

OpenAIRE

ÖZEL, Selma Ayşe; SARAÇ, Esra

2016-01-01

Cyberbullying is defined as an aggressive, intentional action against a defenseless person by using the Internet, or other electronic contents. Researchers have found that many of the bullying cases have tragically ended in suicides; hence automatic detection of cyberbullying has become important. In this study we show the effects of feature extraction, feature selection, and classification methods that are used, on the performance of automatic detection of cyberbullying. To perform the exper...

New indicator for optimal preprocessing and wavelength selection of near-infrared spectra

NARCIS (Netherlands)

Skibsted, E. T. S.; Boelens, H. F. M.; Westerhuis, J. A.; Witte, D. T.; Smilde, A. K.

2004-01-01

Preprocessing of near-infrared spectra to remove unwanted, i.e., non-related spectral variation and selection of informative wavelengths is considered to be a crucial step prior to the construction of a quantitative calibration model. The standard methodology when comparing various preprocessing

Feature extraction & image processing for computer vision

CERN Document Server

Nixon, Mark

2012-01-01

This book is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in Matlab. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, ""The main strength of the proposed book is the exemplar code of the algorithms."" Fully updated with the latest developments in feature extraction, including expanded tutorials and new techniques, this new edition contains extensive new material on Haar wavelets, Viola-Jones, bilateral filt

A Method of Road Extraction from High-resolution Remote Sensing Images Based on Shape Features

Directory of Open Access Journals (Sweden)

LEI Xiaoqi

2016-02-01

Full Text Available Road extraction from high-resolution remote sensing image is an important and difficult task.Since remote sensing images include complicated information,the methods that extract roads by spectral,texture and linear features have certain limitations.Also,many methods need human-intervention to get the road seeds(semi-automatic extraction,which have the great human-dependence and low efficiency.The road-extraction method,which uses the image segmentation based on principle of local gray consistency and integration shape features,is proposed in this paper.Firstly,the image is segmented,and then the linear and curve roads are obtained by using several object shape features,so the method that just only extract linear roads are rectified.Secondly,the step of road extraction is carried out based on the region growth,the road seeds are automatic selected and the road network is extracted.Finally,the extracted roads are regulated by combining the edge information.In experiments,the images that including the better gray uniform of road and the worse illuminated of road surface were chosen,and the results prove that the method of this study is promising.

Extracting product features and opinion words using pattern knowledge in customer reviews.

Science.gov (United States)

Htay, Su Su; Lynn, Khin Thidar

2013-01-01

Due to the development of e-commerce and web technology, most of online Merchant sites are able to write comments about purchasing products for customer. Customer reviews expressed opinion about products or services which are collectively referred to as customer feedback data. Opinion extraction about products from customer reviews is becoming an interesting area of research and it is motivated to develop an automatic opinion mining application for users. Therefore, efficient method and techniques are needed to extract opinions from reviews. In this paper, we proposed a novel idea to find opinion words or phrases for each feature from customer reviews in an efficient way. Our focus in this paper is to get the patterns of opinion words/phrases about the feature of product from the review text through adjective, adverb, verb, and noun. The extracted features and opinions are useful for generating a meaningful summary that can provide significant informative resource to help the user as well as merchants to track the most suitable choice of product.

Extracting Product Features and Opinion Words Using Pattern Knowledge in Customer Reviews

Directory of Open Access Journals (Sweden)

Su Su Htay

2013-01-01

Full Text Available Due to the development of e-commerce and web technology, most of online Merchant sites are able to write comments about purchasing products for customer. Customer reviews expressed opinion about products or services which are collectively referred to as customer feedback data. Opinion extraction about products from customer reviews is becoming an interesting area of research and it is motivated to develop an automatic opinion mining application for users. Therefore, efficient method and techniques are needed to extract opinions from reviews. In this paper, we proposed a novel idea to find opinion words or phrases for each feature from customer reviews in an efficient way. Our focus in this paper is to get the patterns of opinion words/phrases about the feature of product from the review text through adjective, adverb, verb, and noun. The extracted features and opinions are useful for generating a meaningful summary that can provide significant informative resource to help the user as well as merchants to track the most suitable choice of product.

Extracting Product Features and Opinion Words Using Pattern Knowledge in Customer Reviews

Science.gov (United States)

Lynn, Khin Thidar

2013-01-01

Due to the development of e-commerce and web technology, most of online Merchant sites are able to write comments about purchasing products for customer. Customer reviews expressed opinion about products or services which are collectively referred to as customer feedback data. Opinion extraction about products from customer reviews is becoming an interesting area of research and it is motivated to develop an automatic opinion mining application for users. Therefore, efficient method and techniques are needed to extract opinions from reviews. In this paper, we proposed a novel idea to find opinion words or phrases for each feature from customer reviews in an efficient way. Our focus in this paper is to get the patterns of opinion words/phrases about the feature of product from the review text through adjective, adverb, verb, and noun. The extracted features and opinions are useful for generating a meaningful summary that can provide significant informative resource to help the user as well as merchants to track the most suitable choice of product. PMID:24459430

Scale-invariant feature extraction of neural network and renormalization group flow

Science.gov (United States)

Iso, Satoshi; Shiba, Shotaro; Yokoo, Sumito

2018-05-01

Theoretical understanding of how a deep neural network (DNN) extracts features from input images is still unclear, but it is widely believed that the extraction is performed hierarchically through a process of coarse graining. It reminds us of the basic renormalization group (RG) concept in statistical physics. In order to explore possible relations between DNN and RG, we use the restricted Boltzmann machine (RBM) applied to an Ising model and construct a flow of model parameters (in particular, temperature) generated by the RBM. We show that the unsupervised RBM trained by spin configurations at various temperatures from T =0 to T =6 generates a flow along which the temperature approaches the critical value Tc=2.2 7 . This behavior is the opposite of the typical RG flow of the Ising model. By analyzing various properties of the weight matrices of the trained RBM, we discuss why it flows towards Tc and how the RBM learns to extract features of spin configurations.

Bubble feature extracting based on image processing of coal flotation froth

Energy Technology Data Exchange (ETDEWEB)

Wang, F.; Wang, Y.; Lu, M.; Liu, W. [China University of Mining and Technology, Beijing (China). Dept of Chemical Engineering and Environment

2001-11-01

Using image processing the contrast ratio between the bubble on the surface of flotation froth and the image background was enhanced, and the edges of bubble were extracted. Thus a model about the relation between the statistic feature of the bubbles in the image and the cleaned coal can be established. It is feasible to extract the bubble by processing the froth image of coal flotation on the basis of analysing the shape of the bubble. By means of processing the 51 group images sampled from laboratory column, it is thought that the use of the histogram equalization of image gradation and the medium filtering can obviously improve the dynamic contrast range and the brightness of bubbles. Finally, the method of threshold value cut and the bubble edge detecting for extracting the bubble were also discussed to describe the bubble feature, such as size and shape, in the froth image and to distinguish the froth image of coal flotation. 6 refs., 3 figs.

Engagement Assessment Using EEG Signals

Science.gov (United States)

Li, Feng; Li, Jiang; McKenzie, Frederic; Zhang, Guangfan; Wang, Wei; Pepe, Aaron; Xu, Roger; Schnell, Thomas; Anderson, Nick; Heitkamp, Dean

2012-01-01

In this paper, we present methods to analyze and improve an EEG-based engagement assessment approach, consisting of data preprocessing, feature extraction and engagement state classification. During data preprocessing, spikes, baseline drift and saturation caused by recording devices in EEG signals are identified and eliminated, and a wavelet based method is utilized to remove ocular and muscular artifacts in the EEG recordings. In feature extraction, power spectrum densities with 1 Hz bin are calculated as features, and these features are analyzed using the Fisher score and the one way ANOVA method. In the classification step, a committee classifier is trained based on the extracted features to assess engagement status. Finally, experiment results showed that there exist significant differences in the extracted features among different subjects, and we have implemented a feature normalization procedure to mitigate the differences and significantly improved the engagement assessment performance.

Feature Extraction on Brain Computer Interfaces using Discrete Dyadic Wavelet Transform: Preliminary Results

International Nuclear Information System (INIS)

Gareis, I; Gentiletti, G; Acevedo, R; Rufiner, L

2011-01-01

The purpose of this work is to evaluate different feature extraction alternatives to detect the event related evoked potential signal on brain computer interfaces, trying to minimize the time employed and the classification error, in terms of sensibility and specificity of the method, looking for alternatives to coherent averaging. In this context the results obtained performing the feature extraction using discrete dyadic wavelet transform using different mother wavelets are presented. For the classification a single layer perceptron was used. The results obtained with and without the wavelet decomposition were compared; showing an improvement on the classification rate, the specificity and the sensibility for the feature vectors obtained using some mother wavelets.

Comparison of multivariate preprocessing techniques as applied to electronic tongue based pattern classification for black tea

International Nuclear Information System (INIS)

Palit, Mousumi; Tudu, Bipan; Bhattacharyya, Nabarun; Dutta, Ankur; Dutta, Pallab Kumar; Jana, Arun; Bandyopadhyay, Rajib; Chatterjee, Anutosh

2010-01-01

In an electronic tongue, preprocessing on raw data precedes pattern analysis and choice of the appropriate preprocessing technique is crucial for the performance of the pattern classifier. While attempting to classify different grades of black tea using a voltammetric electronic tongue, different preprocessing techniques have been explored and a comparison of their performances is presented in this paper. The preprocessing techniques are compared first by a quantitative measurement of separability followed by principle component analysis; and then two different supervised pattern recognition models based on neural networks are used to evaluate the performance of the preprocessing techniques.

Fusion of Pixel-based and Object-based Features for Road Centerline Extraction from High-resolution Satellite Imagery

Directory of Open Access Journals (Sweden)

CAO Yungang

2016-10-01

Full Text Available A novel approach for road centerline extraction from high spatial resolution satellite imagery is proposed by fusing both pixel-based and object-based features. Firstly, texture and shape features are extracted at the pixel level, and spectral features are extracted at the object level based on multi-scale image segmentation maps. Then, extracted multiple features are utilized in the fusion framework of Dempster-Shafer evidence theory to roughly identify the road network regions. Finally, an automatic noise removing algorithm combined with the tensor voting strategy is presented to accurately extract the road centerline. Experimental results using high-resolution satellite imageries with different scenes and spatial resolutions showed that the proposed approach compared favorably with the traditional methods, particularly in the aspect of eliminating the salt noise and conglutination phenomenon.

Aircraft micro-doppler feature extraction from high range resolution profiles

CSIR Research Space (South Africa)

Berndt, RJ

2015-10-01

Full Text Available The use of high range resolution measurements and the micro-Doppler effect produced by rotating or vibrating parts of a target has been well documented. This paper presents a technique for extracting features related to helicopter rotors...

Low-Level Color and Texture Feature Extraction of Coral Reef Components

Directory of Open Access Journals (Sweden)

Ma. Sheila Angeli Marcos

2003-06-01

Full Text Available The purpose of this study is to develop a computer-based classifier that automates coral reef assessmentfrom digitized underwater video. We extract low-level color and texture features from coral images toserve as input to a high-level classifier. Low-level features for color were labeled blue, green, yellow/brown/orange, and gray/white, which are described by the normalized chromaticity histograms of thesemajor colors. The color matching capability of these features was determined through a technique called“Histogram Backprojection”. The low-level texture feature marks a region as coarse or fine dependingon the gray-level variance of the region.

A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification

Directory of Open Access Journals (Sweden)

Sudeep Thepade

2014-01-01

Full Text Available A number of techniques have been proposed earlier for feature extraction using image binarization. Efficiency of the techniques was dependent on proper threshold selection for the binarization method. In this paper, a new feature extraction technique using image binarization has been proposed. The technique has binarized the significant bit planes of an image by selecting local thresholds. The proposed algorithm has been tested on a public dataset and has been compared with existing widely used techniques using binarization for extraction of features. It has been inferred that the proposed method has outclassed all the existing techniques and has shown consistent classification performance.

Image preprocessing for improving computational efficiency in implementation of restoration and superresolution algorithms.

Science.gov (United States)

Sundareshan, Malur K; Bhattacharjee, Supratik; Inampudi, Radhika; Pang, Ho-Yuen

2002-12-10

Computational complexity is a major impediment to the real-time implementation of image restoration and superresolution algorithms in many applications. Although powerful restoration algorithms have been developed within the past few years utilizing sophisticated mathematical machinery (based on statistical optimization and convex set theory), these algorithms are typically iterative in nature and require a sufficient number of iterations to be executed to achieve the desired resolution improvement that may be needed to meaningfully perform postprocessing image exploitation tasks in practice. Additionally, recent technological breakthroughs have facilitated novel sensor designs (focal plane arrays, for instance) that make it possible to capture megapixel imagery data at video frame rates. A major challenge in the processing of these large-format images is to complete the execution of the image processing steps within the frame capture times and to keep up with the output rate of the sensor so that all data captured by the sensor can be efficiently utilized. Consequently, development of novel methods that facilitate real-time implementation of image restoration and superresolution algorithms is of significant practical interest and is the primary focus of this study. The key to designing computationally efficient processing schemes lies in strategically introducing appropriate preprocessing steps together with the superresolution iterations to tailor optimized overall processing sequences for imagery data of specific formats. For substantiating this assertion, three distinct methods for tailoring a preprocessing filter and integrating it with the superresolution processing steps are outlined. These methods consist of a region-of-interest extraction scheme, a background-detail separation procedure, and a scene-derived information extraction step for implementing a set-theoretic restoration of the image that is less demanding in computation compared with the

Vibration Feature Extraction and Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey

OpenAIRE

Saleem Riaz; Hassan Elahi; Kashif Javaid; Tufail Shahzad

2017-01-01

Safety, reliability, efficiency and performance of rotating machinery in all industrial applications are the main concerns. Rotating machines are widely used in various industrial applications. Condition monitoring and fault diagnosis of rotating machinery faults are very important and often complex and labor-intensive. Feature extraction techniques play a vital role for a reliable, effective and efficient feature extraction for the diagnosis of rotating machinery. Therefore, deve...

Improving ELM-Based Service Quality Prediction by Concise Feature Extraction

Directory of Open Access Journals (Sweden)

Yuhai Zhao

2015-01-01

Full Text Available Web services often run on highly dynamic and changing environments, which generate huge volumes of data. Thus, it is impractical to monitor the change of every QoS parameter for the timely trigger precaution due to high computational costs associated with the process. To address the problem, this paper proposes an active service quality prediction method based on extreme learning machine. First, we extract web service trace logs and QoS information from the service log and convert them into feature vectors. Second, by the proposed EC rules, we are enabled to trigger the precaution of QoS as soon as possible with high confidence. An efficient prefix tree based mining algorithm together with some effective pruning rules is developed to mine such rules. Finally, we study how to extract a set of diversified features as the representative of all mined results. The problem is proved to be NP-hard. A greedy algorithm is presented to approximate the optimal solution. Experimental results show that ELM trained by the selected feature subsets can efficiently improve the reliability and the earliness of service quality prediction.

Hidden discriminative features extraction for supervised high-order time series modeling.

Science.gov (United States)

Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee

2016-11-01

In this paper, an orthogonal Tucker-decomposition-based extraction of high-order discriminative subspaces from a tensor-based time series data structure is presented, named as Tensor Discriminative Feature Extraction (TDFE). TDFE relies on the employment of category information for the maximization of the between-class scatter and the minimization of the within-class scatter to extract optimal hidden discriminative feature subspaces that are simultaneously spanned by every modality for supervised tensor modeling. In this context, the proposed tensor-decomposition method provides the following benefits: i) reduces dimensionality while robustly mining the underlying discriminative features, ii) results in effective interpretable features that lead to an improved classification and visualization, and iii) reduces the processing time during the training stage and the filtering of the projection by solving the generalized eigenvalue issue at each alternation step. Two real third-order tensor-structures of time series datasets (an epilepsy electroencephalogram (EEG) that is modeled as channel×frequency bin×time frame and a microarray data that is modeled as gene×sample×time) were used for the evaluation of the TDFE. The experiment results corroborate the advantages of the proposed method with averages of 98.26% and 89.63% for the classification accuracies of the epilepsy dataset and the microarray dataset, respectively. These performance averages represent an improvement on those of the matrix-based algorithms and recent tensor-based, discriminant-decomposition approaches; this is especially the case considering the small number of samples that are used in practice. Copyright © 2016 Elsevier Ltd. All rights reserved.

Extracting BI-RADS Features from Portuguese Clinical Texts.

Science.gov (United States)

Nassif, Houssam; Cunha, Filipe; Moreira, Inês C; Cruz-Correia, Ricardo; Sousa, Eliana; Page, David; Burnside, Elizabeth; Dutra, Inês

2012-01-01

In this work we build the first BI-RADS parser for Portuguese free texts, modeled after existing approaches to extract BI-RADS features from English medical records. Our concept finder uses a semantic grammar based on the BIRADS lexicon and on iterative transferred expert knowledge. We compare the performance of our algorithm to manual annotation by a specialist in mammography. Our results show that our parser's performance is comparable to the manual method.

Value of Distributed Preprocessing of Biomass Feedstocks to a Bioenergy Industry

Energy Technology Data Exchange (ETDEWEB)

Christopher T Wright

2006-07-01

Biomass preprocessing is one of the primary operations in the feedstock assembly system and the front-end of a biorefinery. Its purpose is to chop, grind, or otherwise format the biomass into a suitable feedstock for conversion to ethanol and other bioproducts. Many variables such as equipment cost and efficiency, and feedstock moisture content, particle size, bulk density, compressibility, and flowability affect the location and implementation of this unit operation. Previous conceptual designs show this operation to be located at the front-end of the biorefinery. However, data are presented that show distributed preprocessing at the field-side or in a fixed preprocessing facility can provide significant cost benefits by producing a higher value feedstock with improved handling, transporting, and merchandising potential. In addition, data supporting the preferential deconstruction of feedstock materials due to their bio-composite structure identifies the potential for significant improvements in equipment efficiencies and compositional quality upgrades. Theses data are collected from full-scale low and high capacity hammermill grinders with various screen sizes. Multiple feedstock varieties with a range of moisture values were used in the preprocessing tests. The comparative values of the different grinding configurations, feedstock varieties, and moisture levels are assessed through post-grinding analysis of the different particle fractions separated with a medium-scale forage particle separator and a Rototap separator. The results show that distributed preprocessing produces a material that has bulk flowable properties and fractionation benefits that can improve the ease of transporting, handling and conveying the material to the biorefinery and improve the biochemical and thermochemical conversion processes.

Micro-Doppler Feature Extraction and Recognition Based on Netted Radar for Ballistic Targets

Directory of Open Access Journals (Sweden)

Feng Cun-qian

2015-12-01

Full Text Available This study examines the complexities of using netted radar to recognize and resolve ballistic midcourse targets. The application of micro-motion feature extraction to ballistic mid-course targets is analyzed, and the current status of application and research on micro-motion feature recognition is concluded for singlefunction radar networks such as low- and high-resolution imaging radar networks. Advantages and disadvantages of these networks are discussed with respect to target recognition. Hybrid-mode radar networks combine low- and high-resolution imaging radar and provide a specific reference frequency that is the basis for ballistic target recognition. Main research trends are discussed for hybrid-mode networks that apply micromotion feature extraction to ballistic mid-course targets.

Fault feature extraction method based on local mean decomposition Shannon entropy and improved kernel principal component analysis model

Directory of Open Access Journals (Sweden)

Jinlu Sheng

2016-07-01

Full Text Available To effectively extract the typical features of the bearing, a new method that related the local mean decomposition Shannon entropy and improved kernel principal component analysis model was proposed. First, the features are extracted by time–frequency domain method, local mean decomposition, and using the Shannon entropy to process the original separated product functions, so as to get the original features. However, the features been extracted still contain superfluous information; the nonlinear multi-features process technique, kernel principal component analysis, is introduced to fuse the characters. The kernel principal component analysis is improved by the weight factor. The extracted characteristic features were inputted in the Morlet wavelet kernel support vector machine to get the bearing running state classification model, bearing running state was thereby identified. Cases of test and actual were analyzed.

Pre-processing for Triangulation of Probabilistic Networks

NARCIS (Netherlands)

Bodlaender, H.L.; Koster, A.M.C.A.; Eijkhof, F. van den; Gaag, L.C. van der

2001-01-01

The currently most efficient algorithm for inference with a probabilistic network builds upon a triangulation of a networks graph. In this paper, we show that pre-processing can help in finding good triangulations for probabilistic networks, that is, triangulations with a minimal maximum

Applying Improved Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

Directory of Open Access Journals (Sweden)

Ming-ai Li

2017-01-01

Full Text Available Electroencephalography (EEG is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG not only has a close correlation with the human imagination and movement intention but also contains a large amount of physiological or disease information. As a result, it has been fully studied in the field of rehabilitation. To correctly interpret and accurately extract the features of MI-EEG signals, many nonlinear dynamic methods based on entropy, such as Approximate Entropy (ApEn, Sample Entropy (SampEn, Fuzzy Entropy (FE, and Permutation Entropy (PE, have been proposed and exploited continuously in recent years. However, these entropy-based methods can only measure the complexity of MI-EEG based on a single scale and therefore fail to account for the multiscale property inherent in MI-EEG. To solve this problem, Multiscale Sample Entropy (MSE, Multiscale Permutation Entropy (MPE, and Multiscale Fuzzy Entropy (MFE are developed by introducing scale factor. However, MFE has not been widely used in analysis of MI-EEG, and the same parameter values are employed when the MFE method is used to calculate the fuzzy entropy values on multiple scales. Actually, each coarse-grained MI-EEG carries the characteristic information of the original signal on different scale factors. It is necessary to optimize MFE parameters to discover more feature information. In this paper, the parameters of MFE are optimized independently for each scale factor, and the improved MFE (IMFE is applied to the feature extraction of MI-EEG. Based on the event-related desynchronization (ERD/event-related synchronization (ERS phenomenon, IMFE features from multi channels are fused organically to construct the feature vector. Experiments are conducted on a public dataset by using Support Vector Machine (SVM as a classifier. The experiment results of 10-fold cross-validation show that the proposed method yields

Comparative performance evaluation of transform coding in image pre-processing

Science.gov (United States)

Menon, Vignesh V.; NB, Harikrishnan; Narayanan, Gayathri; CK, Niveditha

2017-07-01

We are in the midst of a communication transmute which drives the development as largely as dissemination of pioneering communication systems with ever-increasing fidelity and resolution. Distinguishable researches have been appreciative in image processing techniques crazed by a growing thirst for faster and easier encoding, storage and transmission of visual information. In this paper, the researchers intend to throw light on many techniques which could be worn at the transmitter-end in order to ease the transmission and reconstruction of the images. The researchers investigate the performance of different image transform coding schemes used in pre-processing, their comparison, and effectiveness, the necessary and sufficient conditions, properties and complexity in implementation. Whimsical by prior advancements in image processing techniques, the researchers compare various contemporary image pre-processing frameworks- Compressed Sensing, Singular Value Decomposition, Integer Wavelet Transform on performance. The paper exposes the potential of Integer Wavelet transform to be an efficient pre-processing scheme.

The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing.

Science.gov (United States)

Ma, Teng; Li, Hui; Yang, Hao; Lv, Xulin; Li, Peiyang; Liu, Tiejun; Yao, Dezhong; Xu, Peng

2017-01-01

Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong background EEG activities, and an effective approach is needed to extract the corresponding mVEP features to perform task recognition for BCI control. In the current study, we combine deep learning with compressed sensing to mine discriminative mVEP information to improve the mVEP BCI performance. The deep learning and compressed sensing approach can generate the multi-modality features which can effectively improve the BCI performance with approximately 3.5% accuracy incensement over all 11 subjects and is more effective for those subjects with relatively poor performance when using the conventional features. Compared with the conventional amplitude-based mVEP feature extraction approach, the deep learning and compressed sensing approach has a higher classification accuracy and is more effective for subjects with relatively poor performance. According to the results, the deep learning and compressed sensing approach is more effective for extracting the mVEP feature to construct the corresponding BCI system, and the proposed feature extraction framework is easy to extend to other types of BCIs, such as motor imagery (MI), steady-state visual evoked potential (SSVEP)and P300. Copyright © 2016 Elsevier B.V. All rights reserved.

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

2017-12-01

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 consistent

A novel framework for feature extraction in multi-sensor action potential sorting.

Science.gov (United States)

Wu, Shun-Chi; Swindlehurst, A Lee; Nenadic, Zoran

2015-09-30

Extracellular recordings of multi-unit neural activity have become indispensable in neuroscience research. The analysis of the recordings begins with the detection of the action potentials (APs), followed by a classification step where each AP is associated with a given neural source. A feature extraction step is required prior to classification in order to reduce the dimensionality of the data and the impact of noise, allowing source clustering algorithms to work more efficiently. In this paper, we propose a novel framework for multi-sensor AP feature extraction based on the so-called Matched Subspace Detector (MSD), which is shown to be a natural generalization of standard single-sensor algorithms. Clustering using both simulated data and real AP recordings taken in the locust antennal lobe demonstrates that the proposed approach yields features that are discriminatory and lead to promising results. Unlike existing methods, the proposed algorithm finds joint spatio-temporal feature vectors that match the dominant subspace observed in the two-dimensional data without needs for a forward propagation model and AP templates. The proposed MSD approach provides more discriminatory features for unsupervised AP sorting applications. Copyright © 2015 Elsevier B.V. All rights reserved.

a Landmark Extraction Method Associated with Geometric Features and Location Distribution

Science.gov (United States)

Zhang, W.; Li, J.; Wang, Y.; Xiao, Y.; Liu, P.; Zhang, S.

2018-04-01

Landmark plays an important role in spatial cognition and spatial knowledge organization. Significance measuring model is the main method of landmark extraction. It is difficult to take account of the spatial distribution pattern of landmarks because that the significance of landmark is built in one-dimensional space. In this paper, we start with the geometric features of the ground object, an extraction method based on the target height, target gap and field of view is proposed. According to the influence region of Voronoi Diagram, the description of target gap is established to the geometric representation of the distribution of adjacent targets. Then, segmentation process of the visual domain of Voronoi K order adjacent is given to set up target view under the multi view; finally, through three kinds of weighted geometric features, the landmarks are identified. Comparative experiments show that this method has a certain coincidence degree with the results of traditional significance measuring model, which verifies the effectiveness and reliability of the method and reduces the complexity of landmark extraction process without losing the reference value of landmark.

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals

Science.gov (United States)

Zhao, Ming; Lin, Jing; Miao, Yonghao; Xu, Xiaoqiang

2016-01-01

Vibration signals measured in the run-up/coast-down (R/C) processes usually carry rich information about the health status of machinery. However, a major challenge in R/C signals analysis lies in how to exploit more diagnostic information, and how this information could be properly integrated to achieve a more reliable maintenance decision. Aiming at this problem, a framework of R/C signals analysis is presented for the health assessment of gearbox. In the proposed methodology, we first investigate the data preprocessing and feature selection issues for R/C signals. Based on that, a sparsity-guided feature enhancement scheme is then proposed to extract the weak phase jitter associated with gear defect. In order for an effective feature mining and integration under R/C, a generalized phase demodulation technique is further established to reveal the evolution of modulation feature with operating speed and rotation angle. The experimental results indicate that the proposed methodology could not only detect the presence of gear damage, but also offer a novel insight into the dynamic behavior of gearbox. PMID:27827831

A New Indicator for Optimal Preprocessing and Wavelengths Selection of Near-Infrared Spectra

NARCIS (Netherlands)

Skibsted, E.; Boelens, H.F.M.; Westerhuis, J.A.; Witte, D.T.; Smilde, A.K.

2004-01-01

Preprocessing of near-infrared spectra to remove unwanted, i.e., non-related spectral variation and selection of informative wavelengths is considered to be a crucial step prior to the construction of a quantitative calibration model. The standard methodology when comparing various preprocessing

Extract the Relational Information of Static Features and Motion Features for Human Activities Recognition in Videos

Directory of Open Access Journals (Sweden)

Li Yao

2016-01-01

Full Text Available Both static features and motion features have shown promising performance in human activities recognition task. However, the information included in these features is insufficient for complex human activities. In this paper, we propose extracting relational information of static features and motion features for human activities recognition. The videos are represented by a classical Bag-of-Word (BoW model which is useful in many works. To get a compact and discriminative codebook with small dimension, we employ the divisive algorithm based on KL-divergence to reconstruct the codebook. After that, to further capture strong relational information, we construct a bipartite graph to model the relationship between words of different feature set. Then we use a k-way partition to create a new codebook in which similar words are getting together. With this new codebook, videos can be represented by a new BoW vector with strong relational information. Moreover, we propose a method to compute new clusters from the divisive algorithm’s projective function. We test our work on the several datasets and obtain very promising results.

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.

Classification-based comparison of pre-processing methods for interpretation of mass spectrometry generated clinical datasets

Directory of Open Access Journals (Sweden)

Hoefsloot Huub CJ

2009-05-01

Full Text Available Abstract Background Mass spectrometry is increasingly being used to discover proteins or protein profiles associated with disease. Experimental design of mass-spectrometry studies has come under close scrutiny and the importance of strict protocols for sample collection is now understood. However, the question of how best to process the large quantities of data generated is still unanswered. Main challenges for the analysis are the choice of proper pre-processing and classification methods. While these two issues have been investigated in isolation, we propose to use the classification of patient samples as a clinically relevant benchmark for the evaluation of pre-processing methods. Results Two in-house generated clinical SELDI-TOF MS datasets are used in this study as an example of high throughput mass-spectrometry data. We perform a systematic comparison of two commonly used pre-processing methods as implemented in Ciphergen ProteinChip Software and in the Cromwell package. With respect to reproducibility, Ciphergen and Cromwell pre-processing are largely comparable. We find that the overlap between peaks detected by either Ciphergen ProteinChip Software or Cromwell is large. This is especially the case for the more stringent peak detection settings. Moreover, similarity of the estimated intensities between matched peaks is high. We evaluate the pre-processing methods using five different classification methods. Classification is done in a double cross-validation protocol using repeated random sampling to obtain an unbiased estimate of classification accuracy. No pre-processing method significantly outperforms the other for all peak detection settings evaluated. Conclusion We use classification of patient samples as a clinically relevant benchmark for the evaluation of pre-processing methods. Both pre-processing methods lead to similar classification results on an ovarian cancer and a Gaucher disease dataset. However, the settings for pre-processing

Piecewise Polynomial Aggregation as Preprocessing for Data Numerical Modeling

Science.gov (United States)

Dobronets, B. S.; Popova, O. A.

2018-05-01

Data aggregation issues for numerical modeling are reviewed in the present study. The authors discuss data aggregation procedures as preprocessing for subsequent numerical modeling. To calculate the data aggregation, the authors propose using numerical probabilistic analysis (NPA). An important feature of this study is how the authors represent the aggregated data. The study shows that the offered approach to data aggregation can be interpreted as the frequency distribution of a variable. To study its properties, the density function is used. For this purpose, the authors propose using the piecewise polynomial models. A suitable example of such approach is the spline. The authors show that their approach to data aggregation allows reducing the level of data uncertainty and significantly increasing the efficiency of numerical calculations. To demonstrate the degree of the correspondence of the proposed methods to reality, the authors developed a theoretical framework and considered numerical examples devoted to time series aggregation.

Impact of data transformation and preprocessing in supervised ...

African Journals Online (AJOL)

Impact of data transformation and preprocessing in supervised learning ... Nowadays, the ideas of integrating machine learning techniques in power system has ... The proposed algorithm used Python-based split train and k-fold model ...

Fractal Complexity-Based Feature Extraction Algorithm of Communication Signals

Science.gov (United States)

Wang, Hui; Li, Jingchao; Guo, Lili; Dou, Zheng; Lin, Yun; Zhou, Ruolin

How to analyze and identify the characteristics of radiation sources and estimate the threat level by means of detecting, intercepting and locating has been the central issue of electronic support in the electronic warfare, and communication signal recognition is one of the key points to solve this issue. Aiming at accurately extracting the individual characteristics of the radiation source for the increasingly complex communication electromagnetic environment, a novel feature extraction algorithm for individual characteristics of the communication radiation source based on the fractal complexity of the signal is proposed. According to the complexity of the received signal and the situation of environmental noise, use the fractal dimension characteristics of different complexity to depict the subtle characteristics of the signal to establish the characteristic database, and then identify different broadcasting station by gray relation theory system. The simulation results demonstrate that the algorithm can achieve recognition rate of 94% even in the environment with SNR of -10dB, and this provides an important theoretical basis for the accurate identification of the subtle features of the signal at low SNR in the field of information confrontation.

EEMD Independent Extraction for Mixing Features of Rotating Machinery Reconstructed in Phase Space

Directory of Open Access Journals (Sweden)

Zaichao Ma

2015-04-01

Full Text Available Empirical Mode Decomposition (EMD, due to its adaptive decomposition property for the non-linear and non-stationary signals, has been widely used in vibration analyses for rotating machinery. However, EMD suffers from mode mixing, which is difficult to extract features independently. Although the improved EMD, well known as the ensemble EMD (EEMD, has been proposed, mode mixing is alleviated only to a certain degree. Moreover, EEMD needs to determine the amplitude of added noise. In this paper, we propose Phase Space Ensemble Empirical Mode Decomposition (PSEEMD integrating Phase Space Reconstruction (PSR and Manifold Learning (ML for modifying EEMD. We also provide the principle and detailed procedure of PSEEMD, and the analyses on a simulation signal and an actual vibration signal derived from a rubbing rotor are performed. The results show that PSEEMD is more efficient and convenient than EEMD in extracting the mixing features from the investigated signal and in optimizing the amplitude of the necessary added noise. Additionally PSEEMD can extract the weak features interfered with a certain amount of noise.

Three-Dimensional Precession Feature Extraction of Ballistic Targets Based on Narrowband Radar Network

Directory of Open Access Journals (Sweden)

Zhao Shuang

2017-02-01

Full Text Available Micro-motion is a crucial feature used in ballistic target recognition. To address the problem that single-view observations cannot extract true micro-motion parameters, we propose a novel algorithm based on the narrowband radar network to extract three-dimensional precession features. First, we construct a precession model of the cone-shaped target, and as a precondition, we consider the invisible problem of scattering centers. We then analyze in detail the micro-Doppler modulation trait caused by the precession. Then, we match each scattering center in different perspectives based on the ratio of the top scattering center’s micro-Doppler frequency modulation coefficient and extract the 3D coning vector of the target by establishing associated multi-aspect equation systems. In addition, we estimate feature parameters by utilizing the correlation of the micro-Doppler frequency modulation coefficient of the three scattering centers combined with the frequency compensation method. We then calculate the coordinates of the conical point in each moment and reconstruct the 3D spatial portion. Finally, we provide simulation results to validate the proposed algorithm.

Joint Markov Blankets in Feature Sets Extracted from Wavelet Packet Decompositions

Directory of Open Access Journals (Sweden)

Gert Van Dijck

2011-07-01

Full Text Available Since two decades, wavelet packet decompositions have been shown effective as a generic approach to feature extraction from time series and images for the prediction of a target variable. Redundancies exist between the wavelet coefficients and between the energy features that are derived from the wavelet coefficients. We assess these redundancies in wavelet packet decompositions by means of the Markov blanket filtering theory. We introduce the concept of joint Markov blankets. It is shown that joint Markov blankets are a natural extension of Markov blankets, which are defined for single features, to a set of features. We show that these joint Markov blankets exist in feature sets consisting of the wavelet coefficients. Furthermore, we prove that wavelet energy features from the highest frequency resolution level form a joint Markov blanket for all other wavelet energy features. The joint Markov blanket theory indicates that one can expect an increase of classification accuracy with the increase of the frequency resolution level of the energy features.

A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures

Directory of Open Access Journals (Sweden)

T. M. McGinnity

2005-11-01

Full Text Available The paper presents an investigation into a time-frequency (TF method for extracting features from the electroencephalogram (EEG recorded from subjects performing imagination of left- and right-hand movements. The feature extraction procedure (FEP extracts frequency domain information to form features whilst time-frequency resolution is attained by localising the fast Fourier transformations (FFTs of the signals to specific windows localised in time. All features are extracted at the rate of the signal sampling interval from a main feature extraction (FE window through which all data passes. Subject-specific frequency bands are selected for optimal feature extraction and intraclass variations are reduced by smoothing the spectra for each signal by an interpolation (IP process. The TF features are classified using linear discriminant analysis (LDA. The FE window has potential advantages for the FEP to be applied in an online brain-computer interface (BCI. The approach achieves good performance when quantified by classification accuracy (CA rate, information transfer (IT rate, and mutual information (MI. The information that these performance measures provide about a BCI system is analysed and the importance of this is demonstrated through the results.

Vibration Feature Extraction and Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey

Directory of Open Access Journals (Sweden)

Saleem Riaz

2017-02-01

Full Text Available Safety, reliability, efficiency and performance of rotating machinery in all industrial applications are the main concerns. Rotating machines are widely used in various industrial applications. Condition monitoring and fault diagnosis of rotating machinery faults are very important and often complex and labor-intensive. Feature extraction techniques play a vital role for a reliable, effective and efficient feature extraction for the diagnosis of rotating machinery. Therefore, developing effective bearing fault diagnostic method using different fault features at different steps becomes more attractive. Bearings are widely used in medical applications, food processing industries, semi-conductor industries, paper making industries and aircraft components. This paper review has demonstrated that the latest reviews applied to rotating machinery on the available a variety of vibration feature extraction. Generally literature is classified into two main groups: frequency domain, time frequency analysis. However, fault detection and diagnosis of rotating machine vibration signal processing methods to present their own limitations. In practice, most healthy ingredients faulty vibration signal from background noise and mechanical vibration signals are buried. This paper also reviews that how the advanced signal processing methods, empirical mode decomposition and interference cancellation algorithm has been investigated and developed. The condition for rotating machines based rehabilitation, prevent failures increase the availability and reduce the cost of maintenance is becoming necessary too. Rotating machine fault detection and diagnostics in developing algorithms signal processing based on a key problem is the fault feature extraction or quantification. Currently, vibration signal, fault detection and diagnosis of rotating machinery based techniques most widely used techniques. Furthermore, the researchers are widely interested to make automatic

Representation and Metrics Extraction from Feature Basis: An Object Oriented Approach

Directory of Open Access Journals (Sweden)

Fausto Neri da Silva Vanin

2010-10-01

Full Text Available This tutorial presents an object oriented approach to data reading and metrics extraction from feature basis. Structural issues about basis are discussed first, then the Object Oriented Programming (OOP is aplied to modeling the main elements in this context. The model implementation is then discussed using C++ as programing language. To validate the proposed model, we apply on some feature basis from the University of Carolina, Irvine Machine Learning Database.

NAMED ENTITY RECOGNITION FROM BIOMEDICAL TEXT -AN INFORMATION EXTRACTION TASK

Directory of Open Access Journals (Sweden)

N. Kanya

2016-07-01

Full Text Available Biomedical Text Mining targets the Extraction of significant information from biomedical archives. Bio TM encompasses Information Retrieval (IR and Information Extraction (IE. The Information Retrieval will retrieve the relevant Biomedical Literature documents from the various Repositories like PubMed, MedLine etc., based on a search query. The IR Process ends up with the generation of corpus with the relevant document retrieved from the Publication databases based on the query. The IE task includes the process of Preprocessing of the document, Named Entity Recognition (NER from the documents and Relationship Extraction. This process includes Natural Language Processing, Data Mining techniques and machine Language algorithm. The preprocessing task includes tokenization, stop word Removal, shallow parsing, and Parts-Of-Speech tagging. NER phase involves recognition of well-defined objects such as genes, proteins or cell-lines etc. This process leads to the next phase that is extraction of relationships (IE. The work was based on machine learning algorithm Conditional Random Field (CRF.

Using self-organizing maps adaptive resonance theory (CARTMAP) for manufacturing feature recognition

Science.gov (United States)

Yu, Jason S.; Dagli, Cihan H.

1993-10-01

The invariant image preprocessing of moment invariants generates an invariant representation of object features which are insensitive to position, orientation, size, illusion, and contrast change. In this study ARTMAP is used for 3-D object recognition of manufacturing parts through these invariant characteristics. The analog of moment invariants created through the image preprocessing is interpreted by a binary code which is used to predict the manufacturing part through ARTMAP.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression.

Science.gov (United States)

Delwiche, Stephen R; Reeves, James B

2010-01-01

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various

Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery

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Lin Liang

2015-01-01

Full Text Available A new method for extracting the low-dimensional feature automatically with self-organization mapping manifold is proposed for the detection of rotating mechanical nonlinear faults (such as rubbing, pedestal looseness. Under the phase space reconstructed by single vibration signal, the self-organization mapping (SOM with expectation maximization iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention. After that, the local tangent space alignment algorithm is adopted to compress the high-dimensional phase space into low-dimensional feature space. The proposed method takes advantages of the manifold learning in low-dimensional feature extraction and adaptive neighborhood construction of SOM and can extract intrinsic fault features of interest in two dimensional projection space. To evaluate the performance of the proposed method, the Lorenz system was simulated and rotation machinery with nonlinear faults was obtained for test purposes. Compared with the holospectrum approaches, the results reveal that the proposed method is superior in identifying faults and effective for rotating machinery condition monitoring.

An image-processing methodology for extracting bloodstain pattern features.

Science.gov (United States)

Arthur, Ravishka M; Humburg, Philomena J; Hoogenboom, Jerry; Baiker, Martin; Taylor, Michael C; de Bruin, Karla G

2017-08-01

There is a growing trend in forensic science to develop methods to make forensic pattern comparison tasks more objective. This has generally involved the application of suitable image-processing methods to provide numerical data for identification or comparison. This paper outlines a unique image-processing methodology that can be utilised by analysts to generate reliable pattern data that will assist them in forming objective conclusions about a pattern. A range of features were defined and extracted from a laboratory-generated impact spatter pattern. These features were based in part on bloodstain properties commonly used in the analysis of spatter bloodstain patterns. The values of these features were consistent with properties reported qualitatively for such patterns. The image-processing method developed shows considerable promise as a way to establish measurable discriminating pattern criteria that are lacking in current bloodstain pattern taxonomies. Copyright © 2017 Elsevier B.V. All rights reserved.

The extraction and use of facial features in low bit-rate visual communication.

Science.gov (United States)

Pearson, D

1992-01-29

A review is given of experimental investigations by the author and his collaborators into methods of extracting binary features from images of the face and hands. The aim of the research has been to enable deaf people to communicate by sign language over the telephone network. Other applications include model-based image coding and facial-recognition systems. The paper deals with the theoretical postulates underlying the successful experimental extraction of facial features. The basic philosophy has been to treat the face as an illuminated three-dimensional object and to identify features from characteristics of their Gaussian maps. It can be shown that in general a composite image operator linked to a directional-illumination estimator is required to accomplish this, although the latter can often be omitted in practice.

Sabah snake grass extract pre-processing: Preliminary studies in drying and fermentation

Science.gov (United States)

Solibun, A.; Sivakumar, K.

2016-06-01

Clinacanthus nutans (Burm. F.) Lindau which also known as ‘Sabah Snake Grass’ among Malaysians have been studied in terms of its medicinal and chemical properties in Asian countries which is used to treat various diseases from cancer to viral-related diseases such as varicella-zoster virus lesions. Traditionally, this plant has been used by the locals to treat insect and snake bites, skin rashes, diabetes and dysentery. In Malaysia, the fresh leaves of this plant are usually boiled with water and consumed as herbal tea. The objectives of this study are to determine the key process parameters for Sabah Snake Grass fermentation which affect the chemical and biological constituent concentrations within the tea, extraction kinetics of fermented and unfermented tea and the optimal process parameters for the fermentation of this tea. Experimental methods such as drying, fermenting and extraction of C.nutans leaves were conducted before subjecting them to analysis of antioxidant capacity. Conventional oven- dried (40, 45 and 50°C) and fermented (6, 12 and 18 hours) whole C.nutans leaves were subjected to tea infusion extraction (water temperature was 80°C, duration was 90 minutes) and the sample liquid was extracted for every 5th, 10th, 15th, 25th, 40th, 60th and 90th minute. Analysis for antioxidant capacity and total phenolic content (TPC) were conducted by using 2, 2-diphenyl-1-pycryl-hydrazyl (DPPH) and Folin-Ciocaltheu reagent, respectively. The 40°C dried leaves sample produced the highest phenolic content at 0.1344 absorbance value in 15 minutes of extraction while 50°C dried leaves sample produced 0.1298 absorbance value in 10 minutes of extraction. The highest antioxidant content was produced by 50°C dried leaves sample with absorbance value of 1.6299 in 5 minutes of extraction. For 40°C dried leaves sample, the highest antioxidant content could be observed in 25 minutes of extraction with the absorbance value of 1.1456. The largest diameter of disc

Average combination difference morphological filters for fault feature extraction of bearing

Science.gov (United States)

Lv, Jingxiang; Yu, Jianbo

2018-02-01

In order to extract impulse components from vibration signals with much noise and harmonics, a new morphological filter called average combination difference morphological filter (ACDIF) is proposed in this paper. ACDIF constructs firstly several new combination difference (CDIF) operators, and then integrates the best two CDIFs as the final morphological filter. This design scheme enables ACIDF to extract positive and negative impacts existing in vibration signals to enhance accuracy of bearing fault diagnosis. The length of structure element (SE) that affects the performance of ACDIF is determined adaptively by a new indicator called Teager energy kurtosis (TEK). TEK further improves the effectiveness of ACDIF for fault feature extraction. Experimental results on the simulation and bearing vibration signals demonstrate that ACDIF can effectively suppress noise and extract periodic impulses from bearing vibration signals.

Integrated fMRI Preprocessing Framework Using Extended Kalman Filter for Estimation of Slice-Wise Motion

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Basile Pinsard

2018-04-01

Full Text Available Functional MRI acquisition is sensitive to subjects' motion that cannot be fully constrained. Therefore, signal corrections have to be applied a posteriori in order to mitigate the complex interactions between changing tissue localization and magnetic fields, gradients and readouts. To circumvent current preprocessing strategies limitations, we developed an integrated method that correct motion and spatial low-frequency intensity fluctuations at the level of each slice in order to better fit the acquisition processes. The registration of single or multiple simultaneously acquired slices is achieved online by an Iterated Extended Kalman Filter, favoring the robust estimation of continuous motion, while an intensity bias field is non-parametrically fitted. The proposed extraction of gray-matter BOLD activity from the acquisition space to an anatomical group template space, taking into account distortions, better preserves fine-scale patterns of activity. Importantly, the proposed unified framework generalizes to high-resolution multi-slice techniques. When tested on simulated and real data the latter shows a reduction of motion explained variance and signal variability when compared to the conventional preprocessing approach. These improvements provide more stable patterns of activity, facilitating investigation of cerebral information representation in healthy and/or clinical populations where motion is known to impact fine-scale data.

Rotation, scale, and translation invariant pattern recognition using feature extraction

Science.gov (United States)

Prevost, Donald; Doucet, Michel; Bergeron, Alain; Veilleux, Luc; Chevrette, Paul C.; Gingras, Denis J.

1997-03-01

A rotation, scale and translation invariant pattern recognition technique is proposed.It is based on Fourier- Mellin Descriptors (FMD). Each FMD is taken as an independent feature of the object, and a set of those features forms a signature. FMDs are naturally rotation invariant. Translation invariance is achieved through pre- processing. A proper normalization of the FMDs gives the scale invariance property. This approach offers the double advantage of providing invariant signatures of the objects, and a dramatic reduction of the amount of data to process. The compressed invariant feature signature is next presented to a multi-layered perceptron neural network. This final step provides some robustness to the classification of the signatures, enabling good recognition behavior under anamorphically scaled distortion. We also present an original feature extraction technique, adapted to optical calculation of the FMDs. A prototype optical set-up was built, and experimental results are presented.

Special features of SCF solid extraction of natural products: deoiling of wheat gluten and extraction of rose hip oil

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Eggers R.

2000-01-01

Full Text Available Supercritical CO2 extraction has shown great potential in separating vegetable oils as well as removing undesirable oil residuals from natural products. The influence of process parameters, such as pressure, temperature, mass flow and particle size, on the mass transfer kinetics of different natural products has been studied by many authors. However, few publications have focused on specific features of the raw material (moisture, mechanical pretreatment, bed compressibility, etc., which could play an important role, particularly in the scale-up of extraction processes. A review of the influence of both process parameters and specific features of the material on oilseed extraction is given in Eggers (1996. Mechanical pretreatment has been commonly used in order to facilitate mass transfer from the material into the supercritical fluid. However, small particle sizes, especially when combined with high moisture contents, may lead to inefficient extraction results. This paper focuses on the problems that appear during scale-up in processes on a lab to pilot or industrial plant scale related to the pretreatment of material, the control of initial water content and vessel shape. Two applications were studied: deoiling of wheat gluten with supercritical carbon dioxide to produce a totally oil-free (< 0.1 % oil powder (wheat gluten and the extraction of oil from rose hip seeds. Different ways of pretreating the feed material were successfully tested in order to develop an industrial-scale gluten deoiling process. The influence of shape and size of the fixed bed on the extraction results was also studied. In the case of rose hip seeds, the present work discusses the influence of pretreatment of the seeds prior to the extraction process on extraction kinetics.

Thinning: A Preprocessing Technique for an OCR System for the Brahmi Script

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H. K. Anasuya Devi

2006-12-01

Full Text Available In this paper we study the methodology employed for preprocessing the archaeological images. We present the various algorithms used in the low level processing stage of image analysis for Optical Character Recognition System for Brahmi Script. The image preprocessing technique covered in this paper include Thinning method. We also try to analyze the results obtained by the pixel-level processing algorithms.

A Low Cost VLSI Architecture for Spike Sorting Based on Feature Extraction with Peak Search.

Science.gov (United States)

Chang, Yuan-Jyun; Hwang, Wen-Jyi; Chen, Chih-Chang

2016-12-07

The goal of this paper is to present a novel VLSI architecture for spike sorting with high classification accuracy, low area costs and low power consumption. A novel feature extraction algorithm with low computational complexities is proposed for the design of the architecture. In the feature extraction algorithm, a spike is separated into two portions based on its peak value. The area of each portion is then used as a feature. The algorithm is simple to implement and less susceptible to noise interference. Based on the algorithm, a novel architecture capable of identifying peak values and computing spike areas concurrently is proposed. To further accelerate the computation, a spike can be divided into a number of segments for the local feature computation. The local features are subsequently merged with the global ones by a simple hardware circuit. The architecture can also be easily operated in conjunction with the circuits for commonly-used spike detection algorithms, such as the Non-linear Energy Operator (NEO). The architecture has been implemented by an Application-Specific Integrated Circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture is well suited for real-time multi-channel spike detection and feature extraction requiring low hardware area costs, low power consumption and high classification accuracy.

A COMPARATIVE ANALYSIS OF SINGLE AND COMBINATION FEATURE EXTRACTION TECHNIQUES FOR DETECTING CERVICAL CANCER LESIONS

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S. Pradeep Kumar Kenny

2016-02-01

Full Text Available Cervical cancer is the third most common form of cancer affecting women especially in third world countries. The predominant reason for such alarming rate of death is primarily due to lack of awareness and proper health care. As they say, prevention is better than cure, a better strategy has to be put in place to screen a large number of women so that an early diagnosis can help in saving their lives. One such strategy is to implement an automated system. For an automated system to function properly a proper set of features have to be extracted so that the cancer cell can be detected efficiently. In this paper we compare the performances of detecting a cancer cell using a single feature versus a combination feature set technique to see which will suit the automated system in terms of higher detection rate. For this each cell is segmented using multiscale morphological watershed segmentation technique and a series of features are extracted. This process is performed on 967 images and the data extracted is subjected to data mining techniques to determine which feature is best for which stage of cancer. The results thus obtained clearly show a higher percentage of success for combination feature set with 100% accurate detection rate.

Point Cloud Classification of Tesserae from Terrestrial Laser Data Combined with Dense Image Matching for Archaeological Information Extraction

Science.gov (United States)

Poux, F.; Neuville, R.; Billen, R.

2017-08-01

Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor's biased data, each tessera in the high-density point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour's class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud.

Hemorrhage detection in MRI brain images using images features

Science.gov (United States)

Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

2013-11-01

The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

Using different classification models in wheat grading utilizing visual features

Science.gov (United States)

Basati, Zahra; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-04-01

Wheat is one of the most important strategic crops in Iran and in the world. The major component that distinguishes wheat from other grains is the gluten section. In Iran, sunn pest is one of the most important factors influencing the characteristics of wheat gluten and in removing it from a balanced state. The existence of bug-damaged grains in wheat will reduce the quality and price of the product. In addition, damaged grains reduce the enrichment of wheat and the quality of bread products. In this study, after preprocessing and segmentation of images, 25 features including 9 colour features, 10 morphological features, and 6 textual statistical features were extracted so as to classify healthy and bug-damaged wheat grains of Azar cultivar of four levels of moisture content (9, 11.5, 14 and 16.5% w.b.) and two lighting colours (yellow light, the composition of yellow and white lights). Using feature selection methods in the WEKA software and the CfsSubsetEval evaluator, 11 features were chosen as inputs of artificial neural network, decision tree and discriment analysis classifiers. The results showed that the decision tree with the J.48 algorithm had the highest classification accuracy of 90.20%. This was followed by artificial neural network classifier with the topology of 11-19-2 and discrimient analysis classifier at 87.46 and 81.81%, respectively

The Feature Extraction Based on Texture Image Information for Emotion Sensing in Speech

Directory of Open Access Journals (Sweden)

Kun-Ching Wang

2014-09-01

Full Text Available In this paper, we present a novel texture image feature for Emotion Sensing in Speech (ESS. This idea is based on the fact that the texture images carry emotion-related information. The feature extraction is derived from time-frequency representation of spectrogram images. First, we transform the spectrogram as a recognizable image. Next, we use a cubic curve to enhance the image contrast. Then, the texture image information (TII derived from the spectrogram image can be extracted by using Laws’ masks to characterize emotional state. In order to evaluate the effectiveness of the proposed emotion recognition in different languages, we use two open emotional databases including the Berlin Emotional Speech Database (EMO-DB and eNTERFACE corpus and one self-recorded database (KHUSC-EmoDB, to evaluate the performance cross-corpora. The results of the proposed ESS system are presented using support vector machine (SVM as a classifier. Experimental results show that the proposed TII-based feature extraction inspired by visual perception can provide significant classification for ESS systems. The two-dimensional (2-D TII feature can provide the discrimination between different emotions in visual expressions except for the conveyance pitch and formant tracks. In addition, the de-noising in 2-D images can be more easily completed than de-noising in 1-D speech.

Automatic Feature Detection, Description and Matching from Mobile Laser Scanning Data and Aerial Imagery

Science.gov (United States)

Hussnain, Zille; Oude Elberink, Sander; Vosselman, George

2016-06-01

In mobile laser scanning systems, the platform's position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of nadir aerial and MLSPC ortho images. In the data pre-processing step the MLSPC is patch-wise cropped and converted to ortho images. Furthermore, each aerial image patch covering the area of the corresponding MLSPC patch is also cropped from the aerial image. For feature detection, we implemented an adaptive variant of Harris-operator to automatically detect corner feature points on the vertices of road markings. In feature description phase, we used the LATCH binary descriptor, which is robust to data from different sensors. For descriptor matching, we developed an outlier filtering technique, which exploits the arrangements of relative Euclidean-distances and angles between corresponding sets of feature points. We found that the positioning accuracy of the computed correspondence has achieved the pixel level accuracy, where the image resolution is 12cm. Furthermore, the developed approach is reliable when enough road markings are available in the data sets. We conclude that, in urban areas, the developed approach can reliably extract features necessary to improve the MLSPC accuracy to pixel level.

Boosting reversible pushdown machines by preprocessing

DEFF Research Database (Denmark)

Axelsen, Holger Bock; Kutrib, Martin; Malcher, Andreas

2016-01-01

languages, whereas for reversible pushdown automata the accepted family of languages lies strictly in between the reversible deterministic context-free languages and the real-time deterministic context-free languages. Moreover, it is shown that the computational power of both types of machines...... is not changed by allowing the preprocessing sequential transducer to work irreversibly. Finally, we examine the closure properties of the family of languages accepted by such machines....

Vaccine adverse event text mining system for extracting features from vaccine safety reports.

Science.gov (United States)

Botsis, Taxiarchis; Buttolph, Thomas; Nguyen, Michael D; Winiecki, Scott; Woo, Emily Jane; Ball, Robert

2012-01-01

To develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports. Based upon clinical significance to VAERS reviewing physicians, we defined the primary (diagnosis and cause of death) and secondary features (eg, symptoms) for extraction. We built a novel vaccine adverse event text mining (VaeTM) system based on a semantic text mining strategy. The performance of VaeTM was evaluated using a total of 300 VAERS reports in three sequential evaluations of 100 reports each. Moreover, we evaluated the VaeTM contribution to case classification; an information retrieval-based approach was used for the identification of anaphylaxis cases in a set of reports and was compared with two other methods: a dedicated text classifier and an online tool. The performance metrics of VaeTM were text mining metrics: recall, precision and F-measure. We also conducted a qualitative difference analysis and calculated sensitivity and specificity for classification of anaphylaxis cases based on the above three approaches. VaeTM performed best in extracting diagnosis, second level diagnosis, drug, vaccine, and lot number features (lenient F-measure in the third evaluation: 0.897, 0.817, 0.858, 0.874, and 0.914, respectively). In terms of case classification, high sensitivity was achieved (83.1%); this was equal and better compared to the text classifier (83.1%) and the online tool (40.7%), respectively. Our VaeTM implementation of a semantic text mining strategy shows promise in providing accurate and efficient extraction of key features from VAERS narratives.

Feature Extraction of Weld Defectology in Digital Image of Radiographic Film Using Geometric Invariant Moment and Statistical Texture

International Nuclear Information System (INIS)

Muhtadan

2009-01-01

The purpose of this research is to perform feature extraction in weld defect of digital image of radiographic film using geometric invariant moment and statistical texture method. Feature extraction values can be use as values that used to classify and pattern recognition on interpretation of weld defect in digital image of radiographic film by computer automatically. Weld defectology type that used in this research are longitudinal crack, transversal crack, distributed porosity, clustered porosity, wormhole, and no defect. Research methodology on this research are program development to read digital image, then performing image cropping to localize weld position, and then applying geometric invariant moment and statistical texture formulas to find feature values. The result of this research are feature extraction values that have tested with RST (rotation, scale, transformation) treatment and yield moment values that more invariant there are ϕ 3 , ϕ 4 , ϕ 5 from geometric invariant moment method. Feature values from statistical texture that are average intensity, average contrast, smoothness, 3 rd moment, uniformity, and entropy, they used as feature extraction values. (author)

An Improved Rotation Forest for Multi-Feature Remote-Sensing Imagery Classification

Directory of Open Access Journals (Sweden)

Yingchang Xiu

2017-11-01

Full Text Available Multi-feature, especially multi-temporal, remote-sensing data have the potential to improve land cover classification accuracy. However, sometimes it is difficult to utilize all the features efficiently. To enhance classification performance based on multi-feature imagery, an improved rotation forest, combining Principal Component Analysis (PCA and a boosting naïve Bayesian tree (NBTree, is proposed. First, feature extraction was carried out with PCA. The feature set was randomly split into several disjoint subsets; then, PCA was applied to each subset, and new training data for linear extracted features based on original training data were obtained. These steps were repeated several times. Second, based on the new training data, a boosting naïve Bayesian tree was constructed as the base classifier, which aims to achieve lower prediction error than a decision tree in the original rotation forest. At the classification phase, the improved rotation forest has two-layer voting. It first obtains several predictions through weighted voting in a boosting naïve Bayesian tree; then, the first-layer vote predicts by majority to obtain the final result. To examine the classification performance, the improved rotation forest was applied to multi-feature remote-sensing images, including MODIS Enhanced Vegetation Index (EVI imagery time series, MODIS Surface Reflectance products and ancillary data in Shandong Province for 2013. The EVI imagery time series was preprocessed using harmonic analysis of time series (HANTS to reduce the noise effects. The overall accuracy of the final classification result was 89.17%, and the Kappa coefficient was 0.71, which outperforms the original rotation forest and other classifier ensemble results, as well as the NASA land cover product. However, this new algorithm requires more computational time, meaning the efficiency needs to be further improved. Generally, the improved rotation forest has a potential advantage in

Raman spectral feature selection using ant colony optimization for breast cancer diagnosis.

Science.gov (United States)

Fallahzadeh, Omid; Dehghani-Bidgoli, Zohreh; Assarian, Mohammad

2018-06-04

Pathology as a common diagnostic test of cancer is an invasive, time-consuming, and partially subjective method. Therefore, optical techniques, especially Raman spectroscopy, have attracted the attention of cancer diagnosis researchers. However, as Raman spectra contain numerous peaks involved in molecular bounds of the sample, finding the best features related to cancerous changes can improve the accuracy of diagnosis in this method. The present research attempted to improve the power of Raman-based cancer diagnosis by finding the best Raman features using the ACO algorithm. In the present research, 49 spectra were measured from normal, benign, and cancerous breast tissue samples using a 785-nm micro-Raman system. After preprocessing for removal of noise and background fluorescence, the intensity of 12 important Raman bands of the biological samples was extracted as features of each spectrum. Then, the ACO algorithm was applied to find the optimum features for diagnosis. As the results demonstrated, by selecting five features, the classification accuracy of the normal, benign, and cancerous groups increased by 14% and reached 87.7%. ACO feature selection can improve the diagnostic accuracy of Raman-based diagnostic models. In the present study, features corresponding to ν(C-C) αhelix proline, valine (910-940), νs(C-C) skeletal lipids (1110-1130), and δ(CH2)/δ(CH3) proteins (1445-1460) were selected as the best features in cancer diagnosis.

VHDL Implementation of Feature-Extraction Algorithm for the PANDA Electromagnetic Calorimeter

NARCIS (Netherlands)

Kavatsyuk, M.; Guliyev, E.; Lemmens, P. J. J.; Löhner, H.; Tambave, G.

2010-01-01

The feature-extraction algorithm, developed for the digital front-end electronics of the electromagnetic calorimeter of the PANDA detector at the future FAIR facility, is implemented in VHDL for a commercial 16 bit 100 MHz sampling ADC. The use of modified firmware with the running on-line

VHDL implementation of feature-extraction algorithm for the PANDA electromagnetic calorimeter

NARCIS (Netherlands)

Guliyev, E.; Kavatsyuk, M.; Lemmens, P. J. J.; Tambave, G.; Löhner, H.

2012-01-01

A simple, efficient, and robust feature-extraction algorithm, developed for the digital front-end electronics of the electromagnetic calorimeter of the PANDA spectrometer at FAIR, Darmstadt, is implemented in VHDL for a commercial 16 bit 100 MHz sampling ADC. The source-code is available as an

Pre-processing by data augmentation for improved ellipse fitting.

Science.gov (United States)

Kumar, Pankaj; Belchamber, Erika R; Miklavcic, Stanley J

2018-01-01

Ellipse fitting is a highly researched and mature topic. Surprisingly, however, no existing method has thus far considered the data point eccentricity in its ellipse fitting procedure. Here, we introduce the concept of eccentricity of a data point, in analogy with the idea of ellipse eccentricity. We then show empirically that, irrespective of ellipse fitting method used, the root mean square error (RMSE) of a fit increases with the eccentricity of the data point set. The main contribution of the paper is based on the hypothesis that if the data point set were pre-processed to strategically add additional data points in regions of high eccentricity, then the quality of a fit could be improved. Conditional validity of this hypothesis is demonstrated mathematically using a model scenario. Based on this confirmation we propose an algorithm that pre-processes the data so that data points with high eccentricity are replicated. The improvement of ellipse fitting is then demonstrated empirically in real-world application of 3D reconstruction of a plant root system for phenotypic analysis. The degree of improvement for different underlying ellipse fitting methods as a function of data noise level is also analysed. We show that almost every method tested, irrespective of whether it minimizes algebraic error or geometric error, shows improvement in the fit following data augmentation using the proposed pre-processing algorithm.

A Stereo Music Preprocessing Scheme for Cochlear Implant Users.

Science.gov (United States)

Buyens, Wim; van Dijk, Bas; Wouters, Jan; Moonen, Marc

2015-10-01

Listening to music is still one of the more challenging aspects of using a cochlear implant (CI) for most users. Simple musical structures, a clear rhythm/beat, and lyrics that are easy to follow are among the top factors contributing to music appreciation for CI users. Modifying the audio mix of complex music potentially improves music enjoyment in CI users. A stereo music preprocessing scheme is described in which vocals, drums, and bass are emphasized based on the representation of the harmonic and the percussive components in the input spectrogram, combined with the spatial allocation of instruments in typical stereo recordings. The scheme is assessed with postlingually deafened CI subjects (N = 7) using pop/rock music excerpts with different complexity levels. The scheme is capable of modifying relative instrument level settings, with the aim of improving music appreciation in CI users, and allows individual preference adjustments. The assessment with CI subjects confirms the preference for more emphasis on vocals, drums, and bass as offered by the preprocessing scheme, especially for songs with higher complexity. The stereo music preprocessing scheme has the potential to improve music enjoyment in CI users by modifying the audio mix in widespread (stereo) music recordings. Since music enjoyment in CI users is generally poor, this scheme can assist the music listening experience of CI users as a training or rehabilitation tool.

A Low Cost VLSI Architecture for Spike Sorting Based on Feature Extraction with Peak Search

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Yuan-Jyun Chang

2016-12-01

Full Text Available The goal of this paper is to present a novel VLSI architecture for spike sorting with high classification accuracy, low area costs and low power consumption. A novel feature extraction algorithm with low computational complexities is proposed for the design of the architecture. In the feature extraction algorithm, a spike is separated into two portions based on its peak value. The area of each portion is then used as a feature. The algorithm is simple to implement and less susceptible to noise interference. Based on the algorithm, a novel architecture capable of identifying peak values and computing spike areas concurrently is proposed. To further accelerate the computation, a spike can be divided into a number of segments for the local feature computation. The local features are subsequently merged with the global ones by a simple hardware circuit. The architecture can also be easily operated in conjunction with the circuits for commonly-used spike detection algorithms, such as the Non-linear Energy Operator (NEO. The architecture has been implemented by an Application-Specific Integrated Circuit (ASIC with 90-nm technology. Comparisons to the existing works show that the proposed architecture is well suited for real-time multi-channel spike detection and feature extraction requiring low hardware area costs, low power consumption and high classification accuracy.

Depth-based human activity recognition: A comparative perspective study on feature extraction

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Heba Hamdy Ali

2018-06-01

Full Text Available Depth Maps-based Human Activity Recognition is the process of categorizing depth sequences with a particular activity. In this problem, some applications represent robust solutions in domains such as surveillance system, computer vision applications, and video retrieval systems. The task is challenging due to variations inside one class and distinguishes between activities of various classes and video recording settings. In this study, we introduce a detailed study of current advances in the depth maps-based image representations and feature extraction process. Moreover, we discuss the state of art datasets and subsequent classification procedure. Also, a comparative study of some of the more popular depth-map approaches has provided in greater detail. The proposed methods are evaluated on three depth-based datasets “MSR Action 3D”, “MSR Hand Gesture”, and “MSR Daily Activity 3D”. Experimental results achieved 100%, 95.83%, and 96.55% respectively. While combining depth and color features on “RGBD-HuDaAct” Dataset, achieved 89.1%. Keywords: Activity recognition, Depth, Feature extraction, Video, Human body detection, Hand gesture

Singular value decomposition based feature extraction technique for physiological signal analysis.

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Chang, Cheng-Ding; Wang, Chien-Chih; Jiang, Bernard C

2012-06-01

Multiscale entropy (MSE) is one of the popular techniques to calculate and describe the complexity of the physiological signal. Many studies use this approach to detect changes in the physiological conditions in the human body. However, MSE results are easily affected by noise and trends, leading to incorrect estimation of MSE values. In this paper, singular value decomposition (SVD) is adopted to replace MSE to extract the features of physiological signals, and adopt the support vector machine (SVM) to classify the different physiological states. A test data set based on the PhysioNet website was used, and the classification results showed that using SVD to extract features of the physiological signal could attain a classification accuracy rate of 89.157%, which is higher than that using the MSE value (71.084%). The results show the proposed analysis procedure is effective and appropriate for distinguishing different physiological states. This promising result could be used as a reference for doctors in diagnosis of congestive heart failure (CHF) disease.

Comparing Features for Classification of MEG Responses to Motor Imagery.

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Hanna-Leena Halme

Full Text Available Motor imagery (MI with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest.MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD, Morlet wavelets, short-time Fourier transform (STFT, common spatial patterns (CSP, filter-bank common spatial patterns (FBCSP, spatio-spectral decomposition (SSD, and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject.The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7% and MI-vs-rest (mean 81.3% classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%. There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results.We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction

Maximum entropy methods for extracting the learned features of deep neural networks.

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Finnegan, Alex; Song, Jun S

2017-10-01

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

[Identification of special quality eggs with NIR spectroscopy technology based on symbol entropy feature extraction method].

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Zhao, Yong; Hong, Wen-Xue

2011-11-01

Fast, nondestructive and accurate identification of special quality eggs is an urgent problem. The present paper proposed a new feature extraction method based on symbol entropy to identify near infrared spectroscopy of special quality eggs. The authors selected normal eggs, free range eggs, selenium-enriched eggs and zinc-enriched eggs as research objects and measured the near-infrared diffuse reflectance spectra in the range of 12 000-4 000 cm(-1). Raw spectra were symbolically represented with aggregation approximation algorithm and symbolic entropy was extracted as feature vector. An error-correcting output codes multiclass support vector machine classifier was designed to identify the spectrum. Symbolic entropy feature is robust when parameter changed and the highest recognition rate reaches up to 100%. The results show that the identification method of special quality eggs using near-infrared is feasible and the symbol entropy can be used as a new feature extraction method of near-infrared spectra.

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

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Idil Isikli Esener

2017-01-01

Full Text Available A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.

SU-D-BRA-04: Computerized Framework for Marker-Less Localization of Anatomical Feature Points in Range Images Based On Differential Geometry Features for Image-Guided Radiation Therapy

International Nuclear Information System (INIS)

Soufi, M; Arimura, H; Toyofuku, F; Nakamura, K; Hirose, T; Umezu, Y; Shioyama, Y

2016-01-01

Purpose: To propose a computerized framework for localization of anatomical feature points on the patient surface in infrared-ray based range images by using differential geometry (curvature) features. Methods: The general concept was to reconstruct the patient surface by using a mathematical modeling technique for the computation of differential geometry features that characterize the local shapes of the patient surfaces. A region of interest (ROI) was firstly extracted based on a template matching technique applied on amplitude (grayscale) images. The extracted ROI was preprocessed for reducing temporal and spatial noises by using Kalman and bilateral filters, respectively. Next, a smooth patient surface was reconstructed by using a non-uniform rational basis spline (NURBS) model. Finally, differential geometry features, i.e. the shape index and curvedness features were computed for localizing the anatomical feature points. The proposed framework was trained for optimizing shape index and curvedness thresholds and tested on range images of an anthropomorphic head phantom. The range images were acquired by an infrared ray-based time-of-flight (TOF) camera. The localization accuracy was evaluated by measuring the mean of minimum Euclidean distances (MMED) between reference (ground truth) points and the feature points localized by the proposed framework. The evaluation was performed for points localized on convex regions (e.g. apex of nose) and concave regions (e.g. nasofacial sulcus). Results: The proposed framework has localized anatomical feature points on convex and concave anatomical landmarks with MMEDs of 1.91±0.50 mm and 3.70±0.92 mm, respectively. A statistically significant difference was obtained between the feature points on the convex and concave regions (P<0.001). Conclusion: Our study has shown the feasibility of differential geometry features for localization of anatomical feature points on the patient surface in range images. The proposed

SU-D-BRA-04: Computerized Framework for Marker-Less Localization of Anatomical Feature Points in Range Images Based On Differential Geometry Features for Image-Guided Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Soufi, M; Arimura, H; Toyofuku, F [Kyushu University, Fukuoka, Fukuoka (Japan); Nakamura, K [Hamamatsu University School of Medicine, Hamamatsu, Shizuoka (Japan); Hirose, T; Umezu, Y [Kyushu University Hospital, Fukuoka, Fukuoka (Japan); Shioyama, Y [Saga Heavy Ion Medical Accelerator in Tosu, Tosu, Saga (Japan)

2016-06-15

Purpose: To propose a computerized framework for localization of anatomical feature points on the patient surface in infrared-ray based range images by using differential geometry (curvature) features. Methods: The general concept was to reconstruct the patient surface by using a mathematical modeling technique for the computation of differential geometry features that characterize the local shapes of the patient surfaces. A region of interest (ROI) was firstly extracted based on a template matching technique applied on amplitude (grayscale) images. The extracted ROI was preprocessed for reducing temporal and spatial noises by using Kalman and bilateral filters, respectively. Next, a smooth patient surface was reconstructed by using a non-uniform rational basis spline (NURBS) model. Finally, differential geometry features, i.e. the shape index and curvedness features were computed for localizing the anatomical feature points. The proposed framework was trained for optimizing shape index and curvedness thresholds and tested on range images of an anthropomorphic head phantom. The range images were acquired by an infrared ray-based time-of-flight (TOF) camera. The localization accuracy was evaluated by measuring the mean of minimum Euclidean distances (MMED) between reference (ground truth) points and the feature points localized by the proposed framework. The evaluation was performed for points localized on convex regions (e.g. apex of nose) and concave regions (e.g. nasofacial sulcus). Results: The proposed framework has localized anatomical feature points on convex and concave anatomical landmarks with MMEDs of 1.91±0.50 mm and 3.70±0.92 mm, respectively. A statistically significant difference was obtained between the feature points on the convex and concave regions (P<0.001). Conclusion: Our study has shown the feasibility of differential geometry features for localization of anatomical feature points on the patient surface in range images. The proposed

Real-time implementation of optimized maximum noise fraction transform for feature extraction of hyperspectral images

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Wu, Yuanfeng; Gao, Lianru; Zhang, Bing; Zhao, Haina; Li, Jun

2014-01-01

We present a parallel implementation of the optimized maximum noise fraction (G-OMNF) transform algorithm for feature extraction of hyperspectral images on commodity graphics processing units (GPUs). The proposed approach explored the algorithm data-level concurrency and optimized the computing flow. We first defined a three-dimensional grid, in which each thread calculates a sub-block data to easily facilitate the spatial and spectral neighborhood data searches in noise estimation, which is one of the most important steps involved in OMNF. Then, we optimized the processing flow and computed the noise covariance matrix before computing the image covariance matrix to reduce the original hyperspectral image data transmission. These optimization strategies can greatly improve the computing efficiency and can be applied to other feature extraction algorithms. The proposed parallel feature extraction algorithm was implemented on an Nvidia Tesla GPU using the compute unified device architecture and basic linear algebra subroutines library. Through the experiments on several real hyperspectral images, our GPU parallel implementation provides a significant speedup of the algorithm compared with the CPU implementation, especially for highly data parallelizable and arithmetically intensive algorithm parts, such as noise estimation. In order to further evaluate the effectiveness of G-OMNF, we used two different applications: spectral unmixing and classification for evaluation. Considering the sensor scanning rate and the data acquisition time, the proposed parallel implementation met the on-board real-time feature extraction.

Feature Extraction

CERN Document Server

CERN. Geneva

2015-01-01

Feature selection and reduction are key to robust multivariate analyses. In this talk I will focus on pros and cons of various variable selection methods and focus on those that are most relevant in the context of HEP.

Reaction Decoder Tool (RDT): extracting features from chemical reactions.

Science.gov (United States)

Rahman, Syed Asad; Torrance, Gilliean; Baldacci, Lorenzo; Martínez Cuesta, Sergio; Fenninger, Franz; Gopal, Nimish; Choudhary, Saket; May, John W; Holliday, Gemma L; Steinbeck, Christoph; Thornton, Janet M

2016-07-01

Extracting chemical features like Atom-Atom Mapping (AAM), Bond Changes (BCs) and Reaction Centres from biochemical reactions helps us understand the chemical composition of enzymatic reactions. Reaction Decoder is a robust command line tool, which performs this task with high accuracy. It supports standard chemical input/output exchange formats i.e. RXN/SMILES, computes AAM, highlights BCs and creates images of the mapped reaction. This aids in the analysis of metabolic pathways and the ability to perform comparative studies of chemical reactions based on these features. This software is implemented in Java, supported on Windows, Linux and Mac OSX, and freely available at https://github.com/asad/ReactionDecoder : asad@ebi.ac.uk or s9asad@gmail.com. © The Author 2016. Published by Oxford University Press.

On Feature Extraction from Large Scale Linear LiDAR Data

Science.gov (United States)

Acharjee, Partha Pratim

Airborne light detection and ranging (LiDAR) can generate co-registered elevation and intensity map over large terrain. The co-registered 3D map and intensity information can be used efficiently for different feature extraction application. In this dissertation, we developed two algorithms for feature extraction, and usages of features for practical applications. One of the developed algorithms can map still and flowing waterbody features, and another one can extract building feature and estimate solar potential on rooftops and facades. Remote sensing capabilities, distinguishing characteristics of laser returns from water surface and specific data collection procedures provide LiDAR data an edge in this application domain. Furthermore, water surface mapping solutions must work on extremely large datasets, from a thousand square miles, to hundreds of thousands of square miles. National and state-wide map generation/upgradation and hydro-flattening of LiDAR data for many other applications are two leading needs of water surface mapping. These call for as much automation as possible. Researchers have developed many semi-automated algorithms using multiple semi-automated tools and human interventions. This reported work describes a consolidated algorithm and toolbox developed for large scale, automated water surface mapping. Geometric features such as flatness of water surface, higher elevation change in water-land interface and, optical properties such as dropouts caused by specular reflection, bimodal intensity distributions were some of the linear LiDAR features exploited for water surface mapping. Large-scale data handling capabilities are incorporated by automated and intelligent windowing, by resolving boundary issues and integrating all results to a single output. This whole algorithm is developed as an ArcGIS toolbox using Python libraries. Testing and validation are performed on a large datasets to determine the effectiveness of the toolbox and results are

Feature Extraction and Analysis of Breast Cancer Specimen

Science.gov (United States)

Bhattacharyya, Debnath; Robles, Rosslin John; Kim, Tai-Hoon; Bandyopadhyay, Samir Kumar

In this paper, we propose a method to identify abnormal growth of cells in breast tissue and suggest further pathological test, if necessary. We compare normal breast tissue with malignant invasive breast tissue by a series of image processing steps. Normal ductal epithelial cells and ductal / lobular invasive carcinogenic cells also consider for comparison here in this paper. In fact, features of cancerous breast tissue (invasive) are extracted and analyses with normal breast tissue. We also suggest the breast cancer recognition technique through image processing and prevention by controlling p53 gene mutation to some greater extent.

Extracting intrinsic functional networks with feature-based group independent component analysis.

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Calhoun, Vince D; Allen, Elena

2013-04-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro

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.

Detailed Investigation and Comparison of the XCMS and MZmine 2 Chromatogram Construction and Chromatographic Peak Detection Methods for Preprocessing Mass Spectrometry Metabolomics Data.

Science.gov (United States)

Myers, Owen D; Sumner, Susan J; Li, Shuzhao; Barnes, Stephen; Du, Xiuxia

2017-09-05

XCMS and MZmine 2 are two widely used software packages for preprocessing untargeted LC/MS metabolomics data. Both construct extracted ion chromatograms (EICs) and detect peaks from the EICs, the first two steps in the data preprocessing workflow. While both packages have performed admirably in peak picking, they also detect a problematic number of false positive EIC peaks and can also fail to detect real EIC peaks. The former and latter translate downstream into spurious and missing compounds and present significant limitations with most existing software packages that preprocess untargeted mass spectrometry metabolomics data. We seek to understand the specific reasons why XCMS and MZmine 2 find the false positive EIC peaks that they do and in what ways they fail to detect real compounds. We investigate differences of EIC construction methods in XCMS and MZmine 2 and find several problems in the XCMS centWave peak detection algorithm which we show are partly responsible for the false positive and false negative compound identifications. In addition, we find a problem with MZmine 2's use of centWave. We hope that a detailed understanding of the XCMS and MZmine 2 algorithms will allow users to work with them more effectively and will also help with future algorithmic development.

Composite Wavelet Filters for Enhanced Automated Target Recognition

Science.gov (United States)

Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.

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

Science.gov (United States)

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-12-02

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.

ANTHOCYANINS ALIPHATIC ALCOHOLS EXTRACTION FEATURES

Directory of Open Access Journals (Sweden)

P. N. Savvin

2015-01-01

Full Text Available Anthocyanins red pigments that give color a wide range of fruits, berries and flowers. In the food industry it is widely known as a dye a food additive E163. To extract from natural vegetable raw materials traditionally used ethanol or acidified water, but in same technologies it’s unacceptable. In order to expand the use of anthocyanins as colorants and antioxidants were explored extracting pigments alcohols with different structures of the carbon skeleton, and the position and number of hydroxyl groups. For the isolation anthocyanins raw materials were extracted sequentially twice with t = 60 C for 1.5 hours. The evaluation was performed using extracts of classical spectrophotometric methods and modern express chromaticity. Color black currant extracts depends on the length of the carbon skeleton and position of the hydroxyl group, with the alcohols of normal structure have higher alcohols compared to the isomeric structure of the optical density and index of the red color component. This is due to the different ability to form hydrogen bonds when allocating anthocyanins and other intermolecular interactions. During storage blackcurrant extracts are significant structural changes recoverable pigments, which leads to a significant change in color. In this variation, the stronger the higher the length of the carbon skeleton and branched molecules extractant. Extraction polyols (ethyleneglycol, glycerol are less effective than the corresponding monohydric alcohols. However these extracts saved significantly higher because of their reducing ability at interacting with polyphenolic compounds.

Deep SOMs for automated feature extraction and classification from big data streaming

Science.gov (United States)

Sakkari, Mohamed; Ejbali, Ridha; Zaied, Mourad

2017-03-01

In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

A method of evolving novel feature extraction algorithms for detecting buried objects in FLIR imagery using genetic programming

Science.gov (United States)

Paino, A.; Keller, J.; Popescu, M.; Stone, K.

2014-06-01

In this paper we present an approach that uses Genetic Programming (GP) to evolve novel feature extraction algorithms for greyscale images. Our motivation is to create an automated method of building new feature extraction algorithms for images that are competitive with commonly used human-engineered features, such as Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). The evolved feature extraction algorithms are functions defined over the image space, and each produces a real-valued feature vector of variable length. Each evolved feature extractor breaks up the given image into a set of cells centered on every pixel, performs evolved operations on each cell, and then combines the results of those operations for every cell using an evolved operator. Using this method, the algorithm is flexible enough to reproduce both LBP and HOG features. The dataset we use to train and test our approach consists of a large number of pre-segmented image "chips" taken from a Forward Looking Infrared Imagery (FLIR) camera mounted on the hood of a moving vehicle. The goal is to classify each image chip as either containing or not containing a buried object. To this end, we define the fitness of a candidate solution as the cross-fold validation accuracy of the features generated by said candidate solution when used in conjunction with a Support Vector Machine (SVM) classifier. In order to validate our approach, we compare the classification accuracy of an SVM trained using our evolved features with the accuracy of an SVM trained using mainstream feature extraction algorithms, including LBP and HOG.

Multi-scale Analysis of High Resolution Topography: Feature Extraction and Identification of Landscape Characteristic Scales

Science.gov (United States)

Passalacqua, P.; Sangireddy, H.; Stark, C. P.

2015-12-01

With the advent of digital terrain data, detailed information on terrain characteristics and on scale and location of geomorphic features is available over extended areas. Our ability to observe landscapes and quantify topographic patterns has greatly improved, including the estimation of fluxes of mass and energy across landscapes. Challenges still remain in the analysis of high resolution topography data; the presence of features such as roads, for example, challenges classic methods for feature extraction and large data volumes require computationally efficient extraction and analysis methods. Moreover, opportunities exist to define new robust metrics of landscape characterization for landscape comparison and model validation. In this presentation we cover recent research in multi-scale and objective analysis of high resolution topography data. We show how the analysis of the probability density function of topographic attributes such as slope, curvature, and topographic index contains useful information for feature localization and extraction. The analysis of how the distributions change across scales, quantified by the behavior of modal values and interquartile range, allows the identification of landscape characteristic scales, such as terrain roughness. The methods are introduced on synthetic signals in one and two dimensions and then applied to a variety of landscapes of different characteristics. Validation of the methods includes the analysis of modeled landscapes where the noise distribution is known and features of interest easily measured.

New Hybrid Features Selection Method: A Case Study on Websites Phishing

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Khairan D. Rajab

2017-01-01

Full Text Available Phishing is one of the serious web threats that involves mimicking authenticated websites to deceive users in order to obtain their financial information. Phishing has caused financial damage to the different online stakeholders. It is massive in the magnitude of hundreds of millions; hence it is essential to minimize this risk. Classifying websites into “phishy” and legitimate types is a primary task in data mining that security experts and decision makers are hoping to improve particularly with respect to the detection rate and reliability of the results. One way to ensure the reliability of the results and to enhance performance is to identify a set of related features early on so the data dimensionality reduces and irrelevant features are discarded. To increase reliability of preprocessing, this article proposes a new feature selection method that combines the scores of multiple known methods to minimize discrepancies in feature selection results. The proposed method has been applied to the problem of website phishing classification to show its pros and cons in identifying relevant features. Results against a security dataset reveal that the proposed preprocessing method was able to derive new features datasets which when mined generate high competitive classifiers with reference to detection rate when compared to results obtained from other features selection methods.

Advancing Affect Modeling via Preference Learning and Unsupervised Feature Extraction

DEFF Research Database (Denmark)

Martínez, Héctor Pérez

strategies (error functions and training algorithms) for artificial neural networks are examined across synthetic and psycho-physiological datasets, and compared against support vector machines and Cohen’s method. Results reveal the best training strategies for neural networks and suggest their superiority...... difficulties, ordinal reports such as rankings and ratings can yield more reliable affect annotations than alternative tools. This thesis explores preference learning methods to automatically learn computational models from ordinal annotations of affect. In particular, an extensive collection of training...... over the other examined methods. The second challenge addressed in this thesis refers to the extraction of relevant information from physiological modalities. Deep learning is proposed as an automatic approach to extract input features for models of affect from physiological signals. Experiments...

Retinal Identification Based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform

Directory of Open Access Journals (Sweden)

Xiaoming Xi

2013-07-01

Full Text Available Retinal identification based on retinal vasculatures in the retina provides the most secure and accurate means of authentication among biometrics and has primarily been used in combination with access control systems at high security facilities. Recently, there has been much interest in retina identification. As digital retina images always suffer from deformations, the Scale Invariant Feature Transform (SIFT, which is known for its distinctiveness and invariance for scale and rotation, has been introduced to retinal based identification. However, some shortcomings like the difficulty of feature extraction and mismatching exist in SIFT-based identification. To solve these problems, a novel preprocessing method based on the Improved Circular Gabor Transform (ICGF is proposed. After further processing by the iterated spatial anisotropic smooth method, the number of uninformative SIFT keypoints is decreased dramatically. Tested on the VARIA and eight simulated retina databases combining rotation and scaling, the developed method presents promising results and shows robustness to rotations and scale changes.

GA Based Optimal Feature Extraction Method for Functional Data Classification

OpenAIRE

Jun Wan; Zehua Chen; Yingwu Chen; Zhidong Bai

2010-01-01

Classification is an interesting problem in functional data analysis (FDA), because many science and application problems end up with classification problems, such as recognition, prediction, control, decision making, management, etc. As the high dimension and high correlation in functional data (FD), it is a key problem to extract features from FD whereas keeping its global characters, which relates to the classification efficiency and precision to heavens. In this paper...

Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

OpenAIRE

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history si...

Optimal preprocessing of serum and urine metabolomic data fusion for staging prostate cancer through design of experiment

International Nuclear Information System (INIS)

Zheng, Hong; Cai, Aimin; Zhou, Qi; Xu, Pengtao; Zhao, Liangcai; Li, Chen; Dong, Baijun; Gao, Hongchang

2017-01-01

Accurate classification of cancer stages will achieve precision treatment for cancer. Metabolomics presents biological phenotypes at the metabolite level and holds a great potential for cancer classification. Since metabolomic data can be obtained from different samples or analytical techniques, data fusion has been applied to improve classification accuracy. Data preprocessing is an essential step during metabolomic data analysis. Therefore, we developed an innovative optimization method to select a proper data preprocessing strategy for metabolomic data fusion using a design of experiment approach for improving the classification of prostate cancer (PCa) stages. In this study, urine and serum samples were collected from participants at five phases of PCa and analyzed using a 1 H NMR-based metabolomic approach. Partial least squares-discriminant analysis (PLS-DA) was used as a classification model and its performance was assessed by goodness of fit (R 2 ) and predictive ability (Q 2 ). Results show that data preprocessing significantly affect classification performance and depends on data properties. Using the fused metabolomic data from urine and serum, PLS-DA model with the optimal data preprocessing (R 2  = 0.729, Q 2  = 0.504, P < 0.0001) can effectively improve model performance and achieve a better classification result for PCa stages as compared with that without data preprocessing (R 2  = 0.139, Q 2  = 0.006, P = 0.450). Therefore, we propose that metabolomic data fusion integrated with an optimal data preprocessing strategy can significantly improve the classification of cancer stages for precision treatment. - Highlights: • NMR metabolomic analysis of body fluids can be used for staging prostate cancer. • Data preprocessing is an essential step for metabolomic analysis. • Data fusion improves information recovery for cancer classification. • Design of experiment achieves optimal preprocessing of metabolomic data fusion.

Data preprocessing methods of FT-NIR spectral data for the classification cooking oil

Science.gov (United States)

Ruah, Mas Ezatul Nadia Mohd; Rasaruddin, Nor Fazila; Fong, Sim Siong; Jaafar, Mohd Zuli

2014-12-01

This recent work describes the data pre-processing method of FT-NIR spectroscopy datasets of cooking oil and its quality parameters with chemometrics method. Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modelling. Hence, this work is dedicated to investigate the utility and effectiveness of pre-processing algorithms namely row scaling, column scaling and single scaling process with Standard Normal Variate (SNV). The combinations of these scaling methods have impact on exploratory analysis and classification via Principle Component Analysis plot (PCA). The samples were divided into palm oil and non-palm cooking oil. The classification model was build using FT-NIR cooking oil spectra datasets in absorbance mode at the range of 4000cm-1-14000cm-1. Savitzky Golay derivative was applied before developing the classification model. Then, the data was separated into two sets which were training set and test set by using Duplex method. The number of each class was kept equal to 2/3 of the class that has the minimum number of sample. Then, the sample was employed t-statistic as variable selection method in order to select which variable is significant towards the classification models. The evaluation of data pre-processing were looking at value of modified silhouette width (mSW), PCA and also Percentage Correctly Classified (%CC). The results show that different data processing strategies resulting to substantial amount of model performances quality. The effects of several data pre-processing i.e. row scaling, column standardisation and single scaling process with Standard Normal Variate indicated by mSW and %CC. At two PCs model, all five classifier gave high %CC except Quadratic Distance Analysis.

Improving features used for hyper-temporal land cover change detection by reducing the uncertainty in the feature extraction method

CSIR Research Space (South Africa)

Salmon, BP

2017-07-01

Full Text Available the effect which the length of a temporal sliding window has on the success of detecting land cover change. It is shown using a short Fourier transform as a feature extraction method provides meaningful robust input to a machine learning method. In theory...

Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

OpenAIRE

Ahuja, Jyoti; GJUST - Guru Jambheshwar University of Sciecne and Technology; Ratnoo, Saroj Dahiya; GJUST - Guru Jambheshwar University of Sciecne and Technology

2015-01-01

Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the featu...

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

Directory of Open Access Journals (Sweden)

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.

Optimal production scheduling for energy efficiency improvement in biofuel feedstock preprocessing considering work-in-process particle separation

International Nuclear Information System (INIS)

Li, Lin; Sun, Zeyi; Yao, Xufeng; Wang, Donghai

2016-01-01

Biofuel is considered a promising alternative to traditional liquid transportation fuels. The large-scale substitution of biofuel can greatly enhance global energy security and mitigate greenhouse gas emissions. One major concern of the broad adoption of biofuel is the intensive energy consumption in biofuel manufacturing. This paper focuses on the energy efficiency improvement of biofuel feedstock preprocessing, a major process of cellulosic biofuel manufacturing. An improved scheme of the feedstock preprocessing considering work-in-process particle separation is introduced to reduce energy waste and improve energy efficiency. A scheduling model based on the improved scheme is also developed to identify an optimal production schedule that can minimize the energy consumption of the feedstock preprocessing under production target constraint. A numerical case study is used to illustrate the effectiveness of the proposed method. The research outcome is expected to improve the energy efficiency and enhance the environmental sustainability of biomass feedstock preprocessing. - Highlights: • A novel method to schedule production in biofuel feedstock preprocessing process. • Systems modeling approach is used. • Capable of optimize preprocessing to reduce energy waste and improve energy efficiency. • A numerical case is used to illustrate the effectiveness of the method. • Energy consumption per unit production can be significantly reduced.

Constructing New Biorthogonal Wavelet Type which Matched for Extracting the Iris Image Features

International Nuclear Information System (INIS)

Isnanto, R Rizal; Suhardjo; Susanto, Adhi

2013-01-01

Some former research have been made for obtaining a new type of wavelet. In case of iris recognition using orthogonal or biorthogonal wavelets, it had been obtained that Haar filter is most suitable to recognize the iris image. However, designing the new wavelet should be done to find a most matched wavelet to extract the iris image features, for which we can easily apply it for identification, recognition, or authentication purposes. In this research, a new biorthogonal wavelet was designed based on Haar filter properties and Haar's orthogonality conditions. As result, it can be obtained a new biorthogonal 5/7 filter type wavelet which has a better than other types of wavelets, including Haar, to extract the iris image features based on its mean-squared error (MSE) and Euclidean distance parameters.

ROAD AND ROADSIDE FEATURE EXTRACTION USING IMAGERY AND LIDAR DATA FOR TRANSPORTATION OPERATION

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S. Ural

2015-03-01

Full Text Available Transportation agencies require up-to-date, reliable, and feasibly acquired information on road geometry and features within proximity to the roads as input for evaluating and prioritizing new or improvement road projects. The information needed for a robust evaluation of road projects includes road centerline, width, and extent together with the average grade, cross-sections, and obstructions near the travelled way. Remote sensing is equipped with a large collection of data and well-established tools for acquiring the information and extracting aforementioned various road features at various levels and scopes. Even with many remote sensing data and methods available for road extraction, transportation operation requires more than the centerlines. Acquiring information that is spatially coherent at the operational level for the entire road system is challenging and needs multiple data sources to be integrated. In the presented study, we established a framework that used data from multiple sources, including one-foot resolution color infrared orthophotos, airborne LiDAR point clouds, and existing spatially non-accurate ancillary road networks. We were able to extract 90.25% of a total of 23.6 miles of road networks together with estimated road width, average grade along the road, and cross sections at specified intervals. Also, we have extracted buildings and vegetation within a predetermined proximity to the extracted road extent. 90.6% of 107 existing buildings were correctly identified with 31% false detection rate.

Time Domain Feature Extraction Technique for earth's electric field signal prior to the Earthquake

International Nuclear Information System (INIS)

Astuti, W; Sediono, W; Akmeliawati, R; Salami, M J E

2013-01-01

Earthquake is one of the most destructive of natural disasters that killed many people and destroyed a lot of properties. By considering these catastrophic effects, it is highly important of knowing ahead of earthquakes in order to reduce the number of victims and material losses. Earth's electric field is one of the features that can be used to predict earthquakes (EQs), since it has significant changes in the amplitude of the signal prior to the earthquake. This paper presents a detailed analysis of the earth's electric field due to earthquakes which occurred in Greece, between January 1, 2008 and June 30, 2008. In that period of time, 13 earthquakes had occurred. 6 of them were recorded with magnitudes greater than Ms=5R (5R), while 7 of them were recorded with magnitudes greater than Ms=6R (6R). Time domain feature extraction technique is applied to analyze the 1st significant changes in the earth's electric field prior to the earthquake. Two different time domain feature extraction techniques are applied in this work, namely Simple Square Integral (SSI) and Root Mean Square (RMS). The 1st significant change of the earth's electric field signal in each of monitoring sites is extracted using those two techniques. The feature extraction result can be used as input parameter for an earthquake prediction system

One-Dimensional Signal Extraction Of Paper-Written ECG Image And Its Archiving

Science.gov (United States)

Zhang, Zhi-ni; Zhang, Hong; Zhuang, Tian-ge

1987-10-01

A method for converting paper-written electrocardiograms to one dimensional (1-D) signals for archival storage on floppy disk is presented here. Appropriate image processing techniques were employed to remove the back-ground noise inherent to ECG recorder charts and to reconstruct the ECG waveform. The entire process consists of (1) digitization of paper-written ECGs with an image processing system via a TV camera; (2) image preprocessing, including histogram filtering and binary image generation; (3) ECG feature extraction and ECG wave tracing, and (4) transmission of the processed ECG data to IBM-PC compatible floppy disks for storage and retrieval. The algorithms employed here may also be used in the recognition of paper-written EEG or EMG and may be useful in robotic vision.

Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

Directory of Open Access Journals (Sweden)

Jia Uddin

2014-01-01

Full Text Available This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D texture features and a multiclass support vector machine (MCSVM. The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns, and extracts these texture features by generating the dominant neighborhood structure (DNS map. The principal component analysis (PCA is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA multiclass support vector machines (MCSVMs to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

Information Extraction for Clinical Data Mining: A Mammography Case Study.

Science.gov (United States)

Nassif, Houssam; Woods, Ryan; Burnside, Elizabeth; Ayvaci, Mehmet; Shavlik, Jude; Page, David

2009-01-01

Breast cancer is the leading cause of cancer mortality in women between the ages of 15 and 54. During mammography screening, radiologists use a strict lexicon (BI-RADS) to describe and report their findings. Mammography records are then stored in a well-defined database format (NMD). Lately, researchers have applied data mining and machine learning techniques to these databases. They successfully built breast cancer classifiers that can help in early detection of malignancy. However, the validity of these models depends on the quality of the underlying databases. Unfortunately, most databases suffer from inconsistencies, missing data, inter-observer variability and inappropriate term usage. In addition, many databases are not compliant with the NMD format and/or solely consist of text reports. BI-RADS feature extraction from free text and consistency checks between recorded predictive variables and text reports are crucial to addressing this problem. We describe a general scheme for concept information retrieval from free text given a lexicon, and present a BI-RADS features extraction algorithm for clinical data mining. It consists of a syntax analyzer, a concept finder and a negation detector. The syntax analyzer preprocesses the input into individual sentences. The concept finder uses a semantic grammar based on the BI-RADS lexicon and the experts' input. It parses sentences detecting BI-RADS concepts. Once a concept is located, a lexical scanner checks for negation. Our method can handle multiple latent concepts within the text, filtering out ultrasound concepts. On our dataset, our algorithm achieves 97.7% precision, 95.5% recall and an F 1 -score of 0.97. It outperforms manual feature extraction at the 5% statistical significance level.

A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Sunil Tyagi

2017-04-01

Full Text Available A classification technique using Support Vector Machine (SVM classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditions. The time-domain vibration signals were divided into 40 segments and simple features such as peaks in time domain and spectrum along with statistical features such as standard deviation, skewness, kurtosis etc. were extracted. Effectiveness of SVM classifier was compared with the performance of Artificial Neural Network (ANN classifier and it was found that the performance of SVM classifier is superior to that of ANN. The effect of pre-processing of the vibration signal by Discreet Wavelet Transform (DWT prior to feature extraction is also studied and it is shown that pre-processing of vibration signal with DWT enhances the effectiveness of both ANN and SVM classifiers. It has been demonstrated from experiment results that performance of SVM classifier is better than ANN in detection of bearing condition and pre-processing the vibration signal with DWT improves the performance of SVM classifier.

Feature extraction algorithm for space targets based on fractal theory

Science.gov (United States)

Tian, Balin; Yuan, Jianping; Yue, Xiaokui; Ning, Xin

2007-11-01

In order to offer a potential for extending the life of satellites and reducing the launch and operating costs, satellite servicing including conducting repairs, upgrading and refueling spacecraft on-orbit become much more frequently. Future space operations can be more economically and reliably executed using machine vision systems, which can meet real time and tracking reliability requirements for image tracking of space surveillance system. Machine vision was applied to the research of relative pose for spacecrafts, the feature extraction algorithm was the basis of relative pose. In this paper fractal geometry based edge extraction algorithm which can be used in determining and tracking the relative pose of an observed satellite during proximity operations in machine vision system was presented. The method gets the gray-level image distributed by fractal dimension used the Differential Box-Counting (DBC) approach of the fractal theory to restrain the noise. After this, we detect the consecutive edge using Mathematical Morphology. The validity of the proposed method is examined by processing and analyzing images of space targets. The edge extraction method not only extracts the outline of the target, but also keeps the inner details. Meanwhile, edge extraction is only processed in moving area to reduce computation greatly. Simulation results compared edge detection using the method which presented by us with other detection methods. The results indicate that the presented algorithm is a valid method to solve the problems of relative pose for spacecrafts.

Features extraction from image based on processes of human vision; Ningen no shikaku shori tejun ni naratta gazo no tokucho chushutsu hoho

Energy Technology Data Exchange (ETDEWEB)

Nakajima, C.; Ishino, R. [Central Research Institute of Electric Power Industry, Tokyo (Japan)

1997-03-01

Described herein is a feature extraction processing method necessary for measurement of an object on a stationary image. It imitates the structures and functions of the visual area in the human brain to automatically extract features, such as edges, lines and apexes, from a stationary image or drawing. Information transmitted from the retina to the primary visual cortex area 1 (V1 area) is processed to extract feature candidates from brightness changes on the shading-treated image. The V1 area has the cells which react with long lines and the structure which controls the other cells. This structure is used to remove noise, where a portion which is not controlled is extracted from feature candidates, and is regarded as a line feature. This procedure, involving shading, is not suited for process of images containing an out-of-focus portion, but stably extracts features from clear images or drawings. 9 refs., 28 figs.

Feature extraction and classification of clouds in high resolution panchromatic satellite imagery

Science.gov (United States)

Sharghi, Elan

The development of sophisticated remote sensing sensors is rapidly increasing, and the vast amount of satellite imagery collected is too much to be analyzed manually by a human image analyst. It has become necessary for a tool to be developed to automate the job of an image analyst. This tool would need to intelligently detect and classify objects of interest through computer vision algorithms. Existing software called the Rapid Image Exploitation Resource (RAPIER®) was designed by engineers at Space and Naval Warfare Systems Center Pacific (SSC PAC) to perform exactly this function. This software automatically searches for anomalies in the ocean and reports the detections as a possible ship object. However, if the image contains a high percentage of cloud coverage, a high number of false positives are triggered by the clouds. The focus of this thesis is to explore various feature extraction and classification methods to accurately distinguish clouds from ship objects. An examination of a texture analysis method, line detection using the Hough transform, and edge detection using wavelets are explored as possible feature extraction methods. The features are then supplied to a K-Nearest Neighbors (KNN) or Support Vector Machine (SVM) classifier. Parameter options for these classifiers are explored and the optimal parameters are determined.

Automatic building extraction from LiDAR data fusion of point and grid-based features

Science.gov (United States)

Du, Shouji; Zhang, Yunsheng; Zou, Zhengrong; Xu, Shenghua; He, Xue; Chen, Siyang

2017-08-01

This paper proposes a method for extracting buildings from LiDAR point cloud data by combining point-based and grid-based features. To accurately discriminate buildings from vegetation, a point feature based on the variance of normal vectors is proposed. For a robust building extraction, a graph cuts algorithm is employed to combine the used features and consider the neighbor contexture information. As grid feature computing and a graph cuts algorithm are performed on a grid structure, a feature-retained DSM interpolation method is proposed in this paper. The proposed method is validated by the benchmark ISPRS Test Project on Urban Classification and 3D Building Reconstruction and compared to the state-art-of-the methods. The evaluation shows that the proposed method can obtain a promising result both at area-level and at object-level. The method is further applied to the entire ISPRS dataset and to a real dataset of the Wuhan City. The results show a completeness of 94.9% and a correctness of 92.2% at the per-area level for the former dataset and a completeness of 94.4% and a correctness of 95.8% for the latter one. The proposed method has a good potential for large-size LiDAR data.

Water Extraction in High Resolution Remote Sensing Image Based on Hierarchical Spectrum and Shape Features

International Nuclear Information System (INIS)

Li, Bangyu; Zhang, Hui; Xu, Fanjiang

2014-01-01

This paper addresses the problem of water extraction from high resolution remote sensing images (including R, G, B, and NIR channels), which draws considerable attention in recent years. Previous work on water extraction mainly faced two difficulties. 1) It is difficult to obtain accurate position of water boundary because of using low resolution images. 2) Like all other image based object classification problems, the phenomena of ''different objects same image'' or ''different images same object'' affects the water extraction. Shadow of elevated objects (e.g. buildings, bridges, towers and trees) scattered in the remote sensing image is a typical noise objects for water extraction. In many cases, it is difficult to discriminate between water and shadow in a remote sensing image, especially in the urban region. We propose a water extraction method with two hierarchies: the statistical feature of spectral characteristic based on image segmentation and the shape feature based on shadow removing. In the first hierarchy, the Statistical Region Merging (SRM) algorithm is adopted for image segmentation. The SRM includes two key steps: one is sorting adjacent regions according to a pre-ascertained sort function, and the other one is merging adjacent regions based on a pre-ascertained merging predicate. The sort step is done one time during the whole processing without considering changes caused by merging which may cause imprecise results. Therefore, we modify the SRM with dynamic sort processing, which conducts sorting step repetitively when there is large adjacent region changes after doing merging. To achieve robust segmentation, we apply the merging region with six features (four remote sensing image bands, Normalized Difference Water Index (NDWI), and Normalized Saturation-value Difference Index (NSVDI)). All these features contribute to segment image into region of object. NDWI and NSVDI are discriminate between water and

Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

Science.gov (United States)

Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

2016-01-01

Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174

Video genre classification using multimodal features

Science.gov (United States)

Jin, Sung Ho; Bae, Tae Meon; Choo, Jin Ho; Ro, Yong Man

2003-12-01

We propose a video genre classification method using multimodal features. The proposed method is applied for the preprocessing of automatic video summarization or the retrieval and classification of broadcasting video contents. Through a statistical analysis of low-level and middle-level audio-visual features in video, the proposed method can achieve good performance in classifying several broadcasting genres such as cartoon, drama, music video, news, and sports. In this paper, we adopt MPEG-7 audio-visual descriptors as multimodal features of video contents and evaluate the performance of the classification by feeding the features into a decision tree-based classifier which is trained by CART. The experimental results show that the proposed method can recognize several broadcasting video genres with a high accuracy and the classification performance with multimodal features is superior to the one with unimodal features in the genre classification.

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

Science.gov (United States)

Zhang, Ming; Xie, Fei; Zhao, Jing; Sun, Rui; Zhang, Lei; Zhang, Yue

2018-04-01

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

Enhancement and feature extraction of RS images from seismic area and seismic disaster recognition technologies

Science.gov (United States)

Zhang, Jingfa; Qin, Qiming

2003-09-01

Many types of feature extracting of RS image are analyzed, and the work procedure of pattern recognizing in RS images of seismic disaster is proposed. The aerial RS image of Tangshan Great Earthquake is processed, and the digital features of various typical seismic disaster on the RS image is calculated.

Chemical name extraction based on automatic training data generation and rich feature set.

Science.gov (United States)

Yan, Su; Spangler, W Scott; Chen, Ying

2013-01-01

The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.

Clinical data miner: an electronic case report form system with integrated data preprocessing and machine-learning libraries supporting clinical diagnostic model research.

Science.gov (United States)

Installé, Arnaud Jf; Van den Bosch, Thierry; De Moor, Bart; Timmerman, Dirk

2014-10-20

Using machine-learning techniques, clinical diagnostic model research extracts diagnostic models from patient data. Traditionally, patient data are often collected using electronic Case Report Form (eCRF) systems, while mathematical software is used for analyzing these data using machine-learning techniques. Due to the lack of integration between eCRF systems and mathematical software, extracting diagnostic models is a complex, error-prone process. Moreover, due to the complexity of this process, it is usually only performed once, after a predetermined number of data points have been collected, without insight into the predictive performance of the resulting models. The objective of the study of Clinical Data Miner (CDM) software framework is to offer an eCRF system with integrated data preprocessing and machine-learning libraries, improving efficiency of the clinical diagnostic model research workflow, and to enable optimization of patient inclusion numbers through study performance monitoring. The CDM software framework was developed using a test-driven development (TDD) approach, to ensure high software quality. Architecturally, CDM's design is split over a number of modules, to ensure future extendability. The TDD approach has enabled us to deliver high software quality. CDM's eCRF Web interface is in active use by the studies of the International Endometrial Tumor Analysis consortium, with over 4000 enrolled patients, and more studies planned. Additionally, a derived user interface has been used in six separate interrater agreement studies. CDM's integrated data preprocessing and machine-learning libraries simplify some otherwise manual and error-prone steps in the clinical diagnostic model research workflow. Furthermore, CDM's libraries provide study coordinators with a method to monitor a study's predictive performance as patient inclusions increase. To our knowledge, CDM is the only eCRF system integrating data preprocessing and machine-learning libraries

On a possible mechanism of the brain for responding to dynamical features extracted from input signals

International Nuclear Information System (INIS)

Liu Zengrong; Chen Guanrong

2003-01-01

Based on the general theory of nonlinear dynamical systems, a possible mechanism for responding to some dynamical features extracted from input signals in brain activities is described and discussed. This mechanism is first converted to a nonlinear dynamical configuration--a generalized synchronization of complex dynamical systems. Then, some general conditions for achieving such synchronizations are derived. It is shown that dynamical systems have potentials of producing different responses for different features extracted from various input signals, which may be used to describe brain activities. For illustration, some numerical examples are given with simulation figures

Detailed Hydrographic Feature Extraction from High-Resolution LiDAR Data

Energy Technology Data Exchange (ETDEWEB)

Danny L. Anderson

2012-05-01

Detailed hydrographic feature extraction from high-resolution light detection and ranging (LiDAR) data is investigated. Methods for quantitatively evaluating and comparing such extractions are presented, including the use of sinuosity and longitudinal root-mean-square-error (LRMSE). These metrics are then used to quantitatively compare stream networks in two studies. The first study examines the effect of raster cell size on watershed boundaries and stream networks delineated from LiDAR-derived digital elevation models (DEMs). The study confirmed that, with the greatly increased resolution of LiDAR data, smaller cell sizes generally yielded better stream network delineations, based on sinuosity and LRMSE. The second study demonstrates a new method of delineating a stream directly from LiDAR point clouds, without the intermediate step of deriving a DEM. Direct use of LiDAR point clouds could improve efficiency and accuracy of hydrographic feature extractions. The direct delineation method developed herein and termed “mDn”, is an extension of the D8 method that has been used for several decades with gridded raster data. The method divides the region around a starting point into sectors, using the LiDAR data points within each sector to determine an average slope, and selecting the sector with the greatest downward slope to determine the direction of flow. An mDn delineation was compared with a traditional grid-based delineation, using TauDEM, and other readily available, common stream data sets. Although, the TauDEM delineation yielded a sinuosity that more closely matches the reference, the mDn delineation yielded a sinuosity that was higher than either the TauDEM method or the existing published stream delineations. Furthermore, stream delineation using the mDn method yielded the smallest LRMSE.

Feature extraction and classification in automatic weld seam radioscopy

International Nuclear Information System (INIS)

Heindoerfer, F.; Pohle, R.

1994-01-01

The investigations conducted have shown that automatic feature extraction and classification procedures permit the identification of weld seam flaws. Within this context the favored learning fuzzy classificator represents a very good alternative to conventional classificators. The results have also made clear that improvements mainly in the field of image registration are still possible by increasing the resolution of the radioscopy system. Since, only if the flaw is segmented correctly, i.e. in its full size, and due to improved detail recognizability and sufficient contrast difference will an almost error-free classification be conceivable. (orig./MM) [de

Linear feature extraction from radar imagery: SBIR (Small Business Innovative Research), phase 2, option 2

Science.gov (United States)

Milgram, David L.; Kahn, Philip; Conner, Gary D.; Lawton, Daryl T.

1988-12-01

The goal of this effort is to develop and demonstrate prototype processing capabilities for a knowledge-based system to automatically extract and analyze features from Synthetic Aperture Radar (SAR) imagery. This effort constitutes Phase 2 funding through the Defense Small Business Innovative Research (SBIR) Program. Previous work examined the feasibility of and technology issues involved in the development of an automated linear feature extraction system. This final report documents this examination and the technologies involved in automating this image understanding task. In particular, it reports on a major software delivery containing an image processing algorithmic base, a perceptual structures manipulation package, a preliminary hypothesis management framework and an enhanced user interface.

Summary of ENDF/B pre-processing codes

International Nuclear Information System (INIS)

Cullen, D.E.

1981-12-01

This document contains the summary documentation for the ENDF/B pre-processing codes: LINEAR, RECENT, SIGMA1, GROUPIE, EVALPLOT, MERGER, DICTION, CONVERT. This summary documentation is merely a copy of the comment cards that appear at the beginning of each programme; these comment cards always reflect the latest status of input options, etc. For the latest published documentation on the methods used in these codes see UCRL-50400, Vol.17 parts A-E, Lawrence Livermore Laboratory (1979)

Wire Finishing Mill Rolling Bearing Fault Diagnosis Based on Feature Extraction and BP Neural Network

Directory of Open Access Journals (Sweden)

Hong-Yu LIU

2014-10-01

Full Text Available Rolling bearing is main part of rotary machine. It is frail section of rotary machine. Its running status affects entire mechanical equipment system performance directly. Vibration acceleration signals of the third finishing mill of Anshan Steel and Iron Group wire plant were collected in this paper. Fourier analysis, power spectrum analysis and wavelet transform were made on collected signals. Frequency domain feature extraction and wavelet transform feature extraction were made on collected signals. BP neural network fault diagnosis model was adopted. Frequency domain feature values and wavelet transform feature values were treated as neural network input values. Various typical fault models were treated as neural network output values. Corresponding relations between feature vector and fault omen were utilized. BP neural network model of typical wire plant finishing mill rolling bearing fault was constructed by training many groups sample data. After inputting sample needed to be diagnosed, wire plant finishing mill rolling bearing fault can be diagnosed. This research has important practical significance on enhancing rolling bearing fault diagnosis precision, repairing rolling bearing duly, decreasing stop time, enhancing equipment running efficiency and enhancing economic benefits.

Consistent Feature Extraction From Vector Fields: Combinatorial Representations and Analysis Under Local Reference Frames

Energy Technology Data Exchange (ETDEWEB)

Bhatia, Harsh [Univ. of Utah, Salt Lake City, UT (United States)

2015-05-01

This dissertation presents research on addressing some of the contemporary challenges in the analysis of vector fields—an important type of scientific data useful for representing a multitude of physical phenomena, such as wind flow and ocean currents. In particular, new theories and computational frameworks to enable consistent feature extraction from vector fields are presented. One of the most fundamental challenges in the analysis of vector fields is that their features are defined with respect to reference frames. Unfortunately, there is no single “correct” reference frame for analysis, and an unsuitable frame may cause features of interest to remain undetected, thus creating serious physical consequences. This work develops new reference frames that enable extraction of localized features that other techniques and frames fail to detect. As a result, these reference frames objectify the notion of “correctness” of features for certain goals by revealing the phenomena of importance from the underlying data. An important consequence of using these local frames is that the analysis of unsteady (time-varying) vector fields can be reduced to the analysis of sequences of steady (timeindependent) vector fields, which can be performed using simpler and scalable techniques that allow better data management by accessing the data on a per-time-step basis. Nevertheless, the state-of-the-art analysis of steady vector fields is not robust, as most techniques are numerical in nature. The residing numerical errors can violate consistency with the underlying theory by breaching important fundamental laws, which may lead to serious physical consequences. This dissertation considers consistency as the most fundamental characteristic of computational analysis that must always be preserved, and presents a new discrete theory that uses combinatorial representations and algorithms to provide consistency guarantees during vector field analysis along with the uncertainty

A New Method for Weak Fault Feature Extraction Based on Improved MED

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Junlin Li

2018-01-01

Full Text Available Because of the characteristics of weak signal and strong noise, the low-speed vibration signal fault feature extraction has been a hot spot and difficult problem in the field of equipment fault diagnosis. Moreover, the traditional minimum entropy deconvolution (MED method has been proved to be used to detect such fault signals. The MED uses objective function method to design the filter coefficient, and the appropriate threshold value should be set in the calculation process to achieve the optimal iteration effect. It should be pointed out that the improper setting of the threshold will cause the target function to be recalculated, and the resulting error will eventually affect the distortion of the target function in the background of strong noise. This paper presents an improved MED based method of fault feature extraction from rolling bearing vibration signals that originate in high noise environments. The method uses the shuffled frog leaping algorithm (SFLA, finds the set of optimal filter coefficients, and eventually avoids the artificial error influence of selecting threshold parameter. Therefore, the fault bearing under the two rotating speeds of 60 rpm and 70 rpm is selected for verification with typical low-speed fault bearing as the research object; the results show that SFLA-MED extracts more obvious bearings and has a higher signal-to-noise ratio than the prior MED method.

Sliding Window Based Feature Extraction and Traffic Clustering for Green Mobile Cyberphysical Systems

Directory of Open Access Journals (Sweden)

Jiao Zhang

2017-01-01

Full Text Available Both the densification of small base stations and the diversity of user activities bring huge challenges for today’s heterogeneous networks, either heavy burdens on base stations or serious energy waste. In order to ensure coverage of the network while reducing the total energy consumption, we adopt a green mobile cyberphysical system (MCPS to handle this problem. In this paper, we propose a feature extraction method using sliding window to extract the distribution feature of mobile user equipment (UE, and a case study is presented to demonstrate that the method is efficacious in reserving the clustering distribution feature. Furthermore, we present traffic clustering analysis to categorize collected traffic distribution samples into a limited set of traffic patterns, where the patterns and corresponding optimized control strategies are used to similar traffic distributions for the rapid control of base station state. Experimental results show that the sliding window is more superior in enabling higher UE coverage over the grid method. Besides, the optimized control strategy obtained from the traffic pattern is capable of achieving a high coverage that can well serve over 98% of all mobile UE for similar traffic distributions.

Parallel finite elements with domain decomposition and its pre-processing

International Nuclear Information System (INIS)

Yoshida, A.; Yagawa, G.; Hamada, S.

1993-01-01

This paper describes a parallel finite element analysis using a domain decomposition method, and the pre-processing for the parallel calculation. Computer simulations are about to replace experiments in various fields, and the scale of model to be simulated tends to be extremely large. On the other hand, computational environment has drastically changed in these years. Especially, parallel processing on massively parallel computers or computer networks is considered to be promising techniques. In order to achieve high efficiency on such parallel computation environment, large granularity of tasks, a well-balanced workload distribution are key issues. It is also important to reduce the cost of pre-processing in such parallel FEM. From the point of view, the authors developed the domain decomposition FEM with the automatic and dynamic task-allocation mechanism and the automatic mesh generation/domain subdivision system for it. (author)

Early detection of breast cancer mass lesions by mammogram segmentation images based on texture features

International Nuclear Information System (INIS)

Mahmood, F.H.

2012-01-01

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer.The calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years. This paper proposes a computer aided diagnostic system for the extraction of features like mass lesions in mammograms for early detection of breast cancer. The proposed technique is based on a four-step procedure: (a) the preprocessing of the image is done, (b) regions of interest (ROI) specification, (c) supervised segmentation method includes two to stages performed using the minimum distance (M D) criterion, and (d) feature extraction based on Gray level Co-occurrence matrices GLC M for the identification of mass lesions. The method suggested for the detection of mass lesions from mammogram image segmentation and analysis was tested over several images taken from A L-llwiya Hospital in Baghdad, Iraq.The proposed technique shows better results.

Research of building information extraction and evaluation based on high-resolution remote-sensing imagery

Science.gov (United States)

Cao, Qiong; Gu, Lingjia; Ren, Ruizhi; Wang, Lang

2016-09-01

Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection

Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface.

Science.gov (United States)

Matsubara, Takamitsu; Morimoto, Jun

2013-08-01

In this study, we propose a multiuser myoelectric interface that can easily adapt to novel users. When a user performs different motions (e.g., grasping and pinching), different electromyography (EMG) signals are measured. When different users perform the same motion (e.g., grasping), different EMG signals are also measured. Therefore, designing a myoelectric interface that can be used by multiple users to perform multiple motions is difficult. To cope with this problem, we propose for EMG signals a bilinear model that is composed of two linear factors: 1) user dependent and 2) motion dependent. By decomposing the EMG signals into these two factors, the extracted motion-dependent factors can be used as user-independent features. We can construct a motion classifier on the extracted feature space to develop the multiuser interface. For novel users, the proposed adaptation method estimates the user-dependent factor through only a few interactions. The bilinear EMG model with the estimated user-dependent factor can extract the user-independent features from the novel user data. We applied our proposed method to a recognition task of five hand gestures for robotic hand control using four-channel EMG signals measured from subject forearms. Our method resulted in 73% accuracy, which was statistically significantly different from the accuracy of standard nonmultiuser interfaces, as the result of a two-sample t -test at a significance level of 1%.

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...... with sizes dependent on the content of the image, at the location of each triangle. In this paper, we will demonstrate that by rotation of the interest regions at the triangles it is possible in grey scale images to achieve a recognition precision comparable with that of MOPS. The test of the proposed method...

AN EFFICIENT METHOD FOR AUTOMATIC ROAD EXTRACTION BASED ON MULTIPLE FEATURES FROM LiDAR DATA

Directory of Open Access Journals (Sweden)

Y. Li

2016-06-01

Full Text Available The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data directly provides three dimensional (3D points with less occlusions and smaller shadows. The elevation information and surface roughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object extraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper proposes an automatic road centerlines extraction method which has three major steps: (1 road center point detection based on multiple feature spatial clustering for separating road points from ground points, (2 local principal component analysis with least squares fitting for extracting the primitives of road centerlines, and (3 hierarchical grouping for connecting primitives into complete roads network. Compared with MTH (consist of Mean shift algorithm, Tensor voting, and Hough transform proposed in our previous article, this method greatly reduced the computational cost. To evaluate the proposed method, the Vaihingen data set, a benchmark testing data provided by ISPRS for “Urban Classification and 3D Building Reconstruction” project, was selected. The experimental results show that our method achieve the same performance by less time in road extraction using LiDAR data.

An Efficient Method for Automatic Road Extraction Based on Multiple Features from LiDAR Data

Science.gov (United States)

Li, Y.; Hu, X.; Guan, H.; Liu, P.

2016-06-01

The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data directly provides three dimensional (3D) points with less occlusions and smaller shadows. The elevation information and surface roughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object extraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper proposes an automatic road centerlines extraction method which has three major steps: (1) road center point detection based on multiple feature spatial clustering for separating road points from ground points, (2) local principal component analysis with least squares fitting for extracting the primitives of road centerlines, and (3) hierarchical grouping for connecting primitives into complete roads network. Compared with MTH (consist of Mean shift algorithm, Tensor voting, and Hough transform) proposed in our previous article, this method greatly reduced the computational cost. To evaluate the proposed method, the Vaihingen data set, a benchmark testing data provided by ISPRS for "Urban Classification and 3D Building Reconstruction" project, was selected. The experimental results show that our method achieve the same performance by less time in road extraction using LiDAR data.

A new approach to pre-processing digital image for wavelet-based watermark

Science.gov (United States)

Agreste, Santa; Andaloro, Guido

2008-11-01

The growth of the Internet has increased the phenomenon of digital piracy, in multimedia objects, like software, image, video, audio and text. Therefore it is strategic to individualize and to develop methods and numerical algorithms, which are stable and have low computational cost, that will allow us to find a solution to these problems. We describe a digital watermarking algorithm for color image protection and authenticity: robust, not blind, and wavelet-based. The use of Discrete Wavelet Transform is motivated by good time-frequency features and a good match with Human Visual System directives. These two combined elements are important for building an invisible and robust watermark. Moreover our algorithm can work with any image, thanks to the step of pre-processing of the image that includes resize techniques that adapt to the size of the original image for Wavelet transform. The watermark signal is calculated in correlation with the image features and statistic properties. In the detection step we apply a re-synchronization between the original and watermarked image according to the Neyman-Pearson statistic criterion. Experimentation on a large set of different images has been shown to be resistant against geometric, filtering, and StirMark attacks with a low rate of false alarm.

Automatic extraction of nuclei centroids of mouse embryonic cells from fluorescence microscopy images.

Directory of Open Access Journals (Sweden)

Md Khayrul Bashar

Full Text Available Accurate identification of cell nuclei and their tracking using three dimensional (3D microscopic images is a demanding task in many biological studies. Manual identification of nuclei centroids from images is an error-prone task, sometimes impossible to accomplish due to low contrast and the presence of noise. Nonetheless, only a few methods are available for 3D bioimaging applications, which sharply contrast with 2D analysis, where many methods already exist. In addition, most methods essentially adopt segmentation for which a reliable solution is still unknown, especially for 3D bio-images having juxtaposed cells. In this work, we propose a new method that can directly extract nuclei centroids from fluorescence microscopy images. This method involves three steps: (i Pre-processing, (ii Local enhancement, and (iii Centroid extraction. The first step includes two variations: first variation (Variant-1 uses the whole 3D pre-processed image, whereas the second one (Variant-2 modifies the preprocessed image to the candidate regions or the candidate hybrid image for further processing. At the second step, a multiscale cube filtering is employed in order to locally enhance the pre-processed image. Centroid extraction in the third step consists of three stages. In Stage-1, we compute a local characteristic ratio at every voxel and extract local maxima regions as candidate centroids using a ratio threshold. Stage-2 processing removes spurious centroids from Stage-1 results by analyzing shapes of intensity profiles from the enhanced image. An iterative procedure based on the nearest neighborhood principle is then proposed to combine if there are fragmented nuclei. Both qualitative and quantitative analyses on a set of 100 images of 3D mouse embryo are performed. Investigations reveal a promising achievement of the technique presented in terms of average sensitivity and precision (i.e., 88.04% and 91.30% for Variant-1; 86.19% and 95.00% for Variant-2

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.

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

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

Science.gov (United States)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Discrete pre-processing step effects in registration-based pipelines, a preliminary volumetric study on T1-weighted images.

Science.gov (United States)

Muncy, Nathan M; Hedges-Muncy, Ariana M; Kirwan, C Brock

2017-01-01

Pre-processing MRI scans prior to performing volumetric analyses is common practice in MRI studies. As pre-processing steps adjust the voxel intensities, the space in which the scan exists, and the amount of data in the scan, it is possible that the steps have an effect on the volumetric output. To date, studies have compared between and not within pipelines, and so the impact of each step is unknown. This study aims to quantify the effects of pre-processing steps on volumetric measures in T1-weighted scans within a single pipeline. It was our hypothesis that pre-processing steps would significantly impact ROI volume estimations. One hundred fifteen participants from the OASIS dataset were used, where each participant contributed three scans. All scans were then pre-processed using a step-wise pipeline. Bilateral hippocampus, putamen, and middle temporal gyrus volume estimations were assessed following each successive step, and all data were processed by the same pipeline 5 times. Repeated-measures analyses tested for a main effects of pipeline step, scan-rescan (for MRI scanner consistency) and repeated pipeline runs (for algorithmic consistency). A main effect of pipeline step was detected, and interestingly an interaction between pipeline step and ROI exists. No effect for either scan-rescan or repeated pipeline run was detected. We then supply a correction for noise in the data resulting from pre-processing.

Point features extraction: towards slam for an autonomous underwater vehicle

CSIR Research Space (South Africa)

Matsebe, O

2010-07-01

Full Text Available and Control. Available: http://www.robots.ox.ac.uk/~pnewman/papers/Robotica.pdf, date accessed: [2009, 05/20] [7] Williams, S.B., Newman, P., Rosenblatt, J., Dissanayake, G. & Whyte, H.D., Autonomous Underwater Simultaneous and Localisation and Map Building.... Available: http://www.robots.ox.ac.uk/~pnewman/papers/Robotica.pdf., date accessed: [2009, 05/20] [8]http://www.tritech.co.uk/products/products-micron_sonar.htm, date accessed: [10/01/10] [9] Tena, I., Petillot, Y., Lane, D.M.,Salson. Feature Extraction...

Extraction of features from sleep EEG for Bayesian assessment of brain development.

Directory of Open Access Journals (Sweden)

Vitaly Schetinin

Full Text Available Brain development can be evaluated by experts analysing age-related patterns in sleep electroencephalograms (EEG. Natural variations in the patterns, noise, and artefacts affect the evaluation accuracy as well as experts' agreement. The knowledge of predictive posterior distribution allows experts to estimate confidence intervals within which decisions are distributed. Bayesian approach to probabilistic inference has provided accurate estimates of intervals of interest. In this paper we propose a new feature extraction technique for Bayesian assessment and estimation of predictive distribution in a case of newborn brain development assessment. The new EEG features are verified within the Bayesian framework on a large EEG data set including 1,100 recordings made from newborns in 10 age groups. The proposed features are highly correlated with brain maturation and their use increases the assessment accuracy.

Preattentive extraction of abstract feature conjunctions from auditory stimulation as reflected by the mismatch negativity (MMN).

Science.gov (United States)

Paavilainen, P; Simola, J; Jaramillo, M; Näätänen, R; Winkler, I

2001-03-01

Brain mechanisms extracting invariant information from varying auditory inputs were studied using the mismatch-negativity (MMN) brain response. We wished to determine whether the preattentive sound-analysis mechanisms, reflected by MMN, are capable of extracting invariant relationships based on abstract conjunctions between two sound features. The standard stimuli varied over a large range in frequency and intensity dimensions following the rule that the higher the frequency, the louder the intensity. The occasional deviant stimuli violated this frequency-intensity relationship and elicited an MMN. The results demonstrate that preattentive processing of auditory stimuli extends to unexpectedly complex relationships between the stimulus features.

Linear feature extraction from radar imagery: SBIR (Small Business Innovative Research) phase 2, option 1

Science.gov (United States)

Conner, Gary D.; Milgram, David L.; Lawton, Daryl T.; McConnell, Christopher C.

1988-04-01

The goal of this effort is to develop and demonstrate prototype processing capabilities for a knowledge-based system to automatically extract and analyze linear features from synthetic aperture radar (SAR) imagery. This effort constitutes Phase 2 funding through the Defense Small Business Innovative Research (SBIR) Program. Previous work examined the feasibility of the technology issues involved in the development of an automatedlinear feature extraction system. This Option 1 Final Report documents this examination and the technologies involved in automating this image understanding task. In particular, it reports on a major software delivery containing an image processing algorithmic base, a perceptual structures manipulation package, a preliminary hypothesis management framework and an enhanced user interface.

An expert botanical feature extraction technique based on phenetic features for identifying plant species.

Directory of Open Access Journals (Sweden)

Hoshang Kolivand

Full Text Available In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost.

An expert botanical feature extraction technique based on phenetic features for identifying plant species

Science.gov (United States)

Fern, Bong Mei; Rahim, Mohd Shafry Mohd; Sulong, Ghazali; Baker, Thar; Tully, David

2018-01-01

In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost. PMID:29420568

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.

Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)

Science.gov (United States)

Iqtait, M.; Mohamad, F. S.; Mamat, M.

2018-03-01

Biometric is a pattern recognition system which is used for automatic recognition of persons based on characteristics and features of an individual. Face recognition with high recognition rate is still a challenging task and usually accomplished in three phases consisting of face detection, feature extraction, and expression classification. Precise and strong location of trait point is a complicated and difficult issue in face recognition. Cootes proposed a Multi Resolution Active Shape Models (ASM) algorithm, which could extract specified shape accurately and efficiently. Furthermore, as the improvement of ASM, Active Appearance Models algorithm (AAM) is proposed to extracts both shape and texture of specified object simultaneously. In this paper we give more details about the two algorithms and give the results of experiments, testing their performance on one dataset of faces. We found that the ASM is faster and gains more accurate trait point location than the AAM, but the AAM gains a better match to the texture.

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.

Evaluation of a Stereo Music Preprocessing Scheme for Cochlear Implant Users.

Science.gov (United States)

Buyens, Wim; van Dijk, Bas; Moonen, Marc; Wouters, Jan

2018-01-01

Although for most cochlear implant (CI) users good speech understanding is reached (at least in quiet environments), the perception and the appraisal of music are generally unsatisfactory. The improvement in music appraisal was evaluated in CI participants by using a stereo music preprocessing scheme implemented on a take-home device, in a comfortable listening environment. The preprocessing allowed adjusting the balance among vocals/bass/drums and other instruments, and was evaluated for different genres of music. The correlation between the preferred settings and the participants' speech and pitch detection performance was investigated. During the initial visit preceding the take-home test, the participants' speech-in-noise perception and pitch detection performance were measured, and a questionnaire about their music involvement was completed. The take-home device was provided, including the stereo music preprocessing scheme and seven playlists with six songs each. The participants were asked to adjust the balance by means of a turning wheel to make the music sound most enjoyable, and to repeat this three times for all songs. Twelve postlingually deafened CI users participated in the study. The data were collected by means of a take-home device, which preserved all the preferred settings for the different songs. Statistical analysis was done with a Friedman test (with post hoc Wilcoxon signed-rank test) to check the effect of "Genre." The correlations were investigated with Pearson's and Spearman's correlation coefficients. All participants preferred a balance significantly different from the original balance. Differences across participants were observed which could not be explained by perceptual abilities. An effect of "Genre" was found, showing significantly smaller preferred deviation from the original balance for Golden Oldies compared to the other genres. The stereo music preprocessing scheme showed an improvement in music appraisal with complex music and

Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal

International Nuclear Information System (INIS)

Mohebbi, Maryam; Ghassemian, Hassan

2011-01-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of stroke. Predicting the onset of paroxysmal AF (PAF), based on noninvasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize risks for the patients. In this paper, we propose an effective PAF predictor which is based on the analysis of the RR-interval signal. This method consists of three steps: preprocessing, feature extraction and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the RR-interval signal is extracted. In the next step, the recurrence plot (RP) of the RR-interval signal is obtained and five statistically significant features are extracted to characterize the basic patterns of the RP. These features consist of the recurrence rate, length of longest diagonal segments (L max  ), average length of the diagonal lines (L mean ), entropy, and trapping time. Recurrence quantification analysis can reveal subtle aspects of dynamics not easily appreciated by other methods and exhibits characteristic patterns which are caused by the typical dynamical behavior. In the final step, a support vector machine (SVM)-based classifier is used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which consists of both 30 min ECG recordings that end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, positive predictivity and negative predictivity were 97%, 100%, 100%, and 96%, respectively. The proposed methodology presents better results than other existing approaches

A study on the extraction of feature variables for the pattern recognition for welding flaws

International Nuclear Information System (INIS)

Kim, J. Y.; Kim, C. H.; Kim, B. H.

1996-01-01

In this study, the researches classifying the artificial and natural flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing, feature extraction, feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear discriminant function classifier, the empirical Bayesian classifier. Also, the pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack, lack of penetration, lack of fusion, porosity, and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately teamed the neural network classifier is better than stastical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Directory of Open Access Journals (Sweden)

Hsueh-Hsien Chang

2017-04-01

Full Text Available This paper proposes statistical feature extraction methods combined with artificial intelligence (AI approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.

Feature Extraction for Track Section Status Classification Based on UGW Signals

Directory of Open Access Journals (Sweden)

Lei Yuan

2018-04-01

Full Text Available Track status classification is essential for the stability and safety of railway operations nowadays, when railway networks are becoming more and more complex and broad. In this situation, monitoring systems are already a key element in applications dedicated to evaluating the status of a certain track section, often determining whether it is free or occupied by a train. Different technologies have already been involved in the design of monitoring systems, including ultrasonic guided waves (UGW. This work proposes the use of the UGW signals captured by a track monitoring system to extract the features that are relevant for determining the corresponding track section status. For that purpose, three features of UGW signals have been considered: the root mean square value, the energy, and the main frequency components. Experimental results successfully validated how these features can be used to classify the track section status into free, occupied and broken. Furthermore, spatial and temporal dependencies among these features were analysed in order to show how they can improve the final classification performance. Finally, a preliminary high-level classification system based on deep learning networks has been envisaged for future works.

Credible Set Estimation, Analysis, and Applications in Synthetic Aperture Radar Canonical Feature Extraction

Science.gov (United States)

2015-03-26

83 5.1 Marginal PMFs for the cylinder scene at coarse zoom. . . . . . . . . . . . . . . 85 5.2 SAR image of a Nissan Sentra with canonical...of a Nissan Sentra with canonical features extracted by the SPLIT algorithm. 5.2.4 Experiment Summary. A notional algorithm is presented in Figure 5.3

Improving Feature Representation Based on a Neural Network for Author Profiling in Social Media Texts.

Science.gov (United States)

Gómez-Adorno, Helena; Markov, Ilia; Sidorov, Grigori; Posadas-Durán, Juan-Pablo; Sanchez-Perez, Miguel A; Chanona-Hernandez, Liliana

2016-01-01

We introduce a lexical resource for preprocessing social media data. We show that a neural network-based feature representation is enhanced by using this resource. We conducted experiments on the PAN 2015 and PAN 2016 author profiling corpora and obtained better results when performing the data preprocessing using the developed lexical resource. The resource includes dictionaries of slang words, contractions, abbreviations, and emoticons commonly used in social media. Each of the dictionaries was built for the English, Spanish, Dutch, and Italian languages. The resource is freely available.

Feature Extraction in the North Sinai Desert Using Spaceborne Synthetic Aperture Radar: Potential Archaeological Applications

Directory of Open Access Journals (Sweden)

Christopher Stewart

2016-10-01

Full Text Available Techniques were implemented to extract anthropogenic features in the desert region of North Sinai using data from the first- and second-generation Phased Array type L-band Synthetic Aperture Radar (PALSAR-1 and 2. To obtain a synoptic view over the study area, a mosaic of average, multitemporal (De Grandi filtered PALSAR-1 σ° backscatter of North Sinai was produced. Two subset regions were selected for further analysis. The first included an area of abundant linear features of high relative backscatter in a strategic, but sparsely developed area between the Wadi Tumilat and Gebel Maghara. The second included an area of low backscatter anomaly features in a coastal sabkha around the archaeological sites of Tell el-Farama, Tell el-Mahzan, and Tell el-Kanais. Over the subset region between the Wadi Tumilat and Gebel Maghara, algorithms were developed to extract linear features and convert them to vector format to facilitate interpretation. The algorithms were based on mathematical morphology, but to distinguish apparent man-made features from sand dune ridges, several techniques were applied. The first technique took as input the average σ° backscatter and used a Digital Elevation Model (DEM derived Local Incidence Angle (LAI mask to exclude sand dune ridges. The second technique, which proved more effective, used the average interferometric coherence as input. Extracted features were compared with other available information layers and in some cases revealed partially buried roads. Over the coastal subset region a time series of PALSAR-2 spotlight data were processed. The coefficient of variation (CoV of De Grandi filtered imagery clearly revealed anomaly features of low CoV. These were compared with the results of an archaeological field walking survey carried out previously. The features generally correspond with isolated areas identified in the field survey as having a higher density of archaeological finds, and interpreted as possible

Summary of ENDF/B Pre-Processing Codes June 1983

International Nuclear Information System (INIS)

Cullen, D.E.

1983-06-01

This is the summary documentation for the 1983 version of the ENDF/B Pre-Processing Codes LINEAR, RECENT, SIGMA1, GROUPIE, EVALPLOT, MERGER, DICTION, COMPLOT, CONVERT. This summary documentation is merely a copy of the comment cards that appear at the beginning of each programme; these comment cards always reflect the latest status of input options, etc

Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

Science.gov (United States)

Wang, Min; Cui, Qi; Wang, Jie; Ming, Dongping; Lv, Guonian

2017-01-01

In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance.

Data-Wave-Based Features Extraction and Its Application in Symbol Identifier Recognition and Positioning Suitable for Multi-Robot Systems

Directory of Open Access Journals (Sweden)

Xilong Liu

2012-12-01

Full Text Available In this paper, feature extraction based on data-wave is proposed. The concept of data-wave is introduced to describe the rising and falling trends of the data over the long-term which are detected based on ripple and wave filters. Supported by data-wave, a novel symbol identifier with significant structure features is designed and these features are extracted by constructing pixel chains. On this basis, the corresponding recognition and positioning approach is presented. The effectiveness of the proposed approach is verified by experiments.

The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value

Directory of Open Access Journals (Sweden)

Te Han

2016-01-01

Full Text Available Nowadays, the fault diagnosis of rolling bearing in aeroengines is based on the vibration signal measured on casing, instead of bearing block. However, the vibration signal of the bearing is often covered by a series of complex components caused by other structures (rotor, gears. Therefore, when bearings cause failure, it is still not certain that the fault feature can be extracted from the vibration signal on casing. In order to solve this problem, a novel fault feature extraction method for rolling bearing based on empirical mode decomposition (EMD and the difference spectrum of singular value is proposed in this paper. Firstly, the vibration signal is decomposed by EMD. Next, the difference spectrum of singular value method is applied. The study finds that each peak on the difference spectrum corresponds to each component in the original signal. According to the peaks on the difference spectrum, the component signal of the bearing fault can be reconstructed. To validate the proposed method, the bearing fault data collected on the casing are analyzed. The results indicate that the proposed rolling bearing diagnosis method can accurately extract the fault feature that is submerged in other component signals and noise.

Three dimensional pattern recognition using feature-based indexing and rule-based search

Science.gov (United States)

Lee, Jae-Kyu

In flexible automated manufacturing, robots can perform routine operations as well as recover from atypical events, provided that process-relevant information is available to the robot controller. Real time vision is among the most versatile sensing tools, yet the reliability of machine-based scene interpretation can be questionable. The effort described here is focused on the development of machine-based vision methods to support autonomous nuclear fuel manufacturing operations in hot cells. This thesis presents a method to efficiently recognize 3D objects from 2D images based on feature-based indexing. Object recognition is the identification of correspondences between parts of a current scene and stored views of known objects, using chains of segments or indexing vectors. To create indexed object models, characteristic model image features are extracted during preprocessing. Feature vectors representing model object contours are acquired from several points of view around each object and stored. Recognition is the process of matching stored views with features or patterns detected in a test scene. Two sets of algorithms were developed, one for preprocessing and indexed database creation, and one for pattern searching and matching during recognition. At recognition time, those indexing vectors with the highest match probability are retrieved from the model image database, using a nearest neighbor search algorithm. The nearest neighbor search predicts the best possible match candidates. Extended searches are guided by a search strategy that employs knowledge-base (KB) selection criteria. The knowledge-based system simplifies the recognition process and minimizes the number of iterations and memory usage. Novel contributions include the use of a feature-based indexing data structure together with a knowledge base. Both components improve the efficiency of the recognition process by improved structuring of the database of object features and reducing data base size

Distant supervision for neural relation extraction integrated with word attention and property features.

Science.gov (United States)

Qu, Jianfeng; Ouyang, Dantong; Hua, Wen; Ye, Yuxin; Li, Ximing

2018-04-01

Distant supervision for neural relation extraction is an efficient approach to extracting massive relations with reference to plain texts. However, the existing neural methods fail to capture the critical words in sentence encoding and meanwhile lack useful sentence information for some positive training instances. To address the above issues, we propose a novel neural relation extraction model. First, we develop a word-level attention mechanism to distinguish the importance of each individual word in a sentence, increasing the attention weights for those critical words. Second, we investigate the semantic information from word embeddings of target entities, which can be developed as a supplementary feature for the extractor. Experimental results show that our model outperforms previous state-of-the-art baselines. Copyright © 2018 Elsevier Ltd. All rights reserved.

An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

Directory of Open Access Journals (Sweden)

Muhammad Ali

2017-11-01

Full Text Available Current transformer (CT saturation is one of the significant problems for protection engineers. If CT saturation is not tackled properly, it can cause a disastrous effect on the stability of the power system, and may even create a complete blackout. To cope with CT saturation properly, an accurate detection or classification should be preceded. Recently, deep learning (DL methods have brought a subversive revolution in the field of artificial intelligence (AI. This paper presents a new DL classification method based on unsupervised feature extraction and supervised fine-tuning strategy to classify the saturated and unsaturated regions in case of CT saturation. In other words, if protection system is subjected to a CT saturation, proposed method will correctly classify the different levels of saturation with a high accuracy. Traditional AI methods are mostly based on supervised learning and rely heavily on human crafted features. This paper contributes to an unsupervised feature extraction, using autoencoders and deep neural networks (DNNs to extract features automatically without prior knowledge of optimal features. To validate the effectiveness of proposed method, a variety of simulation tests are conducted, and classification results are analyzed using standard classification metrics. Simulation results confirm that proposed method classifies the different levels of CT saturation with a remarkable accuracy and has unique feature extraction capabilities. Lastly, we provided a potential future research direction to conclude this paper.

Discriminative kernel feature extraction and learning for object recognition and detection

DEFF Research Database (Denmark)

Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

2015-01-01

Feature extraction and learning is critical for object recognition and detection. By embedding context cue of image attributes into the kernel descriptors, we propose a set of novel kernel descriptors called context kernel descriptors (CKD). The motivation of CKD is to use the spatial consistency...... even in high-dimensional space. In addition, the latent connection between Rényi quadratic entropy and the mapping data in kernel feature space further facilitates us to capture the geometric structure as well as the information about the underlying labels of the CKD using CSQMI. Thus the resulting...... codebook and reduced CKD are discriminative. We report superior performance of our algorithm for object recognition on benchmark datasets like Caltech-101 and CIFAR-10, as well as for detection on a challenging chicken feet dataset....

Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

Science.gov (United States)

Hortos, William S.

2008-04-01

Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at

Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance.

Science.gov (United States)

Poplová, Michaela; Sovka, Pavel; Cifra, Michal

2017-01-01

Photonic signals are broadly exploited in communication and sensing and they typically exhibit Poisson-like statistics. In a common scenario where the intensity of the photonic signals is low and one needs to remove a nonstationary trend of the signals for any further analysis, one faces an obstacle: due to the dependence between the mean and variance typical for a Poisson-like process, information about the trend remains in the variance even after the trend has been subtracted, possibly yielding artifactual results in further analyses. Commonly available detrending or normalizing methods cannot cope with this issue. To alleviate this issue we developed a suitable pre-processing method for the signals that originate from a Poisson-like process. In this paper, a Poisson pre-processing method for nonstationary time series with Poisson distribution is developed and tested on computer-generated model data and experimental data of chemiluminescence from human neutrophils and mung seeds. The presented method transforms a nonstationary Poisson signal into a stationary signal with a Poisson distribution while preserving the type of photocount distribution and phase-space structure of the signal. The importance of the suggested pre-processing method is shown in Fano factor and Hurst exponent analysis of both computer-generated model signals and experimental photonic signals. It is demonstrated that our pre-processing method is superior to standard detrending-based methods whenever further signal analysis is sensitive to variance of the signal.

Pre-processing data using wavelet transform and PCA based on ...

Indian Academy of Sciences (India)

Abazar Solgi

2017-07-14

Jul 14, 2017 ... Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation. Abazar Solgi1,*, Amir Pourhaghi1, Ramin Bahmani2 and Heidar Zarei3. 1. Department of Water Resources Engineering, Shahid Chamran University of ...

A comparative study of image low level feature extraction algorithms

Directory of Open Access Journals (Sweden)

M.M. El-gayar

2013-07-01

Full Text Available Feature extraction and matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods for assessing the performance of popular image matching algorithms are presented and rely on costly descriptors for detection and matching. Specifically, the method assesses the type of images under which each of the algorithms reviewed herein perform to its maximum or highest efficiency. The efficiency is measured in terms of the number of matches founds by the algorithm and the number of type I and type II errors encountered when the algorithm is tested against a specific pair of images. Current comparative studies asses the performance of the algorithms based on the results obtained in different criteria such as speed, sensitivity, occlusion, and others. This study addresses the limitations of the existing comparative tools and delivers a generalized criterion to determine beforehand the level of efficiency expected from a matching algorithm given the type of images evaluated. The algorithms and the respective images used within this work are divided into two groups: feature-based and texture-based. And from this broad classification only three of the most widely used algorithms are assessed: color histogram, FAST (Features from Accelerated Segment Test, SIFT (Scale Invariant Feature Transform, PCA-SIFT (Principal Component Analysis-SIFT, F-SIFT (fast-SIFT and SURF (speeded up robust features. The performance of the Fast-SIFT (F-SIFT feature detection methods are compared for scale changes, rotation, blur, illumination changes and affine transformations. All the experiments use repeatability measurement and the number of correct matches for the evaluation measurements. SIFT presents its stability in most situations although its slow. F-SIFT is the fastest one with good performance as the same as SURF, SIFT, PCA-SIFT show its advantages in rotation and illumination changes.

Deep PDF parsing to extract features for detecting embedded malware.

Energy Technology Data Exchange (ETDEWEB)

Munson, Miles Arthur; Cross, Jesse S. (Missouri University of Science and Technology, Rolla, MO)

2011-09-01

The number of PDF files with embedded malicious code has risen significantly in the past few years. This is due to the portability of the file format, the ways Adobe Reader recovers from corrupt PDF files, the addition of many multimedia and scripting extensions to the file format, and many format properties the malware author may use to disguise the presence of malware. Current research focuses on executable, MS Office, and HTML formats. In this paper, several features and properties of PDF Files are identified. Features are extracted using an instrumented open source PDF viewer. The feature descriptions of benign and malicious PDFs can be used to construct a machine learning model for detecting possible malware in future PDF files. The detection rate of PDF malware by current antivirus software is very low. A PDF file is easy to edit and manipulate because it is a text format, providing a low barrier to malware authors. Analyzing PDF files for malware is nonetheless difficult because of (a) the complexity of the formatting language, (b) the parsing idiosyncrasies in Adobe Reader, and (c) undocumented correction techniques employed in Adobe Reader. In May 2011, Esparza demonstrated that PDF malware could be hidden from 42 of 43 antivirus packages by combining multiple obfuscation techniques [4]. One reason current antivirus software fails is the ease of varying byte sequences in PDF malware, thereby rendering conventional signature-based virus detection useless. The compression and encryption functions produce sequences of bytes that are each functions of multiple input bytes. As a result, padding the malware payload with some whitespace before compression/encryption can change many of the bytes in the final payload. In this study we analyzed a corpus of 2591 benign and 87 malicious PDF files. While this corpus is admittedly small, it allowed us to test a system for collecting indicators of embedded PDF malware. We will call these indicators features throughout

Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

Directory of Open Access Journals (Sweden)

Pradipta Maji

Full Text Available Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

Science.gov (United States)

Sharma, Sanjib; Siddique, Ridwan; Reed, Seann; Ahnert, Peter; Mendoza, Pablo; Mejia, Alfonso

2018-03-01

The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1-7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised of the following components: (i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); (ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); (iii) NOAA's Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); (iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; (v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and (vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the US Middle Atlantic region, ranging in size from 381 to 12 362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (> 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble streamflow forecasts, particularly in the cool season, but QR outperforms ARX(1,1). The scenarios that implement preprocessing and postprocessing separately tend to perform similarly, although the postprocessing-alone scenario is often more effective. The scenario involving both preprocessing and postprocessing consistently outperforms the other scenarios. In some cases

Preprocessing for Optimization of Probabilistic-Logic Models for Sequence Analysis

DEFF Research Database (Denmark)

Christiansen, Henning; Lassen, Ole Torp

2009-01-01

and approximation are needed. The first steps are taken towards a methodology for optimizing such models by approximations using auxiliary models for preprocessing or splitting them into submodels. Evaluation of such approximating models is challenging as authoritative test data may be sparse. On the other hand...

Visual feature extraction and establishment of visual tags in the intelligent visual internet of things

Science.gov (United States)

Zhao, Yiqun; Wang, Zhihui

2015-12-01

The Internet of things (IOT) is a kind of intelligent networks which can be used to locate, track, identify and supervise people and objects. One of important core technologies of intelligent visual internet of things ( IVIOT) is the intelligent visual tag system. In this paper, a research is done into visual feature extraction and establishment of visual tags of the human face based on ORL face database. Firstly, we use the principal component analysis (PCA) algorithm for face feature extraction, then adopt the support vector machine (SVM) for classifying and face recognition, finally establish a visual tag for face which is already classified. We conducted a experiment focused on a group of people face images, the result show that the proposed algorithm have good performance, and can show the visual tag of objects conveniently.

General tensor discriminant analysis and gabor features for gait recognition.

Science.gov (United States)

Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

2007-10-01

The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine

Descending necrotizing mediastinitis following dental extraction. Radiological features and surgical treatment considerations.

Science.gov (United States)

González-García, Raúl; Risco-Rojas, Roberto; Román-Romero, Leticia; Moreno-García, Carlos; López García, Cipriano

2011-07-01

Descending necrotizing mediastinitis (DNM) following dental extraction is an extremely serious infection with a high mortality rate. Oral infection may rapidly descend into the mediastinum across the retropharyngeal and retrovisceral spaces. Once established, mediastinitis is rapidly followed by sepsis and death. If DNM is suspected cervical and thoracic CT must be carried out urgently. After this, prompt control of the upper airway with tracheostomy, aggressive surgical debridement of the deep cervical spaces and mediastinum, and intravenous broad spectrum antibiotic therapy are mandatory. The present paper reports two new cases of DNM following dental extraction, and focuses on radiological features of abscess progression through the cervical spaces down into the mediastinum. 2010 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Development of feature extraction analysis for a multi-functional optical profiling device applied to field engineering applications

Science.gov (United States)

Han, Xu; Xie, Guangping; Laflen, Brandon; Jia, Ming; Song, Guiju; Harding, Kevin G.

2015-05-01

In the real application environment of field engineering, a large variety of metrology tools are required by the technician to inspect part profile features. However, some of these tools are burdensome and only address a sole application or measurement. In other cases, standard tools lack the capability of accessing irregular profile features. Customers of field engineering want the next generation metrology devices to have the ability to replace the many current tools with one single device. This paper will describe a method based on the ring optical gage concept to the measurement of numerous kinds of profile features useful for the field technician. The ring optical system is composed of a collimated laser, a conical mirror and a CCD camera. To be useful for a wide range of applications, the ring optical system requires profile feature extraction algorithms and data manipulation directed toward real world applications in field operation. The paper will discuss such practical applications as measuring the non-ideal round hole with both off-centered and oblique axes. The algorithms needed to analyze other features such as measuring the width of gaps, radius of transition fillets, fall of step surfaces, and surface parallelism will also be discussed in this paper. With the assistance of image processing and geometric algorithms, these features can be extracted with a reasonable performance. Tailoring the feature extraction analysis to this specific gage offers the potential for a wider application base beyond simple inner diameter measurements. The paper will present experimental results that are compared with standard gages to prove the performance and feasibility of the analysis in real world field engineering. Potential accuracy improvement methods, a new dual ring design and future work will be discussed at the end of this paper.

A Novel Ship Detection Method Based on Gradient and Integral Feature for Single-Polarization Synthetic Aperture Radar Imagery

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Hao Shi

2018-02-01

Full Text Available With the rapid development of remote sensing technologies, SAR satellites like China’s Gaofen-3 satellite have more imaging modes and higher resolution. With the availability of high-resolution SAR images, automatic ship target detection has become an important topic in maritime research. In this paper, a novel ship detection method based on gradient and integral features is proposed. This method is mainly composed of three steps. First, in the preprocessing step, a filter is employed to smooth the clutters and the smoothing effect can be adaptive adjusted according to the statistics information of the sub-window. Thus, it can retain details while achieving noise suppression. Second, in the candidate area extraction, a sea-land segmentation method based on gradient enhancement is presented. The integral image method is employed to accelerate computation. Finally, in the ship target identification step, a feature extraction strategy based on Haar-like gradient information and a Radon transform is proposed. This strategy decreases the number of templates found in traditional Haar-like methods. Experiments were performed using Gaofen-3 single-polarization SAR images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency. In addition, this method has the potential for on-board processing.

Efficient ConvNet Feature Extraction with Multiple RoI Pooling for Landmark-Based Visual Localization of Autonomous Vehicles

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Yi Hou

2017-01-01

Full Text Available Efficient and robust visual localization is important for autonomous vehicles. By achieving impressive localization accuracy under conditions of significant changes, ConvNet landmark-based approach has attracted the attention of people in several research communities including autonomous vehicles. Such an approach relies heavily on the outstanding discrimination power of ConvNet features to match detected landmarks between images. However, a major challenge of this approach is how to extract discriminative ConvNet features efficiently. To address this challenging, inspired by the high efficiency of the region of interest (RoI pooling layer, we propose a Multiple RoI (MRoI pooling technique, an enhancement of RoI, and a simple yet efficient ConvNet feature extraction method. Our idea is to leverage MRoI pooling to exploit multilevel and multiresolution information from multiple convolutional layers and then fuse them to improve the discrimination capacity of the final ConvNet features. The main advantages of our method are (a high computational efficiency for real-time applications; (b GPU memory efficiency for mobile applications; and (c use of pretrained model without fine-tuning or retraining for easy implementation. Experimental results on four datasets have demonstrated not only the above advantages but also the high discriminating power of the extracted ConvNet features with state-of-the-art localization accuracy.

Urban Area Extent Extraction in Spaceborne HR and VHR Data Using Multi-Resolution Features

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Gianni Cristian Iannelli

2014-09-01

Full Text Available Detection of urban area extents by means of remotely sensed data is a difficult task, especially because of the multiple, diverse definitions of what an “urban area” is. The models of urban areas listed in technical literature are based on the combination of spectral information with spatial patterns, possibly at different spatial resolutions. Starting from the same data set, “urban area” extraction may thus lead to multiple outputs. If this is done in a well-structured framework, however, this may be considered as an advantage rather than an issue. This paper proposes a novel framework for urban area extent extraction from multispectral Earth Observation (EO data. The key is to compute and combine spectral and multi-scale spatial features. By selecting the most adequate features, and combining them with proper logical rules, the approach allows matching multiple urban area models. Experimental results for different locations in Brazil and Kenya using High-Resolution (HR data prove the usefulness and flexibility of the framework.

Autonomous celestial navigation based on Earth ultraviolet radiance and fast gradient statistic feature extraction

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Lu, Shan; Zhang, Hanmo

2016-01-01

To meet the requirement of autonomous orbit determination, this paper proposes a fast curve fitting method based on earth ultraviolet features to obtain accurate earth vector direction, in order to achieve the high precision autonomous navigation. Firstly, combining the stable characters of earth ultraviolet radiance and the use of transmission model software of atmospheric radiation, the paper simulates earth ultraviolet radiation model on different time and chooses the proper observation band. Then the fast improved edge extracting method combined Sobel operator and local binary pattern (LBP) is utilized, which can both eliminate noises efficiently and extract earth ultraviolet limb features accurately. And earth's centroid locations on simulated images are estimated via the least square fitting method using part of the limb edges. Taken advantage of the estimated earth vector direction and earth distance, Extended Kalman Filter (EKF) is applied to realize the autonomous navigation finally. Experiment results indicate the proposed method can achieve a sub-pixel earth centroid location estimation and extremely enhance autonomous celestial navigation precision.

A Biologically Inspired Approach to Frequency Domain Feature Extraction for EEG Classification

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Nurhan Gursel Ozmen

2018-01-01

Full Text Available Classification of electroencephalogram (EEG signal is important in mental decoding for brain-computer interfaces (BCI. We introduced a feature extraction approach based on frequency domain analysis to improve the classification performance on different mental tasks using single-channel EEG. This biologically inspired method extracts the most discriminative spectral features from power spectral densities (PSDs of the EEG signals. We applied our method on a dataset of six subjects who performed five different imagination tasks: (i resting state, (ii mental arithmetic, (iii imagination of left hand movement, (iv imagination of right hand movement, and (v imagination of letter “A.” Pairwise and multiclass classifications were performed in single EEG channel using Linear Discriminant Analysis and Support Vector Machines. Our method produced results (mean classification accuracy of 83.06% for binary classification and 91.85% for multiclassification that are on par with the state-of-the-art methods, using single-channel EEG with low computational cost. Among all task pairs, mental arithmetic versus letter imagination yielded the best result (mean classification accuracy of 90.29%, indicating that this task pair could be the most suitable pair for a binary class BCI. This study contributes to the development of single-channel BCI, as well as finding the best task pair for user defined applications.

Thresholding: A Pixel-Level Image Processing Methodology Preprocessing Technique for an OCR System for the Brahmi Script

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H. K. Anasuya Devi

2006-12-01

Full Text Available In this paper we study the methodology employed for preprocessing the archaeological images. We present the various algorithms used in the low-level processing stage of image analysis for Optical Character Recognition System for Brahmi Script. The image preprocessing technique covered in this paper is thresholding. We also try to analyze the results obtained by the pixel-level processing algorithms.

High Resolution SAR Imaging Employing Geometric Features for Extracting Seismic Damage of Buildings

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Cui, L. P.; Wang, X. P.; Dou, A. X.; Ding, X.

2018-04-01

Synthetic Aperture Radar (SAR) image is relatively easy to acquire but difficult for interpretation. This paper probes how to identify seismic damage of building using geometric features of SAR. The SAR imaging geometric features of buildings, such as the high intensity layover, bright line induced by double bounce backscattering and dark shadow is analysed, and show obvious differences texture features of homogeneity, similarity and entropy in combinatorial imaging geometric regions between the un-collapsed and collapsed buildings in airborne SAR images acquired in Yushu city damaged by 2010 Ms7.1 Yushu, Qinghai, China earthquake, which implicates a potential capability to discriminate collapsed and un-collapsed buildings from SAR image. Study also shows that the proportion of highlight (layover & bright line) area (HA) is related to the seismic damage degree, thus a SAR image damage index (SARDI), which related to the ratio of HA to the building occupation are of building in a street block (SA), is proposed. While HA is identified through feature extraction with high-pass and low-pass filtering of SAR image in frequency domain. A partial region with 58 natural street blocks in the Yushu City are selected as study area. Then according to the above method, HA is extracted, SARDI is then calculated and further classified into 3 classes. The results show effective through validation check with seismic damage classes interpreted artificially from post-earthquake airborne high resolution optical image, which shows total classification accuracy 89.3 %, Kappa coefficient 0.79 and identical to the practical seismic damage distribution. The results are also compared and discussed with the building damage identified from SAR image available by other authors.

Automated Solar Flare Detection and Feature Extraction in High-Resolution and Full-Disk Hα Images

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Yang, Meng; Tian, Yu; Liu, Yangyi; Rao, Changhui

2018-05-01

In this article, an automated solar flare detection method applied to both full-disk and local high-resolution Hα images is proposed. An adaptive gray threshold and an area threshold are used to segment the flare region. Features of each detected flare event are extracted, e.g. the start, peak, and end time, the importance class, and the brightness class. Experimental results have verified that the proposed method can obtain more stable and accurate segmentation results than previous works on full-disk images from Big Bear Solar Observatory (BBSO) and Kanzelhöhe Observatory for Solar and Environmental Research (KSO), and satisfying segmentation results on high-resolution images from the Goode Solar Telescope (GST). Moreover, the extracted flare features correlate well with the data given by KSO. The method may be able to implement a more complicated statistical analysis of Hα solar flares.

Comparison of planar images and SPECT with bayesean preprocessing for the demonstration of facial anatomy and craniomandibular disorders

International Nuclear Information System (INIS)

Kircos, L.T.; Ortendahl, D.A.; Hattner, R.S.; Faulkner, D.; Taylor, R.L.

1984-01-01

Craniomandiublar disorders involving the facial anatomy may be difficult to demonstrate in planar images. Although bone scanning is generally more sensitive than radiography, facial bone anatomy is complex and focal areas of increased or decreased radiotracer may become obscured by overlapping structures in planar images. Thus SPECT appears ideally suited to examination of the facial skeleton. A series of patients with craniomandibular disorders of unknown origin were imaged using 20 mCi Tc-99m MDP. Planar and SPECT (Siemens 7500 ZLC Orbiter) images were obtained four hours after injection. The SPECT images were reconstructed with a filtered back-projection algorithm. In order to improve image contrast and resolution in SPECT images, the rotation views were pre-processed with a Bayesean deblurring algorithm which has previously been show to offer improved contrast and resolution in planar images. SPECT images using the pre-processed rotation views were obtained and compared to the SPECT images without pre-processing and the planar images. TMJ arthropathy involving either the glenoid fossa or the mandibular condyle, orthopedic changes involving the mandible or maxilla, localized dental pathosis, as well as changes in structures peripheral to the facial skeleton were identified. Bayesean pre-processed SPECT depicted the facial skeleton more clearly as well as providing a more obvious demonstration of the bony changes associated with craniomandibular disorders than either planar images or SPECT without pre-processing

Integrating angle-frequency domain synchronous averaging technique with feature extraction for gear fault diagnosis

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Zhang, Shengli; Tang, J.

2018-01-01

Gear fault diagnosis relies heavily on the scrutiny of vibration responses measured. In reality, gear vibration signals are noisy and dominated by meshing frequencies as well as their harmonics, which oftentimes overlay the fault related components. Moreover, many gear transmission systems, e.g., those in wind turbines, constantly operate under non-stationary conditions. To reduce the influences of non-synchronous components and noise, a fault signature enhancement method that is built upon angle-frequency domain synchronous averaging is developed in this paper. Instead of being averaged in the time domain, the signals are processed in the angle-frequency domain to solve the issue of phase shifts between signal segments due to uncertainties caused by clearances, input disturbances, and sampling errors, etc. The enhanced results are then analyzed through feature extraction algorithms to identify the most distinct features for fault classification and identification. Specifically, Kernel Principal Component Analysis (KPCA) targeting at nonlinearity, Multilinear Principal Component Analysis (MPCA) targeting at high dimensionality, and Locally Linear Embedding (LLE) targeting at local similarity among the enhanced data are employed and compared to yield insights. Numerical and experimental investigations are performed, and the results reveal the effectiveness of angle-frequency domain synchronous averaging in enabling feature extraction and classification.

Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III.

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Boon, K H; Khalil-Hani, M; Malarvili, M B

2018-01-01

This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity. Copyright © 2017 Elsevier B.V. All rights reserved.

Predictive analysis of the influence of the chemical composition and pre-processing regimen on structural properties of steel alloys using machine learning techniques

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Krishnamurthy, Narayanan; Maddali, Siddharth; Romanov, Vyacheslav; Hawk, Jeffrey

We present some structural properties of multi-component steel alloys as predicted by a random forest machine-learning model. These non-parametric models are trained on high-dimensional data sets defined by features such as chemical composition, pre-processing temperatures and environmental influences, the latter of which are based upon standardized testing procedures for tensile, creep and rupture properties as defined by the American Society of Testing and Materials (ASTM). We quantify the goodness of fit of these models as well as the inferred relative importance of each of these features, all with a conveniently defined metric and scale. The models are tested with synthetic data points, generated subject to the appropriate mathematical constraints for the various features. By this we highlight possible trends in the increase or degradation of the structural properties with perturbations in the features of importance. This work is presented as part of the Data Science Initiative at the National Energy Technology Laboratory, directed specifically towards the computational design of steel alloys.

Figure text extraction in biomedical literature.

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Daehyun Kim

2011-01-01

Full Text Available Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engine (http://figuresearch.askHERMES.org to allow bioscientists to access figures efficiently. Since text frequently appears in figures, automatically extracting such text may assist the task of mining information from figures. Little research, however, has been conducted exploring text extraction from biomedical figures.We first evaluated an off-the-shelf Optical Character Recognition (OCR tool on its ability to extract text from figures appearing in biomedical full-text articles. We then developed a Figure Text Extraction Tool (FigTExT to improve the performance of the OCR tool for figure text extraction through the use of three innovative components: image preprocessing, character recognition, and text correction. We first developed image preprocessing to enhance image quality and to improve text localization. Then we adapted the off-the-shelf OCR tool on the improved text localization for character recognition. Finally, we developed and evaluated a novel text correction framework by taking advantage of figure-specific lexicons.The evaluation on 382 figures (9,643 figure texts in total randomly selected from PubMed Central full-text articles shows that FigTExT performed with 84% precision, 98% recall, and 90% F1-score for text localization and with 62.5% precision, 51.0% recall and 56.2% F1-score for figure text extraction. When limiting figure texts to those judged by domain experts to be important content, FigTExT performed with 87.3% precision, 68.8% recall, and 77% F1-score. FigTExT significantly improved the performance of the off-the-shelf OCR tool we used, which on its own performed with 36.6% precision, 19.3% recall, and 25.3% F1-score for

Validation of DWI pre-processing procedures for reliable differentiation between human brain gliomas.

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Vellmer, Sebastian; Tonoyan, Aram S; Suter, Dieter; Pronin, Igor N; Maximov, Ivan I

2018-02-01

Diffusion magnetic resonance imaging (dMRI) is a powerful tool in clinical applications, in particular, in oncology screening. dMRI demonstrated its benefit and efficiency in the localisation and detection of different types of human brain tumours. Clinical dMRI data suffer from multiple artefacts such as motion and eddy-current distortions, contamination by noise, outliers etc. In order to increase the image quality of the derived diffusion scalar metrics and the accuracy of the subsequent data analysis, various pre-processing approaches are actively developed and used. In the present work we assess the effect of different pre-processing procedures such as a noise correction, different smoothing algorithms and spatial interpolation of raw diffusion data, with respect to the accuracy of brain glioma differentiation. As a set of sensitive biomarkers of the glioma malignancy grades we chose the derived scalar metrics from diffusion and kurtosis tensor imaging as well as the neurite orientation dispersion and density imaging (NODDI) biophysical model. Our results show that the application of noise correction, anisotropic diffusion filtering, and cubic-order spline interpolation resulted in the highest sensitivity and specificity for glioma malignancy grading. Thus, these pre-processing steps are recommended for the statistical analysis in brain tumour studies. Copyright © 2017. Published by Elsevier GmbH.