Comparison of crisp and fuzzy character networks in handwritten word recognition

Science.gov (United States)

Gader, Paul; Mohamed, Magdi; Chiang, Jung-Hsien

1992-01-01

Experiments involving handwritten word recognition on words taken from images of handwritten address blocks from the United States Postal Service mailstream are described. The word recognition algorithm relies on the use of neural networks at the character level. The neural networks are trained using crisp and fuzzy desired outputs. The fuzzy outputs were defined using a fuzzy k-nearest neighbor algorithm. The crisp networks slightly outperformed the fuzzy networks at the character level but the fuzzy networks outperformed the crisp networks at the word level.

IMPROVEMENT IN HANDWRITTEN NUMERAL STRING RECOGNITION BY SLANT NORMALIZATION AND CONTEXTUAL INFORMATION

NARCIS (Netherlands)

Britto jr., A. de S.; Sabourin, R.; Lethelier, E.; Bortolozzi, F.; Suen, C.Y.

2004-01-01

This work describes a way of enhancing handwritten numeral string recognition by considering slant normalization and contextual information to train an implicit segmentation­based system. A word slant normalization method is modified in order to improve the results for handwritten numeral strings.

Contribution to automatic handwritten characters recognition. Application to optical moving characters recognition

International Nuclear Information System (INIS)

Gokana, Denis

1986-01-01

This paper describes a research work on computer aided vision relating to the design of a vision system which can recognize isolated handwritten characters written on a mobile support. We use a technique which consists in analyzing information contained in the contours of the polygon circumscribed to the character's shape. These contours are segmented and labelled to give a new set of features constituted by: - right and left 'profiles', - topological and algebraic unvarying properties. A new method of character's recognition induced from this representation based on a multilevel hierarchical technique is then described. In the primary level, we use a fuzzy classification with dynamic programming technique using 'profiles'. The other levels adjust the recognition by using topological and algebraic unvarying properties. Several results are presented and an accuracy of 99 pc was reached for handwritten numeral characters, thereby attesting the robustness of our algorithm. (author) [fr

Recognition of handwritten characters using local gradient feature descriptors

NARCIS (Netherlands)

Surinta, Olarik; Karaaba, Mahir F.; Schomaker, Lambert R.B.; Wiering, Marco A.

2015-01-01

Abstract In this paper we propose to use local gradient feature descriptors, namely the scale invariant feature transform keypoint descriptor and the histogram of oriented gradients, for handwritten character recognition. The local gradient feature descriptors are used to extract feature vectors

A NEW APPROACH TO SEGMENT HANDWRITTEN DIGITS

NARCIS (Netherlands)

Oliveira, L.S.; Lethelier, E.; Bortolozzi, F.; Sabourin, R.

2004-01-01

This article presents a new segmentation approach applied to unconstrained handwritten digits. The novelty of the proposed algorithm is based on the combination of two types of structural features in order to provide the best segmentation path between connected entities. In this article, we first

ADAPTIVE CONTEXT PROCESSING IN ON-LINE HANDWRITTEN CHARACTER RECOGNITION

NARCIS (Netherlands)

Iwayama, N.; Ishigaki, K.

2004-01-01

We propose a new approach to context processing in on-line handwritten character recognition (OLCR). Based on the observation that writers often repeat the strings that they input, we take the approach of adaptive context processing. (ACP). In ACP, the strings input by a writer are automatically

New Approach of Feature Extraction Method Based on the Raw Form and his Skeleton for Gujarati Handwritten Digits using Neural Networks Classifier

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K. Moro

2014-12-01

Full Text Available This paper presents an optical character recognition (OCR system for Gujarati handwritten digits. One may find so much of work for latin writing, arabic, chines, etc. but Gujarati is a language for which hardly any work is traceable especially for handwritten characters. Here in this work we have proposed a method of feature extraction based on the raw form of the character and his skeleton and we have shown the advantage of using this method over other approaches mentioned in this article.

Evaluating structural pattern recognition for handwritten math via primitive label graphs

Science.gov (United States)

Zanibbi, Richard; Mouchère, Harold; Viard-Gaudin, Christian

2013-01-01

Currently, structural pattern recognizer evaluations compare graphs of detected structure to target structures (i.e. ground truth) using recognition rates, recall and precision for object segmentation, classification and relationships. In document recognition, these target objects (e.g. symbols) are frequently comprised of multiple primitives (e.g. connected components, or strokes for online handwritten data), but current metrics do not characterize errors at the primitive level, from which object-level structure is obtained. Primitive label graphs are directed graphs defined over primitives and primitive pairs. We define new metrics obtained by Hamming distances over label graphs, which allow classification, segmentation and parsing errors to be characterized separately, or using a single measure. Recall and precision for detected objects may also be computed directly from label graphs. We illustrate the new metrics by comparing a new primitive-level evaluation to the symbol-level evaluation performed for the CROHME 2012 handwritten math recognition competition. A Python-based set of utilities for evaluating, visualizing and translating label graphs is publicly available.

A GRU-based Encoder-Decoder Approach with Attention for Online Handwritten Mathematical Expression Recognition

OpenAIRE

Zhang, Jianshu; Du, Jun; Dai, Lirong

2017-01-01

In this study, we present a novel end-to-end approach based on the encoder-decoder framework with the attention mechanism for online handwritten mathematical expression recognition (OHMER). First, the input two-dimensional ink trajectory information of handwritten expression is encoded via the gated recurrent unit based recurrent neural network (GRU-RNN). Then the decoder is also implemented by the GRU-RNN with a coverage-based attention model. The proposed approach can simultaneously accompl...

Development of an optical character recognition pipeline for handwritten form fields from an electronic health record.

Science.gov (United States)

Rasmussen, Luke V; Peissig, Peggy L; McCarty, Catherine A; Starren, Justin

2012-06-01

Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms. We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms. The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%. While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline.

Recognition of Handwritten Arabic words using a neuro-fuzzy network

International Nuclear Information System (INIS)

Boukharouba, Abdelhak; Bennia, Abdelhak

2008-01-01

We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system.The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database

Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods

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Gregorius Satia Budhi

2015-07-01

Full Text Available Javanese characters are traditional characters that are used to write the Javanese language. The Javanese language is a language used by many people on the island of Java, Indonesia. The use of Javanese characters is diminishing more and more because of the difficulty of studying the Javanese characters themselves. The Javanese character set consists of basic characters, numbers, complementary characters, and so on. In this research we have developed a system to recognize Javanese characters. Input for the system is a digital image containing several handwritten Javanese characters. Preprocessing and segmentation are performed on the input image to get each character. For each character, feature extraction is done using the ICZ-ZCZ method. The output from feature extraction will become input for an artificial neural network. We used several artificial neural networks, namely a bidirectional associative memory network, a counterpropagation network, an evolutionary network, a backpropagation network, and a backpropagation network combined with chi2. From the experimental results it can be seen that the combination of chi2 and backpropagation achieved better recognition accuracy than the other methods.

Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language

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Youssef Boulid

2017-08-01

Full Text Available A good Arabic handwritten recognition system must consider the characteristics of Arabic letters which can be explicit such as the presence of diacritics or implicit such as the baseline information (a virtual line on which cursive text are aligned and/join. In order to find an adequate method of features extraction, we have taken into consideration the nature of the Arabic characters. The paper investigate two methods based on two different visions: one describes the image in terms of the distribution of pixels, and the other describes it in terms of local patterns. Spatial Distribution of Pixels (SDP is used according to the first vision; whereas Local Binary Patterns (LBP are used for the second one. Tested on the Arabic portion of the Isolated Farsi Handwritten Character Database (IFHCDB and using neural networks as a classifier, SDP achieve a recognition rate around 94% while LBP achieve a recognition rate of about 96%.

A study of symbol segmentation method for handwritten mathematical formula recognition using mathematical structure information

OpenAIRE

Toyozumi, Kenichi; Yamada, Naoya; Kitasaka, Takayuki; Mori, Kensaku; Suenaga, Yasuhito; Mase, Kenji; Takahashi, Tomoichi

2004-01-01

Symbol segmentation is very important in handwritten mathematical formula recognition, since it is the very first portion of the recognition, since it is the very first portion of the recognition process. This paper proposes a new symbol segmentation method using mathematical structure information. The base technique of symbol segmentation employed in theexisting methods is dynamic programming which optimizes the overall results of individual symbol recognition. The new method we propose here...

Hardware Acceleration on Cloud Services: The use of Restricted Boltzmann Machines on Handwritten Digits Recognition

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Eleni Bougioukou

2018-02-01

Full Text Available Cloud computing allows users and enterprises to process their data in high performance servers, thus reducing the need for advanced hardware at the client side. Although local processing is viable in many cases, collecting data from multiple clients and processing them in a server gives the best possible performance in terms of processing rate. In this work, the implementation of a high performance cloud computing engine for recognizing handwritten digits is presented. The engine exploits the benefits of cloud and uses a powerful hardware accelerator in order to classify the images received concurrently from multiple clients. The accelerator implements a number of neural networks, operating in parallel, resulting to a processing rate of more than 10 MImages/sec.

HMM-based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts.

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Bharath, A; Madhvanath, Sriganesh

2012-04-01

Research for recognizing online handwritten words in Indic scripts is at its early stages when compared to Latin and Oriental scripts. In this paper, we address this problem specifically for two major Indic scripts--Devanagari and Tamil. In contrast to previous approaches, the techniques we propose are largely data driven and script independent. We propose two different techniques for word recognition based on Hidden Markov Models (HMM): lexicon driven and lexicon free. The lexicon-driven technique models each word in the lexicon as a sequence of symbol HMMs according to a standard symbol writing order derived from the phonetic representation. The lexicon-free technique uses a novel Bag-of-Symbols representation of the handwritten word that is independent of symbol order and allows rapid pruning of the lexicon. On handwritten Devanagari word samples featuring both standard and nonstandard symbol writing orders, a combination of lexicon-driven and lexicon-free recognizers significantly outperforms either of them used in isolation. In contrast, most Tamil word samples feature the standard symbol order, and the lexicon-driven recognizer outperforms the lexicon free one as well as their combination. The best recognition accuracies obtained for 20,000 word lexicons are 87.13 percent for Devanagari when the two recognizers are combined, and 91.8 percent for Tamil using the lexicon-driven technique.

Optical character recognition of handwritten Arabic using hidden Markov models

Science.gov (United States)

Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.; Olama, Mohammed M.

2011-04-01

The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language is initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.

Handwritten Sindhi Character Recognition Using Neural Networks

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Shafique Ahmed Awan

2018-01-01

Full Text Available OCR (OpticalCharacter Recognition is a technology in which text image is used to understand and write text by machines. The work on languages containing isolated characters such as German, English, French and others is at its peak. The OCR and ICR (Intelligent Character Recognition research in Sindhi script is currently at in starting stages and not sufficient work have been cited in this area even though Sindhi language is rich in culture and history. This paper presents one of the initial steps in recognizing Sindhi handwritten characters. The isolated characters of Sindhi script written by thesubjects have been recognized. The various subjects were asked to write Sindhi characters in unconstrained form and then the written samples were collected and scanned through a flatbed scanner. The scanned documents were preprocessedwith the help of binary conversion, removing noise by pepper noise and the lines were segmented with the help of horizontal profile technique. The segmented lines were used to extract characters from scanned pages.This character segmentation was done by vertical projection. The extracted characters have been used to extract features so that the characters can be classified easily. Zoning was used for the feature extraction technique. For the classification, neural network has been used. The recognized characters converted into editable text with an average accuracy of 85%.

Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks

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Emilio Granell

2018-01-01

Full Text Available The digitization of historical handwritten document images is important for the preservation of cultural heritage. Moreover, the transcription of text images obtained from digitization is necessary to provide efficient information access to the content of these documents. Handwritten Text Recognition (HTR has become an important research topic in the areas of image and computational language processing that allows us to obtain transcriptions from text images. State-of-the-art HTR systems are, however, far from perfect. One difficulty is that they have to cope with image noise and handwriting variability. Another difficulty is the presence of a large amount of Out-Of-Vocabulary (OOV words in ancient historical texts. A solution to this problem is to use external lexical resources, but such resources might be scarce or unavailable given the nature and the age of such documents. This work proposes a solution to avoid this limitation. It consists of associating a powerful optical recognition system that will cope with image noise and variability, with a language model based on sub-lexical units that will model OOV words. Such a language modeling approach reduces the size of the lexicon while increasing the lexicon coverage. Experiments are first conducted on the publicly available Rodrigo dataset, which contains the digitization of an ancient Spanish manuscript, with a recognizer based on Hidden Markov Models (HMMs. They show that sub-lexical units outperform word units in terms of Word Error Rate (WER, Character Error Rate (CER and OOV word accuracy rate. This approach is then applied to deep net classifiers, namely Bi-directional Long-Short Term Memory (BLSTMs and Convolutional Recurrent Neural Nets (CRNNs. Results show that CRNNs outperform HMMs and BLSTMs, reaching the lowest WER and CER for this image dataset and significantly improving OOV recognition.

Investigation of efficient features for image recognition by neural networks.

Science.gov (United States)

Goltsev, Alexander; Gritsenko, Vladimir

2012-04-01

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

Handwritten Devanagari Character Recognition Using Layer-Wise Training of Deep Convolutional Neural Networks and Adaptive Gradient Methods

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Mahesh Jangid

2018-02-01

Full Text Available Handwritten character recognition is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human–robot interaction, automatic data entry for business documents, etc. In this work, we propose a technique to recognize handwritten Devanagari characters using deep convolutional neural networks (DCNN which are one of the recent techniques adopted from the deep learning community. We experimented the ISIDCHAR database provided by (Information Sharing Index ISI, Kolkata and V2DMDCHAR database with six different architectures of DCNN to evaluate the performance and also investigate the use of six recently developed adaptive gradient methods. A layer-wise technique of DCNN has been employed that helped to achieve the highest recognition accuracy and also get a faster convergence rate. The results of layer-wise-trained DCNN are favorable in comparison with those achieved by a shallow technique of handcrafted features and standard DCNN.

Eye movements when reading sentences with handwritten words.

Science.gov (United States)

Perea, Manuel; Marcet, Ana; Uixera, Beatriz; Vergara-Martínez, Marta

2016-10-17

The examination of how we read handwritten words (i.e., the original form of writing) has typically been disregarded in the literature on reading. Previous research using word recognition tasks has shown that lexical effects (e.g., the word-frequency effect) are magnified when reading difficult handwritten words. To examine this issue in a more ecological scenario, we registered the participants' eye movements when reading handwritten sentences that varied in the degree of legibility (i.e., sentences composed of words in easy vs. difficult handwritten style). For comparison purposes, we included a condition with printed sentences. Results showed a larger reading cost for sentences with difficult handwritten words than for sentences with easy handwritten words, which in turn showed a reading cost relative to the sentences with printed words. Critically, the effect of word frequency was greater for difficult handwritten words than for easy handwritten words or printed words in the total times on a target word, but not on first-fixation durations or gaze durations. We examine the implications of these findings for models of eye movement control in reading.

A comparative study of different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron

OpenAIRE

Das, Nibaran; Mollah, Ayatullah Faruk; Sarkar, Ram; Basu, Subhadip

2010-01-01

The work presents a comparative assessment of seven different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron (MLP) based classifier. The seven feature sets employed here consist of shadow features, octant centroids, longest runs, angular distances, effective spans, dynamic centers of gravity, and some of their combinations. On experimentation with a database of 3000 samples, the maximum recognition rate of 95.80% is observed with both of two separat...

BanglaLekha-Isolated: A multi-purpose comprehensive dataset of Handwritten Bangla Isolated characters

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Mithun Biswas

2017-06-01

Full Text Available BanglaLekha-Isolated, a Bangla handwritten isolated character dataset is presented in this article. This dataset contains 84 different characters comprising of 50 Bangla basic characters, 10 Bangla numerals and 24 selected compound characters. 2000 handwriting samples for each of the 84 characters were collected, digitized and pre-processed. After discarding mistakes and scribbles, 1,66,105 handwritten character images were included in the final dataset. The dataset also includes labels indicating the age and the gender of the subjects from whom the samples were collected. This dataset could be used not only for optical handwriting recognition research but also to explore the influence of gender and age on handwriting. The dataset is publicly available at https://data.mendeley.com/datasets/hf6sf8zrkc/2.

Interpreting Chicken-Scratch: Lexical Access for Handwritten Words

Science.gov (United States)

Barnhart, Anthony S.; Goldinger, Stephen D.

2010-01-01

Handwritten word recognition is a field of study that has largely been neglected in the psychological literature, despite its prevalence in society. Whereas studies of spoken word recognition almost exclusively employ natural, human voices as stimuli, studies of visual word recognition use synthetic typefaces, thus simplifying the process of word…

Detection of Text Lines of Handwritten Arabic Manuscripts using Markov Decision Processes

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Youssef Boulid

2016-09-01

Full Text Available In a character recognition systems, the segmentation phase is critical since the accuracy of the recognition depend strongly on it. In this paper we present an approach based on Markov Decision Processes to extract text lines from binary images of Arabic handwritten documents. The proposed approach detects the connected components belonging to the same line by making use of knowledge about features and arrangement of those components. The initial results show that the system is promising for extracting Arabic handwritten lines.

Handwritten-word spotting using biologically inspired features

NARCIS (Netherlands)

van der Zant, Tijn; Schomaker, Lambert; Haak, Koen

For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language, and collection. We propose a biologically inspired

A comparison of 1D and 2D LSTM architectures for the recognition of handwritten Arabic

Science.gov (United States)

Yousefi, Mohammad Reza; Soheili, Mohammad Reza; Breuel, Thomas M.; Stricker, Didier

2015-01-01

In this paper, we present an Arabic handwriting recognition method based on recurrent neural network. We use the Long Short Term Memory (LSTM) architecture, that have proven successful in different printed and handwritten OCR tasks. Applications of LSTM for handwriting recognition employ the two-dimensional architecture to deal with the variations in both vertical and horizontal axis. However, we show that using a simple pre-processing step that normalizes the position and baseline of letters, we can make use of 1D LSTM, which is faster in learning and convergence, and yet achieve superior performance. In a series of experiments on IFN/ENIT database for Arabic handwriting recognition, we demonstrate that our proposed pipeline can outperform 2D LSTM networks. Furthermore, we provide comparisons with 1D LSTM networks trained with manually crafted features to show that the automatically learned features in a globally trained 1D LSTM network with our normalization step can even outperform such systems.

Where are the Search Engines for Handwritten Documents?

NARCIS (Netherlands)

van der Zant, Tijn; Schomaker, Lambert; Zinger, Svitlana; van Schie, Henny

Although the problems of optical character recognition for contemporary printed text have been resolved, for historical printed and handwritten connected cursive text (i.e. western style writing), they have not. This does not mean that scanning historical documents is not useful. This article

Where are the search engines for handwritten documents?

NARCIS (Netherlands)

Zant, T.; Schomaker, L.; Zinger, S.; Schie, H.

2009-01-01

Although the problems of optical character recognition for contemporary printed text have been resolved, for historical printed and handwritten connected cursive text (i.e. western style writing), they have not. This does not mean that scanning historical documents is not useful. This article

Handwritten Digit Recognition using Edit Distance-Based KNN

OpenAIRE

Bernard , Marc; Fromont , Elisa; Habrard , Amaury; Sebban , Marc

2012-01-01

We discuss the student project given for the last 5 years to the 1st year Master Students which follow the Machine Learning lecture at the University Jean Monnet in Saint Etienne, France. The goal of this project is to develop a GUI that can recognize digits and/or letters drawn manually. The system is based on a string representation of the dig- its using Freeman codes and on the use of an edit-distance-based K-Nearest Neighbors classifier. In addition to the machine learning knowledge about...

Beyond OCR : Multi-faceted understanding of handwritten document characteristics

NARCIS (Netherlands)

He, Sheng; Schomaker, Lambert

Handwritten document understanding is a fundamental research problem in pattern recognition and it relies on the effective features. In this paper, we propose a joint feature distribution (JFD) principle to design novel discriminative features which could be the joint distribution of features on

Font generation of personal handwritten Chinese characters

Science.gov (United States)

Lin, Jeng-Wei; Wang, Chih-Yin; Ting, Chao-Lung; Chang, Ray-I.

2014-01-01

Today, digital multimedia messages have drawn more and more attention due to the great achievement of computer and network techniques. Nevertheless, text is still the most popular media for people to communicate with others. Many fonts have been developed so that product designers can choose unique fonts to demonstrate their idea gracefully. It is commonly believed that handwritings can reflect one's personality, emotion, feeling, education level, and so on. This is especially true in Chinese calligraphy. However, it is not easy for ordinary users to customize a font of their personal handwritings. In this study, we performed a process reengineering in font generation. We present a new method to create font in a batch mode. Rather than to create glyphs of characters one by one according to their codepoints, people create glyphs incrementally in an on-demand manner. A Java Implementation is developed to read a document image of user handwritten Chinese characters, and make a vector font of these handwritten Chinese characters. Preliminary experiment result shows that the proposed method can help ordinary users create their personal handwritten fonts easily and quickly.

Slant correction for handwritten English documents

Science.gov (United States)

Shridhar, Malayappan; Kimura, Fumitaka; Ding, Yimei; Miller, John W. V.

2004-12-01

Optical character recognition of machine-printed documents is an effective means for extracting textural material. While the level of effectiveness for handwritten documents is much poorer, progress is being made in more constrained applications such as personal checks and postal addresses. In these applications a series of steps is performed for recognition beginning with removal of skew and slant. Slant is a characteristic unique to the writer and varies from writer to writer in which characters are tilted some amount from vertical. The second attribute is the skew that arises from the inability of the writer to write on a horizontal line. Several methods have been proposed and discussed for average slant estimation and correction in the earlier papers. However, analysis of many handwritten documents reveals that slant is a local property and slant varies even within a word. The use of an average slant for the entire word often results in overestimation or underestimation of the local slant. This paper describes three methods for local slant estimation, namely the simple iterative method, high-speed iterative method, and the 8-directional chain code method. The experimental results show that the proposed methods can estimate and correct local slant more effectively than the average slant correction.

Segmentation of Arabic Handwritten Documents into Text Lines using Watershed Transform

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Abdelghani Souhar

2017-12-01

Full Text Available A crucial task in character recognition systems is the segmentation of the document into text lines and especially if it is handwritten. When dealing with non-Latin document such as Arabic, the challenge becomes greater since in addition to the variability of writing, the presence of diacritical points and the high number of ascender and descender characters complicates more the process of the segmentation. To remedy with this complexity and even to make this difficulty an advantage since the focus is on the Arabic language which is semi-cursive in nature, a method based on the Watershed Transform technique is proposed. Tested on «Handwritten Arabic Proximity Datasets» a segmentation rate of 93% for a 95% of matching score is achieved.

Comparative implementation of Handwritten and Machine written Gurmukhi text utilizing appropriate parameters

Science.gov (United States)

Kaur, Jaswinder; Jagdev, Gagandeep, Dr.

2018-01-01

Optical character recognition is concerned with the recognition of optically processed characters. The recognition is done offline after the writing or printing has been completed, unlike online recognition where the computer has to recognize the characters instantly as they are drawn. The performance of character recognition depends upon the quality of scanned documents. The preprocessing steps are used for removing low-frequency background noise and normalizing the intensity of individual scanned documents. Several filters are used for reducing certain image details and enabling an easier or faster evaluation. The primary aim of the research work is to recognize handwritten and machine written characters and differentiate them. The language opted for the research work is Punjabi Gurmukhi and tool utilized is Matlab.

Structural analysis of online handwritten mathematical symbols based on support vector machines

Science.gov (United States)

Simistira, Foteini; Papavassiliou, Vassilis; Katsouros, Vassilis; Carayannis, George

2013-01-01

Mathematical expression recognition is still a very challenging task for the research community mainly because of the two-dimensional (2d) structure of mathematical expressions (MEs). In this paper, we present a novel approach for the structural analysis between two on-line handwritten mathematical symbols of a ME, based on spatial features of the symbols. We introduce six features to represent the spatial affinity of the symbols and compare two multi-class classification methods that employ support vector machines (SVMs): one based on the "one-against-one" technique and one based on the "one-against-all", in identifying the relation between a pair of symbols (i.e. subscript, numerator, etc). A dataset containing 1906 spatial relations derived from the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2012 training dataset is constructed to evaluate the classifiers and compare them with the rule-based classifier of the ILSP-1 system participated in the contest. The experimental results give an overall mean error rate of 2.61% for the "one-against-one" SVM approach, 6.57% for the "one-against-all" SVM technique and 12.31% error rate for the ILSP-1 classifier.

A Proposed Arabic Handwritten Text Normalization Method

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Tarik Abu-Ain

2014-11-01

Full Text Available Text normalization is an important technique in document image analysis and recognition. It consists of many preprocessing stages, which include slope correction, text padding, skew correction, and straight the writing line. In this side, text normalization has an important role in many procedures such as text segmentation, feature extraction and characters recognition. In the present article, a new method for text baseline detection, straightening, and slant correction for Arabic handwritten texts is proposed. The method comprises a set of sequential steps: first components segmentation is done followed by components text thinning; then, the direction features of the skeletons are extracted, and the candidate baseline regions are determined. After that, selection of the correct baseline region is done, and finally, the baselines of all components are aligned with the writing line.  The experiments are conducted on IFN/ENIT benchmark Arabic dataset. The results show that the proposed method has a promising and encouraging performance.

Least Square Support Vector Machine Classifier vs a Logistic Regression Classifier on the Recognition of Numeric Digits

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Danilo A. López-Sarmiento

2013-11-01

Full Text Available In this paper is compared the performance of a multi-class least squares support vector machine (LSSVM mc versus a multi-class logistic regression classifier to problem of recognizing the numeric digits (0-9 handwritten. To develop the comparison was used a data set consisting of 5000 images of handwritten numeric digits (500 images for each number from 0-9, each image of 20 x 20 pixels. The inputs to each of the systems were vectors of 400 dimensions corresponding to each image (not done feature extraction. Both classifiers used OneVsAll strategy to enable multi-classification and a random cross-validation function for the process of minimizing the cost function. The metrics of comparison were precision and training time under the same computational conditions. Both techniques evaluated showed a precision above 95 %, with LS-SVM slightly more accurate. However the computational cost if we found a marked difference: LS-SVM training requires time 16.42 % less than that required by the logistic regression model based on the same low computational conditions.

A REVIEW: OPTICAL CHARACTER RECOGNITION

OpenAIRE

Swati Tomar*1 & Amit Kishore2

2018-01-01

This paper presents detailed review in the field of Optical Character Recognition. Various techniques are determine that have been proposed to realize the center of character recognition in an optical character recognition system. Even though, sufficient studies and papers are describes the techniques for converting textual content from a paper document into machine readable form. Optical character recognition is a process where the computer understands automatically the image of handwritten ...

Assessment of legibility and completeness of handwritten and electronic prescriptions.

Science.gov (United States)

Albarrak, Ahmed I; Al Rashidi, Eman Abdulrahman; Fatani, Rwaa Kamil; Al Ageel, Shoog Ibrahim; Mohammed, Rafiuddin

2014-12-01

To assess the legibility and completeness of handwritten prescriptions and compare with electronic prescription system for medication errors. Prospective study. King Khalid University Hospital (KKUH), Riyadh, Saudi Arabia. Handwritten prescriptions were received from clinical units of Medicine Outpatient Department (MOPD), Primary Care Clinic (PCC) and Surgery Outpatient Department (SOPD) whereas electronic prescriptions were collected from the pediatric ward. The handwritten prescription was assessed for completeness by the checklist designed according to the hospital prescription and evaluated for legibility by two pharmacists. The comparison between handwritten and electronic prescription errors was evaluated based on the validated checklist adopted from previous studies. Legibility and completeness of prescriptions. 398 prescriptions (199 handwritten and 199 e-prescriptions) were assessed. About 71 (35.7%) of handwritten and 5 (2.5%) of electronic prescription errors were identified. A significant statistical difference (P prescriptions in omitted dose and omitted route of administration category of error distribution. The rate of completeness in patient identification in handwritten prescriptions was 80.97% in MOPD, 76.36% in PCC and 85.93% in SOPD clinic units. Assessment of medication prescription completeness was 91.48% in MOPD, 88.48% in PCC, and 89.28% in SOPD. This study revealed a high incidence of prescribing errors in handwritten prescriptions. The use of e-prescription system showed a significant decline in the incidence of errors. The legibility of handwritten prescriptions was relatively good whereas the level of completeness was very low.

Attributing Authorship in the Noisy Digitized Correspondence of Jacob and Wilhelm Grimm

Directory of Open Access Journals (Sweden)

Greta Franzini

2018-04-01

Full Text Available This article presents the results of a multidisciplinary project aimed at better understanding the impact of different digitization strategies in computational text analysis. More specifically, it describes an effort to automatically discern the authorship of Jacob and Wilhelm Grimm in a body of uncorrected correspondence processed by HTR (Handwritten Text Recognition and OCR (Optical Character Recognition, reporting on the effect this noise has on the analyses necessary to computationally identify the different writing style of the two brothers. In summary, our findings show that OCR digitization serves as a reliable proxy for the more painstaking process of manual digitization, at least when it comes to authorship attribution. Our results suggest that attribution is viable even when using training and test sets from different digitization pipelines. With regards to HTR, this research demonstrates that even though automated transcription significantly increases the risk of text misclassification when compared to OCR, a cleanliness above ≈ 20% is already sufficient to achieve a higher-than-chance probability of correct binary attribution.

Boosting bonsai trees for handwritten/printed text discrimination

Science.gov (United States)

Ricquebourg, Yann; Raymond, Christian; Poirriez, Baptiste; Lemaitre, Aurélie; Coüasnon, Bertrand

2013-12-01

Boosting over decision-stumps proved its efficiency in Natural Language Processing essentially with symbolic features, and its good properties (fast, few and not critical parameters, not sensitive to over-fitting) could be of great interest in the numeric world of pixel images. In this article we investigated the use of boosting over small decision trees, in image classification processing, for the discrimination of handwritten/printed text. Then, we conducted experiments to compare it to usual SVM-based classification revealing convincing results with very close performance, but with faster predictions and behaving far less as a black-box. Those promising results tend to make use of this classifier in more complex recognition tasks like multiclass problems.

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

Science.gov (United States)

Sadeghi, Zahra; Testolin, Alberto

2017-08-01

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

Enhancing spoken connected-digit recognition accuracy by error ...

Indian Academy of Sciences (India)

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

nition systems have gained acceptable accuracy levels, the accuracy of recognition of current connected ... bar code and ISBN1 library code to name a few. ..... Kopec G, Bush M 1985 Network-based connected-digit recognition. IEEE Trans.

Classification of remotely sensed data using OCR-inspired neural network techniques. [Optical Character Recognition

Science.gov (United States)

Kiang, Richard K.

1992-01-01

Neural networks have been applied to classifications of remotely sensed data with some success. To improve the performance of this approach, an examination was made of how neural networks are applied to the optical character recognition (OCR) of handwritten digits and letters. A three-layer, feedforward network, along with techniques adopted from OCR, was used to classify Landsat-4 Thematic Mapper data. Good results were obtained. To overcome the difficulties that are characteristic of remote sensing applications and to attain significant improvements in classification accuracy, a special network architecture may be required.

Character context: a shape descriptor for Arabic handwriting recognition

Science.gov (United States)

Mudhsh, Mohammed; Almodfer, Rolla; Duan, Pengfei; Xiong, Shengwu

2017-11-01

In the handwriting recognition field, designing good descriptors are substantial to obtain rich information of the data. However, the handwriting recognition research of a good descriptor is still an open issue due to unlimited variation in human handwriting. We introduce a "character context descriptor" that efficiently dealt with the structural characteristics of Arabic handwritten characters. First, the character image is smoothed and normalized, then the character context descriptor of 32 feature bins is built based on the proposed "distance function." Finally, a multilayer perceptron with regularization is used as a classifier. On experimentation with a handwritten Arabic characters database, the proposed method achieved a state-of-the-art performance with recognition rate equal to 98.93% and 99.06% for the 66 and 24 classes, respectively.

Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the 'Extreme Learning Machine' Algorithm.

Directory of Open Access Journals (Sweden)

Mark D McDonnell

Full Text Available Recent advances in training deep (multi-layer architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM approach, which also enables a very rapid training time (∼ 10 minutes. Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random 'receptive field' sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

Connected digit speech recognition system for Malayalam language

Indian Academy of Sciences (India)

A connected digit speech recognition is important in many applications such as automated banking system, catalogue-dialing, automatic data entry, automated banking system, etc. This paper presents an optimum speaker-independent connected digit recognizer for Malayalam language. The system employs Perceptual ...

ASM Based Synthesis of Handwritten Arabic Text Pages

Directory of Open Access Journals (Sweden)

Laslo Dinges

2015-01-01

Full Text Available Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system that automatically turns Arabic Unicode text into synthetic images of handwritten documents and detailed ground truth. Active Shape Models (ASMs based on 28046 online samples were used for character synthesis and statistical properties were extracted from the IESK-arDB database to simulate baselines and word slant or skew. In the synthesis step ASM based representations are composed to words and text pages, smoothed by B-Spline interpolation and rendered considering writing speed and pen characteristics. Finally, we use the synthetic data to validate a segmentation method. An experimental comparison with the IESK-arDB database encourages to train and test document analysis related methods on synthetic samples, whenever no sufficient natural ground truthed data is available.

ASM Based Synthesis of Handwritten Arabic Text Pages.

Science.gov (United States)

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-Etriby, Sherif; Ghoneim, Ahmed

2015-01-01

Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system that automatically turns Arabic Unicode text into synthetic images of handwritten documents and detailed ground truth. Active Shape Models (ASMs) based on 28046 online samples were used for character synthesis and statistical properties were extracted from the IESK-arDB database to simulate baselines and word slant or skew. In the synthesis step ASM based representations are composed to words and text pages, smoothed by B-Spline interpolation and rendered considering writing speed and pen characteristics. Finally, we use the synthetic data to validate a segmentation method. An experimental comparison with the IESK-arDB database encourages to train and test document analysis related methods on synthetic samples, whenever no sufficient natural ground truthed data is available.

UNCONSTRAINED HANDWRITING RECOGNITION : LANGUAGE MODELS, PERPLEXITY, AND SYSTEM PERFORMANCE

NARCIS (Netherlands)

Marti, U-V.; Bunke, H.

2004-01-01

In this paper we present a number of language models and their behavior in the recognition of unconstrained handwritten English sentences. We use the perplexity to compare the different models and their prediction power, and relate it to the performance of a recognition system under different

Mathematical symbol hypothesis recognition with rejection option

OpenAIRE

Julca-Aguilar , Frank; Hirata , Nina ,; Viard-Gaudin , Christian; Mouchère , Harold; Medjkoune , Sofiane

2014-01-01

International audience; In the context of handwritten mathematical expressions recognition, a first step consist on grouping strokes (segmentation) to form symbol hypotheses: groups of strokes that might represent a symbol. Then, the symbol recognition step needs to cope with the identification of wrong segmented symbols (false hypotheses). However, previous works on symbol recognition consider only correctly segmented symbols. In this work, we focus on the problem of mathematical symbol reco...

Human interface for personal information systems. On-line handwriting recognition; Pasonaru joho kiki ni okeru human interface. On-line tegaki ninshiki

Energy Technology Data Exchange (ETDEWEB)

Morita, T. [Sharp Corp., Osaka (Japan)

1996-01-05

Most of information devices used in the business field use keyboards for the inputting measure, but keyboards are rather awkward for personal use. In contrast to this, the pen input method which everybody can use easily is a product of the latest development. In this articles, on-line handwritten letter recognition is roughly explained which is the basic technique of pen input. Pen input has a demerit that its letter inputting speed is slow, but has much more merits that Chinese ideographs can be directly input, figures, handwritten memoranda, etc. are treated likewise, the device itself can be made compact and no noise is made. The on-line letter recognition methods now used practically can be roughly divided into the pattern matching method and the basic stroke method. Each of them has its own merits and demerits. For the current on-line handwritten letter recognition, the condition is necessary to handwritten a letter in the square style (kaisho) and carefully within the framework for letter entry upon writing, and for this arrangement, input is performed through the work processes of pretreatment/feature extraction, stroke recognition, letter comparison, detail discrimination, and after-treatment. 3 refs., 7 figs.

Korean letter handwritten recognition using deep convolutional neural network on android platform

Science.gov (United States)

Purnamawati, S.; Rachmawati, D.; Lumanauw, G.; Rahmat, R. F.; Taqyuddin, R.

2018-03-01

Currently, popularity of Korean culture attracts many people to learn everything about Korea, particularly its language. To acquire Korean Language, every single learner needs to be able to understand Korean non-Latin character. A digital approach needs to be carried out in order to make Korean learning process easier. This study is done by using Deep Convolutional Neural Network (DCNN). DCNN performs the recognition process on the image based on the model that has been trained such as Inception-v3 Model. Subsequently, re-training process using transfer learning technique with the trained and re-trained value of model is carried though in order to develop a new model with a better performance without any specific systemic errors. The testing accuracy of this research results in 86,9%.

ANALYTIC WORD RECOGNITION WITHOUT SEGMENTATION BASED ON MARKOV RANDOM FIELDS

NARCIS (Netherlands)

Coisy, C.; Belaid, A.

2004-01-01

In this paper, a method for analytic handwritten word recognition based on causal Markov random fields is described. The words models are HMMs where each state corresponds to a letter; each letter is modelled by a NSHP­HMM (Markov field). Global models are build dynamically, and used for recognition

A Novel Handwritten Letter Recognizer Using Enhanced Evolutionary Neural Network

Science.gov (United States)

Mahmoudi, Fariborz; Mirzashaeri, Mohsen; Shahamatnia, Ehsan; Faridnia, Saed

This paper introduces a novel design for handwritten letter recognition by employing a hybrid back-propagation neural network with an enhanced evolutionary algorithm. Feeding the neural network consists of a new approach which is invariant to translation, rotation, and scaling of input letters. Evolutionary algorithm is used for the global search of the search space and the back-propagation algorithm is used for the local search. The results have been computed by implementing this approach for recognizing 26 English capital letters in the handwritings of different people. The computational results show that the neural network reaches very satisfying results with relatively scarce input data and a promising performance improvement in convergence of the hybrid evolutionary back-propagation algorithms is exhibited.

A knowledge-based approach for recognition of handwritten Pitman ...

Indian Academy of Sciences (India)

The paper describes a knowledge-based approach for the recognition of PSL strokes. Information about location and the direction of the starting point and final point of strokes are considered the knowledge base for recognition of strokes. The work comprises preprocessing, determination of starting and final points, ...

Implementation of age and gender recognition system for intelligent digital signage

Science.gov (United States)

Lee, Sang-Heon; Sohn, Myoung-Kyu; Kim, Hyunduk

2015-12-01

Intelligent digital signage systems transmit customized advertising and information by analyzing users and customers, unlike existing system that presented advertising in the form of broadcast without regard to type of customers. Currently, development of intelligent digital signage system has been pushed forward vigorously. In this study, we designed a system capable of analyzing gender and age of customers based on image obtained from camera, although there are many different methods for analyzing customers. We conducted age and gender recognition experiments using public database. The age/gender recognition experiments were performed through histogram matching method by extracting Local binary patterns (LBP) features after facial area on input image was normalized. The results of experiment showed that gender recognition rate was as high as approximately 97% on average. Age recognition was conducted based on categorization into 5 age classes. Age recognition rates for women and men were about 67% and 68%, respectively when that conducted separately for different gender.

IMPROVEMENT OF RECOGNITION QUALITY IN DEEP LEARNING NETWORKS BY SIMULATED ANNEALING METHOD

Directory of Open Access Journals (Sweden)

A. S. Potapov

2014-09-01

Full Text Available The subject of this research is deep learning methods, in which automatic construction of feature transforms is taken place in tasks of pattern recognition. Multilayer autoencoders have been taken as the considered type of deep learning networks. Autoencoders perform nonlinear feature transform with logistic regression as an upper classification layer. In order to verify the hypothesis of possibility to improve recognition rate by global optimization of parameters for deep learning networks, which are traditionally trained layer-by-layer by gradient descent, a new method has been designed and implemented. The method applies simulated annealing for tuning connection weights of autoencoders while regression layer is simultaneously trained by stochastic gradient descent. Experiments held by means of standard MNIST handwritten digit database have shown the decrease of recognition error rate from 1.1 to 1.5 times in case of the modified method comparing to the traditional method, which is based on local optimization. Thus, overfitting effect doesn’t appear and the possibility to improve learning rate is confirmed in deep learning networks by global optimization methods (in terms of increasing recognition probability. Research results can be applied for improving the probability of pattern recognition in the fields, which require automatic construction of nonlinear feature transforms, in particular, in the image recognition. Keywords: pattern recognition, deep learning, autoencoder, logistic regression, simulated annealing.

Statistical pattern recognition for automatic writer identification and verification

NARCIS (Netherlands)

Bulacu, Marius Lucian

2007-01-01

The thesis addresses the problem of automatic person identification using scanned images of handwriting.Identifying the author of a handwritten sample using automatic image-based methods is an interesting pattern recognition problem with direct applicability in the forensic and historic document

Recognition of online handwritten Gurmukhi characters based on ...

Indian Academy of Sciences (India)

Karun Verma

as the recognition of characters using rule based post-pro- cessing algorithm. ... ods in their work in order to recognize handwriting with pen-based devices. ..... Centernew is the average y-coordinate value of new stroke and denotes the center ...

Ekstraksi Ciri Batang untuk Pengenalan Nomer Rekening Tulisan Tangan dengan Jaringan Syaraf Tiruan Perambatan Balik

Directory of Open Access Journals (Sweden)

Farida Asriani

2012-02-01

Full Text Available Handwriting number recognition was being challenge problem to do in the recent years. The main objective for our research waso recognized handwritten account number. The original data was bank deposit slip that acquired by scanner. Before do the recognition of account number handwritten, first step that must be done was located account number on the bank deposit slip. After the location was found then the account number was segmented to cut up each numbern. After cutting the stem then performed feature extraction to obtain a vector which was fed to the neural network system for recognition rate. System back propagation neural network for handwritten digit pattern recognition was designed by 168neuron consists of input layer, 70 neurons in the hidden layer and 10 neurons in the output layer. The results obtained in this study were 83.78% of the data slip can be recognized correctly.

Sensitivity and specificity of a digit symbol recognition trial in the identification of response bias.

Science.gov (United States)

Kim, Nancy; Boone, Kyle B; Victor, Tara; Lu, Po; Keatinge, Carolyn; Mitchell, Cary

2010-08-01

Recently published practice standards recommend that multiple effort indicators be interspersed throughout neuropsychological evaluations to assess for response bias, which is most efficiently accomplished through use of effort indicators from standard cognitive tests already included in test batteries. The present study examined the utility of a timed recognition trial added to standard administration of the WAIS-III Digit Symbol subtest in a large sample of "real world" noncredible patients (n=82) as compared with credible neuropsychology clinic patients (n=89). Scores from the recognition trial were more sensitive in identifying poor effort than were standard Digit Symbol scores, and use of an equation incorporating Digit Symbol Age-Corrected Scaled Scores plus accuracy and time scores from the recognition trial was associated with nearly 80% sensitivity at 88.7% specificity. Thus, inclusion of a brief recognition trial to Digit Symbol administration has the potential to provide accurate assessment of response bias.

SU-F-T-20: Novel Catheter Lumen Recognition Algorithm for Rapid Digitization

Energy Technology Data Exchange (ETDEWEB)

Dise, J; McDonald, D; Ashenafi, M; Peng, J; Mart, C; Koch, N; Vanek, K [Medical University of South Carolina, Charleston, SC (United States)

2016-06-15

Purpose: Manual catheter recognition remains a time-consuming aspect of high-dose-rate brachytherapy (HDR) treatment planning. In this work, a novel catheter lumen recognition algorithm was created for accurate and rapid digitization. Methods: MatLab v8.5 was used to create the catheter recognition algorithm. Initially, the algorithm searches the patient CT dataset using an intensity based k-means filter designed to locate catheters. Once the catheters have been located, seed points are manually selected to initialize digitization of each catheter. From each seed point, the algorithm searches locally in order to automatically digitize the remaining catheter. This digitization is accomplished by finding pixels with similar image curvature and divergence parameters compared to the seed pixel. Newly digitized pixels are treated as new seed positions, and hessian image analysis is used to direct the algorithm toward neighboring catheter pixels, and to make the algorithm insensitive to adjacent catheters that are unresolvable on CT, air pockets, and high Z artifacts. The algorithm was tested using 11 HDR treatment plans, including the Syed template, tandem and ovoid applicator, and multi-catheter lung brachytherapy. Digitization error was calculated by comparing manually determined catheter positions to those determined by the algorithm. Results: he digitization error was 0.23 mm ± 0.14 mm axially and 0.62 mm ± 0.13 mm longitudinally at the tip. The time of digitization, following initial seed placement was less than 1 second per catheter. The maximum total time required to digitize all tested applicators was 4 minutes (Syed template with 15 needles). Conclusion: This algorithm successfully digitizes HDR catheters for a variety of applicators with or without CT markers. The minimal axial error demonstrates the accuracy of the algorithm, and its insensitivity to image artifacts and challenging catheter positioning. Future work to automatically place initial seed

SU-F-T-20: Novel Catheter Lumen Recognition Algorithm for Rapid Digitization

International Nuclear Information System (INIS)

Dise, J; McDonald, D; Ashenafi, M; Peng, J; Mart, C; Koch, N; Vanek, K

2016-01-01

Purpose: Manual catheter recognition remains a time-consuming aspect of high-dose-rate brachytherapy (HDR) treatment planning. In this work, a novel catheter lumen recognition algorithm was created for accurate and rapid digitization. Methods: MatLab v8.5 was used to create the catheter recognition algorithm. Initially, the algorithm searches the patient CT dataset using an intensity based k-means filter designed to locate catheters. Once the catheters have been located, seed points are manually selected to initialize digitization of each catheter. From each seed point, the algorithm searches locally in order to automatically digitize the remaining catheter. This digitization is accomplished by finding pixels with similar image curvature and divergence parameters compared to the seed pixel. Newly digitized pixels are treated as new seed positions, and hessian image analysis is used to direct the algorithm toward neighboring catheter pixels, and to make the algorithm insensitive to adjacent catheters that are unresolvable on CT, air pockets, and high Z artifacts. The algorithm was tested using 11 HDR treatment plans, including the Syed template, tandem and ovoid applicator, and multi-catheter lung brachytherapy. Digitization error was calculated by comparing manually determined catheter positions to those determined by the algorithm. Results: he digitization error was 0.23 mm ± 0.14 mm axially and 0.62 mm ± 0.13 mm longitudinally at the tip. The time of digitization, following initial seed placement was less than 1 second per catheter. The maximum total time required to digitize all tested applicators was 4 minutes (Syed template with 15 needles). Conclusion: This algorithm successfully digitizes HDR catheters for a variety of applicators with or without CT markers. The minimal axial error demonstrates the accuracy of the algorithm, and its insensitivity to image artifacts and challenging catheter positioning. Future work to automatically place initial seed

Handwritten document age classification based on handwriting styles

Science.gov (United States)

Ramaiah, Chetan; Kumar, Gaurav; Govindaraju, Venu

2012-01-01

Handwriting styles are constantly changing over time. We approach the novel problem of estimating the approximate age of Historical Handwritten Documents using Handwriting styles. This system will have many applications in handwritten document processing engines where specialized processing techniques can be applied based on the estimated age of the document. We propose to learn a distribution over styles across centuries using Topic Models and to apply a classifier over weights learned in order to estimate the approximate age of the documents. We present a comparison of different distance metrics such as Euclidean Distance and Hellinger Distance within this application.

Teachers' Perceptions of Digital Badges as Recognition of Professional Development

Science.gov (United States)

Jones, W. Monty; Hope, Samantha; Adams, Brianne

2018-01-01

This mixed methods study examined teachers' perceptions and uses of digital badges received as recognition of participation in a professional development program. Quantitative and qualitative survey data was collected from 99 K-12 teachers who were awarded digital badges in Spring 2016. In addition, qualitative data was collected through…

Latin Letters Recognition Using Optical Character Recognition to Convert Printed Media Into Digital Format

Directory of Open Access Journals (Sweden)

Rio Anugrah

2017-12-01

Full Text Available Printed media is still popular now days society. Unfortunately, such media encountered several drawbacks. For example, this type of media consumes large storage that impact in high maintenance cost. To keep printed information more efficient and long-lasting, people usually convert it into digital format. In this paper, we built Optical Character Recognition (OCR system to enable automatic conversion the image containing the sentence in Latin characters into digital text-shaped information. This system consists of several interrelated stages including preprocessing, segmentation, feature extraction, classifier, model and recognition. In preprocessing, the median filter is used to clarify the image from noise and the Otsu’s function is used to binarize the image. It followed by character segmentation using connected component labeling. Artificial neural network (ANN is used for feature extraction to recognize the character. The result shows that this system enable to recognize the characters in the image whose success rate is influenced by the training of the system.

Beyond OCR: Handwritten manuscript attribute understanding

NARCIS (Netherlands)

He, Sheng

2017-01-01

Knowing the author, date and location of handwritten historical documents is very important for historians to completely understand and reveal the valuable information they contain. In this thesis, three attributes, such as writer, date and geographical location, are studied by analyzing the

Handwriting Moroccan regions recognition using Tifinagh character

Directory of Open Access Journals (Sweden)

B. El Kessab

2015-09-01

In this context we propose a data set for handwritten Tifinagh regions composed of 1600 image (100 Image for each region. The dataset can be used in one hand to test the efficiency of the Tifinagh region recognition system in extraction of characteristics significatives and the correct identification of each region in classification phase in the other hand.

Use of digital speech recognition in diagnostics radiology

International Nuclear Information System (INIS)

Arndt, H.; Stockheim, D.; Mutze, S.; Petersein, J.; Gregor, P.; Hamm, B.

1999-01-01

Purpose: Applicability and benefits of digital speech recognition in diagnostic radiology were tested using the speech recognition system SP 6000. Methods: The speech recognition system SP 6000 was integrated into the network of the institute and connected to the existing Radiological Information System (RIS). Three subjects used this system for writing 2305 findings from dictation. After the recognition process the date, length of dictation, time required for checking/correction, kind of examination and error rate were recorded for every dictation. With the same subjects, a correlation was performed with 625 conventionally written finding. Results: After an 1-hour initial training the average error rates were 8.4 to 13.3%. The first adaptation of the speech recognition system (after nine days) decreased the average error rates to 2.4 to 10.7% due to the ability of the program to learn. The 2 nd and 3 rd adaptations resulted only in small changes of the error rate. An individual comparison of the error rate developments in the same kind of investigation showed the relative independence of the error rate on the individual user. Conclusion: The results show that the speech recognition system SP 6000 can be evaluated as an advantageous alternative for quickly recording radiological findings. A comparison between manually writing and dictating the findings verifies the individual differences of the writing speeds and shows the advantage of the application of voice recognition when faced with normal keyboard performance. (orig.) [de

On writing legibly: Processing fluency systematically biases evaluations of handwritten material

OpenAIRE

Greifeneder, Rainer; Alt, Alexander; Bottenberg, Konstantin; Seele, Tim; Zelt, Sarah; Wagener, Dietrich

2010-01-01

Evaluations of handwritten essays or exams are often suspected of being biased, such as by mood states or individual predilections. Although most of these influences are unsystematic, at least one bias is problematic because it systematically affects evaluations of handwritten materials. Three experiments revealed that essays in legible as compared to less legible handwriting were evaluated more positively. This robust finding was related to a basic judgmental mechanism that builds on the flu...

Optical Music Recognition for Scores Written in White Mensural Notation

Directory of Open Access Journals (Sweden)

Tardón LorenzoJ

2009-01-01

Full Text Available An Optical Music Recognition (OMR system especially adapted for handwritten musical scores of the XVII-th and the early XVIII-th centuries written in white mensural notation is presented. The system performs a complete sequence of analysis stages: the input is the RGB image of the score to be analyzed and, after a preprocessing that returns a black and white image with corrected rotation, the staves are processed to return a score without staff lines; then, a music symbol processing stage isolates the music symbols contained in the score and, finally, the classification process starts to obtain the transcription in a suitable electronic format so that it can be stored or played. This work will help to preserve our cultural heritage keeping the musical information of the scores in a digital format that also gives the possibility to perform and distribute the original music contained in those scores.

Physical Principles of the Method for Determination of Geometrical Characteristics and Particle Recognition in Digital Holography

Science.gov (United States)

Dyomin, V. V.; Polovtsev, I. G.; Davydova, A. Yu.

2018-03-01

The physical principles of a method for determination of geometrical characteristics of particles and particle recognition based on the concepts of digital holography, followed by processing of the particle images reconstructed from the digital hologram, using the morphological parameter are reported. An example of application of this method for fast plankton particle recognition is given.

Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research

Directory of Open Access Journals (Sweden)

Laslo Dinges

2016-03-01

Full Text Available Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers—that we proposed earlier—improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction.

Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research.

Science.gov (United States)

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-Etriby, Sherif

2016-03-11

Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers-that we proposed earlier-improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction.

Segmentation-Based And Segmentation-Free Methods for Spotting Handwritten Arabic Words

OpenAIRE

Ball , Gregory R.; Srihari , Sargur N.; Srinivasan , Harish

2006-01-01

http://www.suvisoft.com; Given a set of handwritten documents, a common goal is to search for a relevant subset. Attempting to find a query word or image in such a set of documents is called word spotting. Spotting handwritten words in documents written in the Latin alphabet, and more recently in Arabic, has received considerable attention. One issue is generating candidate word regions on a page. Attempting to definitely segment the document into such regions (automatic segmentation) can mee...

Fractionally Spaced Constant Modulus Equalizer with Recognition Capability for Digital Array Radar

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Feng Wang

2017-01-01

Full Text Available Fractionally spaced blind equalizer (BE based on constant modulus criteria is exploited to compensate for the channel-to-channel mismatch in a digital array radar. We apply the technique of recognition to improve the stability and reliability of the BE. The surveillance of the calibration signal and the convergence property of BE are both implemented with recognition description words. BE with cognitive capability is appropriate for the equalization of a digital array radar with thousands of channels and hundreds of working frequencies, where reliability becomes the most concerned indicator. The improvement of performance in the accidental scenarios is tested via numerical simulations with the cost of increased computational complexity.

Type-2 fuzzy graphical models for pattern recognition

CERN Document Server

Zeng, Jia

2015-01-01

This book discusses how to combine type-2 fuzzy sets and graphical models to solve a range of real-world pattern recognition problems such as speech recognition, handwritten Chinese character recognition, topic modeling as well as human action recognition. It covers these recent developments while also providing a comprehensive introduction to the fields of type-2 fuzzy sets and graphical models. Though primarily intended for graduate students, researchers and practitioners in fuzzy logic and pattern recognition, the book can also serve as a valuable reference work for researchers without any previous knowledge of these fields. Dr. Jia Zeng is a Professor at the School of Computer Science and Technology, Soochow University, China. Dr. Zhi-Qiang Liu is a Professor at the School of Creative Media, City University of Hong Kong, China.

Fulltext PDF

Indian Academy of Sciences (India)

Approximation methods. Sensitivity based reduced approaches for structural reliability analysis ... Classification and recognition of handwritten digits by using mathematical morphology. 419. Bolt. Relaxation .... A study on soil–structure interaction analysis in canyon-shaped topographies. 255. Elasto-plastic element free ...

A New Database of Digits Extracted from Coins with Hard-to-Segment Foreground for Optical Character Recognition Evaluation

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Xingyu Pan

2017-05-01

Full Text Available Since the release date struck on a coin is important information of its monetary type, recognition of extracted digits may assist in identification of monetary types. However, digit images extracted from coins are challenging for conventional optical character recognition methods because the foreground of such digits has very often the same color as their background. In addition, other noises, including the wear of coin metal, make it more difficult to obtain a correct segmentation of the character shape. To address those challenges, this article presents the CoinNUMS database for automatic digit recognition. The database CoinNUMS, containing 3,006 digit images, is divided into three subsets. The first subset CoinNUMS_geni consists of 606 digit images manually cropped from high-resolution photographs of well-conserved coins from GENI coin photographs; the second subset CoinNUMS_pcgs_a consists of 1,200 digit images automatically extracted from a subset of the USA_Grading numismatic database containing coins in different quality; the last subset CoinNUMS_pcgs_m consists of 1,200 digit images manually extracted from the same coin photographs as CoinNUMS_pcgs_a. In CoinNUMS_pcgs_a and CoinNUMS_pcgs_m, the digit images are extracted from the release date. In CoinNUMS_geni, the digit images can come from the cropped date, the face value, or any other legends containing digits in the coin. To show the difficulty of these databases, we have tested recognition algorithms of the literature. The database and the results of the tested algorithms will be freely available on a dedicated website.1

Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks

KAUST Repository

Younis, Sohaib; Weiland, Claus; Hoehndorf, Robert; Dressler, Stefan; Hickler, Thomas; Seeger, Bernhard; Schmidt, Marco

2018-01-01

Herbaria worldwide are housing a treasure of hundreds of millions of herbarium specimens, which are increasingly being digitized and thereby more accessible to the scientific community. At the same time, deep-learning algorithms are rapidly improving pattern recognition from images and these techniques are more and more being applied to biological objects. In this study, we are using digital images of herbarium specimens in order to identify taxa and traits of these collection objects by applying convolutional neural networks (CNN). Images of the 1000 species most frequently documented by herbarium specimens on GBIF have been downloaded and combined with morphological trait data, preprocessed and divided into training and test datasets for species and trait recognition. Good performance in both domains suggests substantial potential of this approach for supporting taxonomy and natural history collection management. Trait recognition is also promising for applications in functional ecology.

Taxon and trait recognition from digitized herbarium specimens using deep convolutional neural networks

KAUST Repository

Younis, Sohaib

2018-03-13

Herbaria worldwide are housing a treasure of hundreds of millions of herbarium specimens, which are increasingly being digitized and thereby more accessible to the scientific community. At the same time, deep-learning algorithms are rapidly improving pattern recognition from images and these techniques are more and more being applied to biological objects. In this study, we are using digital images of herbarium specimens in order to identify taxa and traits of these collection objects by applying convolutional neural networks (CNN). Images of the 1000 species most frequently documented by herbarium specimens on GBIF have been downloaded and combined with morphological trait data, preprocessed and divided into training and test datasets for species and trait recognition. Good performance in both domains suggests substantial potential of this approach for supporting taxonomy and natural history collection management. Trait recognition is also promising for applications in functional ecology.

Analog design of a new neural network for optical character recognition.

Science.gov (United States)

Morns, I P; Dlay, S S

1999-01-01

An electronic circuit is presented for a new type of neural network, which gives a recognition rate of over 100 kHz. The network is used to classify handwritten numerals, presented as Fourier and wavelet descriptors, and has been shown to train far quicker than the popular backpropagation network while maintaining classification accuracy.

COMPARISON OF SVM AND FUZZY CLASSIFIER FOR AN INDIAN SCRIPT

Directory of Open Access Journals (Sweden)

M. J. Baheti

2012-01-01

Full Text Available With the advent of technological era, conversion of scanned document (handwritten or printed into machine editable format has attracted many researchers. This paper deals with the problem of recognition of Gujarati handwritten numerals. Gujarati numeral recognition requires performing some specific steps as a part of preprocessing. For preprocessing digitization, segmentation, normalization and thinning are done with considering that the image have almost no noise. Further affine invariant moments based model is used for feature extraction and finally Support Vector Machine (SVM and Fuzzy classifiers are used for numeral classification. . The comparison of SVM and Fuzzy classifier is made and it can be seen that SVM procured better results as compared to Fuzzy Classifier.

Towards a Digital Infrastructure for Illustrated Handwritten Archives

NARCIS (Netherlands)

Weber, Andreas; Ameryan, Mahya; Wolstencroft, Katherine; Stork, Lise; Heerlien, Maarten; Schomaker, Lambert; Ioannides, Marinos

Large and important parts of cultural heritage are stored in archives that are difficult to access, even after digitization. Documents and notes are written in hard-to-read historical handwriting and are often interspersed with illustrations. Such collections are weakly structured and largely

Implementation theory of distortion-invariant pattern recognition for optical and digital signal processing systems

Science.gov (United States)

Lhamon, Michael Earl

A pattern recognition system which uses complex correlation filter banks requires proportionally more computational effort than single-real valued filters. This introduces increased computation burden but also introduces a higher level of parallelism, that common computing platforms fail to identify. As a result, we consider algorithm mapping to both optical and digital processors. For digital implementation, we develop computationally efficient pattern recognition algorithms, referred to as, vector inner product operators that require less computational effort than traditional fast Fourier methods. These algorithms do not need correlation and they map readily onto parallel digital architectures, which imply new architectures for optical processors. These filters exploit circulant-symmetric matrix structures of the training set data representing a variety of distortions. By using the same mathematical basis as with the vector inner product operations, we are able to extend the capabilities of more traditional correlation filtering to what we refer to as "Super Images". These "Super Images" are used to morphologically transform a complicated input scene into a predetermined dot pattern. The orientation of the dot pattern is related to the rotational distortion of the object of interest. The optical implementation of "Super Images" yields feature reduction necessary for using other techniques, such as artificial neural networks. We propose a parallel digital signal processor architecture based on specific pattern recognition algorithms but general enough to be applicable to other similar problems. Such an architecture is classified as a data flow architecture. Instead of mapping an algorithm to an architecture, we propose mapping the DSP architecture to a class of pattern recognition algorithms. Today's optical processing systems have difficulties implementing full complex filter structures. Typically, optical systems (like the 4f correlators) are limited to phase

Modeling the lexical morphology of Western handwritten signatures.

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Moises Diaz-Cabrera

Full Text Available A handwritten signature is the final response to a complex cognitive and neuromuscular process which is the result of the learning process. Because of the many factors involved in signing, it is possible to study the signature from many points of view: graphologists, forensic experts, neurologists and computer vision experts have all examined them. Researchers study written signatures for psychiatric, penal, health and automatic verification purposes. As a potentially useful, multi-purpose study, this paper is focused on the lexical morphology of handwritten signatures. This we understand to mean the identification, analysis, and description of the signature structures of a given signer. In this work we analyze different public datasets involving 1533 signers from different Western geographical areas. Some relevant characteristics of signature lexical morphology have been selected, examined in terms of their probability distribution functions and modeled through a General Extreme Value distribution. This study suggests some useful models for multi-disciplinary sciences which depend on handwriting signatures.

Cryptographic key generation using handwritten signature

OpenAIRE

Freire, Manuel R.; Fiérrez, Julián; Ortega-García, Javier

2006-01-01

M. Freire-Santos ; J. Fierrez-Aguilar ; J. Ortega-Garcia; "Cryptographic key generation using handwritten signature", Biometric Technology for Human Identification III, Proc. SPIE 6202 (April 17, 2006); doi:10.1117/12.665875. Copyright 2006 Society of Photo‑Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of...

Applications of chaotic neurodynamics in pattern recognition

Science.gov (United States)

Baird, Bill; Freeman, Walter J.; Eeckman, Frank H.; Yao, Yong

1991-08-01

Network algorithms and architectures for pattern recognition derived from neural models of the olfactory system are reviewed. These span a range from highly abstract to physiologically detailed, and employ the kind of dynamical complexity observed in olfactory cortex, ranging from oscillation to chaos. A simple architecture and algorithm for analytically guaranteed associative memory storage of analog patterns, continuous sequences, and chaotic attractors in the same network is described. A matrix inversion determines network weights, given prototype patterns to be stored. There are N units of capacity in an N node network with 3N2 weights. It costs one unit per static attractor, two per Fourier component of each sequence, and three to four per chaotic attractor. There are no spurious attractors, and for sequences there is a Liapunov function in a special coordinate system which governs the approach of transient states to stored trajectories. Unsupervised or supervised incremental learning algorithms for pattern classification, such as competitive learning or bootstrap Widrow-Hoff can easily be implemented. The architecture can be ''folded'' into a recurrent network with higher order weights that can be used as a model of cortex that stores oscillatory and chaotic attractors by a Hebb rule. Network performance is demonstrated by application to the problem of real-time handwritten digit recognition. An effective system with on-line learning has been written by Eeckman and Baird for the Macintosh. It utilizes static, oscillatory, and/or chaotic attractors of two kinds--Lorenze attractors, or attractors resulting from chaotically interacting oscillatory modes. The successful application to an industrial pattern recognition problem of a network architecture of considerable physiological and dynamical complexity, developed by Freeman and Yao, is described. The data sets of the problem come in three classes of difficulty, and performance of the biological network is

Segmentation of Handwritten Chinese Character Strings Based on improved Algorithm Liu

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Zhihua Cai

2014-09-01

Full Text Available Algorithm Liu attracts high attention because of its high accuracy in segmentation of Japanese postal address. But the disadvantages, such as complexity and difficult implementation of algorithm, etc. have an adverse effect on its popularization and application. In this paper, the author applies the principles of algorithm Liu to handwritten Chinese character segmentation according to the characteristics of the handwritten Chinese characters, based on deeply study on algorithm Liu.In the same time, the author put forward the judgment criterion of Segmentation block classification and adhering mode of the handwritten Chinese characters.In the process of segmentation, text images are seen as the sequence made up of Connected Components (CCs, while the connected components are made up of several horizontal itinerary set of black pixels in image. The author determines whether these parts will be merged into segmentation through analyzing connected components. And then the author does image segmentation through adhering mode based on the analysis of outline edges. Finally cut the text images into character segmentation. Experimental results show that the improved Algorithm Liu obtains high segmentation accuracy and produces a satisfactory segmentation result.

Reduction of the dimension of neural network models in problems of pattern recognition and forecasting

Science.gov (United States)

Nasertdinova, A. D.; Bochkarev, V. V.

2017-11-01

Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good. The average error rate for the recognition of handwritten figures from the MNIST database was 1.12% (which is comparable to the results obtained using the "Deep training" methods), while the number of parameters of the neural network can be reduced to 130 times.

[About da tai - abortion in old Chinese folk medicine handwritten manuscripts].

Science.gov (United States)

Zheng, Jinsheng

2013-01-01

Of 881 Chinese handwritten volumes with medical texts of the 17th through mid-20th century held by Staatsbibliothek zu Berlin and Ethnologisches Museum Berlin-Dahlem, 48 volumes include prescriptions for induced abortion. A comparison shows that these records are significantly different from references to abortion in Chinese printed medical texts of pre-modern times. For example, the percentage of recipes recommended for artificial abortions in handwritten texts is significantly higher than those in printed medical books. Authors of handwritten texts used 25 terms to designate artificial abortion, with the term da tai [see text], lit.: "to strike the fetus", occurring most frequently. Its meaning is well defined, in contrast to other terms used, such as duo tai [see text], lit: "to make a fetus fall", xia tai [see text], lit. "to bring a fetus down", und duan chan [see text], lit., to interrupt birthing", which is mostly used to indicate a temporary or permanent sterilization. Pre-modern Chinese medicine has not generally abstained from inducing abortions; physicians showed a differentiating attitude. While abortions were descibed as "things a [physician with an attitude of] humaneness will not do", in case a pregnancy was seen as too risky for a woman she was offered medication to terminate this pregnancy. The commercial application of abortifacients has been recorded in China since ancient times. A request for such services has continued over time for various reasons, including so-called illegitimate pregnancies, and those by nuns, widows and prostitutes. In general, recipes to induce abortions documented in printed medical literature have mild effects and are to be ingested orally. In comparison, those recommended in handwritten texts are rather toxic. Possibly to minimize the negative side-effects of such medication, practitioners of folk medicine developed mechanical devices to perform "external", i.e., vaginal approaches.

Script-independent text line segmentation in freestyle handwritten documents.

Science.gov (United States)

Li, Yi; Zheng, Yefeng; Doermann, David; Jaeger, Stefan; Li, Yi

2008-08-01

Text line segmentation in freestyle handwritten documents remains an open document analysis problem. Curvilinear text lines and small gaps between neighboring text lines present a challenge to algorithms developed for machine printed or hand-printed documents. In this paper, we propose a novel approach based on density estimation and a state-of-the-art image segmentation technique, the level set method. From an input document image, we estimate a probability map, where each element represents the probability that the underlying pixel belongs to a text line. The level set method is then exploited to determine the boundary of neighboring text lines by evolving an initial estimate. Unlike connected component based methods ( [1], [2] for example), the proposed algorithm does not use any script-specific knowledge. Extensive quantitative experiments on freestyle handwritten documents with diverse scripts, such as Arabic, Chinese, Korean, and Hindi, demonstrate that our algorithm consistently outperforms previous methods [1]-[3]. Further experiments show the proposed algorithm is robust to scale change, rotation, and noise.

Are Malaysian Students Ready to Be Authors of Digital Contents? A Case Study of Digital Library Stakeholders’ Readiness

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Abrizah Abdullah

2007-09-01

Full Text Available The paper reports on a study that ascertains the factors facilitating students to utilize digital libraries for educational purposes. The study investigates students ICT readiness, usage of online resources and information seeking behaviour of secondary school students with the specific goal of applying the results to the design of a collaborative digital library for school projects. The digital library has been conceived to support resource needs of these students as well provide the space for them to publish their school projects, which are currently submitted handwritten. The study uses the case study approach and an urban secondary school in Malaysia is chosen as the case school. Findings from a survey and focus group interviews indicate that the students are ready to collaboratively build the digital library resources as evidenced by students digital library readiness score of 31.4/40.

Marker Registration Technique for Handwritten Text Marker in Augmented Reality Applications

Science.gov (United States)

Thanaborvornwiwat, N.; Patanukhom, K.

2018-04-01

Marker registration is a fundamental process to estimate camera poses in marker-based Augmented Reality (AR) systems. We developed AR system that creates correspondence virtual objects on handwritten text markers. This paper presents a new method for registration that is robust for low-content text markers, variation of camera poses, and variation of handwritten styles. The proposed method uses Maximally Stable Extremal Regions (MSER) and polygon simplification for a feature point extraction. The experiment shows that we need to extract only five feature points per image which can provide the best registration results. An exhaustive search is used to find the best matching pattern of the feature points in two images. We also compared performance of the proposed method to some existing registration methods and found that the proposed method can provide better accuracy and time efficiency.

Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

Science.gov (United States)

Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

2015-01-01

A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

Influence of the signer's psychophysiological state on the results of his identification using handwritten pattern by natural and artificial intelligence

Directory of Open Access Journals (Sweden)

Alexey E. Sulavko

2017-11-01

Full Text Available At present, while various mechanisms to ensure information security are actively being improved, particular attention is paid to prevent unauthorized access to information resources.  The human factor and process of identification still remain the most problematic, as well as user authentication. A progress in the technology of information resources protection from internal security threats paves its way towards biometric systems of hidden identification of computer users and their psychophysiological state. A change in psychophysiological state results in the person's handwriting. The influence of the signer’s state of fatigue and excitation on the results of its identification both by a person and by pattern recognition methods on reproduced signatures are studied. Capabilities of human and artificial intelligence are compared in equal conditions. When the state of the signer changes, the probability of erroneous recognition by artificial intelligence increases by factor 3.3 to 3.7. A person identifies a handwritten image with fewer errors in case when the signer is agitated, and with higher error rate if the signer is tired.

Robust Digital Speech Watermarking For Online Speaker Recognition

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Mohammad Ali Nematollahi

2015-01-01

Full Text Available A robust and blind digital speech watermarking technique has been proposed for online speaker recognition systems based on Discrete Wavelet Packet Transform (DWPT and multiplication to embed the watermark in the amplitudes of the wavelet’s subbands. In order to minimize the degradation effect of the watermark, these subbands are selected where less speaker-specific information was available (500 Hz–3500 Hz and 6000 Hz–7000 Hz. Experimental results on Texas Instruments Massachusetts Institute of Technology (TIMIT, Massachusetts Institute of Technology (MIT, and Mobile Biometry (MOBIO show that the degradation for speaker verification and identification is 1.16% and 2.52%, respectively. Furthermore, the proposed watermark technique can provide enough robustness against different signal processing attacks.

Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

Directory of Open Access Journals (Sweden)

Jiangyi Qin

Full Text Available A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

New Digital Approach to CNN On-chip Implementation for Pattern Recognition

OpenAIRE

Durackova, Daniela

2008-01-01

We developed a novel simulator for the CNN using the program tool Visual Basic for Application. Its algorithm is based on the same principle as the planned designed circuit. The network can process the patterns with 400 point recognition. The created universal simulator can change various simulation parameters. We found that the rounding at multiplication is not as important as we previously expected. On the basis of the simulations we designed a novel digital CNN cell implemented on a chip. ...

Device of Definition of Hand-Written Documents Belonging to One Executor

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S. D. Kulik

2012-03-01

Full Text Available Results of working out of the device of definition of hand-written documents belonging to the executor of the text in Russian are presented. The device is intended for automation of work of experts and allows to solve problems of information security and search of criminals.

A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.

Science.gov (United States)

Zhang, Yong; Li, Peng; Jin, Yingyezhe; Choe, Yoonsuck

2015-11-01

This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression. The proposed learning rule is local such that each synaptic weight update is based only upon the firing activities of the corresponding presynaptic and postsynaptic neurons without incurring global communications across the neural network. Compared with the backpropagation-based learning, the locality of computation in the proposed approach lends itself to efficient parallel VLSI implementation. We use subsets of the TI46 speech corpus to benchmark the bioinspired digital LSM. To reduce the complexity of the spiking neural network model without performance degradation for speech recognition, we study the impacts of synaptic models on the fading memory of the reservoir and hence the network performance. Moreover, we examine the tradeoffs between synaptic weight resolution, reservoir size, and recognition performance and present techniques to further reduce the overhead of hardware implementation. Our simulation results show that in terms of isolated word recognition evaluated using the TI46 speech corpus, the proposed digital LSM rivals the state-of-the-art hidden Markov-model-based recognizer Sphinx-4 and outperforms all other reported recognizers including the ones that are based upon the LSM or neural networks.

The Effects of Handwritten Feedback on Paper and Tablet PC in Learning Japanese Writing

Directory of Open Access Journals (Sweden)

Kai LI

2007-12-01

Full Text Available This paper compares the effect of paper-basedhandwritten feedback (PBHF and that of Tablet PC-basedhandwritten feedback (TBHF in learning Japanese writing.The study contributes to the research on motivation,usability and presence when learners are given differentmedia-based handwritten error feedback. The resultsindicated that there was little difference in the effect of thetwo media on motivation and usability factors. However,PBHF showed a positive effect on presence factor thanTBHF. Also, there was little difference in proficiencyimprovement after the students reviewed different mediabased handwritten feedback. The results of this studysuggest that language teachers should not use ICT withtraditional strategies, but in an innovative way to improvetheir writing instruction and enhance learners’ writingproficiency.

House officer procedure documentation using a personal digital assistant: a longitudinal study

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Lane David R

2006-01-01

Full Text Available Abstract Background Personal Digital Assistants (PDAs have been integrated into daily practice for many emergency physicians and house officers. Few objective data exist that quantify the effect of PDAs on documentation. The objective of this study was to determine whether use of a PDA would improve emergency medicine house officer documentation of procedures and patient resuscitations. Methods Twelve first-year Emergency Medicine (EM residents were provided a Palm V (Palm, Inc., Santa Clara, California, USA PDA. A customizable patient procedure and encounter program was constructed and loaded into each PDA. Residents were instructed to enter information on patients who had any of 20 procedures performed, were deemed clinically unstable, or on whom follow-up was obtained. These data were downloaded to the residency coordinator's desktop computer on a weekly basis for 36 months. The mean number of procedures and encounters performed per resident over a three year period were then compared with those of 12 historical controls from a previous residency class that had recorded the same information using a handwritten card system for 36 months. Means of both groups were compared a two-tailed Student's t test with a Bonferroni correction for multiple comparisons. One hundred randomly selected entries from both the PDA and handwritten groups were reviewed for completeness. Another group of 11 residents who had used both handwritten and PDA procedure logs for one year each were asked to complete a questionnaire regarding their satisfaction with the PDA system. Results Mean documentation of three procedures significantly increased in the PDA vs handwritten groups: conscious sedation 24.0 vs 0.03 (p = 0.001; thoracentesis 3.0 vs 0.0 (p = 0.001; and ED ultrasound 24.5 vs. 0.0 (p = 0.001. In the handwritten cohort, only the number of cardioversions/defibrillations (26.5 vs 11.5 was statistically increased (p = 0.001. Of the PDA entries, 100% were entered

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

Science.gov (United States)

Juday, Richard D. (Editor)

1988-01-01

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

Students' Perceived Preference for Visual and Auditory Assessment with E-Handwritten Feedback

Science.gov (United States)

Crews, Tena B.; Wilkinson, Kelly

2010-01-01

Undergraduate business communication students were surveyed to determine their perceived most effective method of assessment on writing assignments. The results indicated students' preference for a process that incorporates visual, auditory, and e-handwritten presentation via a tablet PC. Students also identified this assessment process would…

Data set for Tifinagh handwriting character recognition

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Omar Bencharef

2015-09-01

Full Text Available The Tifinagh alphabet-IRCAM is the official alphabet of the Amazigh language widely used in North Africa [1]. It includes thirty-one basic letter and two letters each composed of a base letter followed by the sign of labialization. Normalized only in 2003 (Unicode [2], ICRAM-Tifinagh is a young character repertoire. Which needs more work on all levels. In this context we propose a data set for handwritten Tifinagh characters composed of 1376 image; 43 Image For Each character. The dataset can be used to train a Tifinagh character recognition system, or to extract the meaning characteristics of each character.

The Comparison of Typed and Handwritten Essays of Iranian EFL Students in terms of Length, Spelling, and Grammar

Directory of Open Access Journals (Sweden)

Behrouz Sarbakhshian

2016-11-01

Full Text Available This study attempted to compare typed and handwritten essays of Iranian EFL students in terms of length, spelling, and grammar. To administer the study, the researchers utilized Alice Touch Typing Tutor software to select 15 upper intermediate students with higher ability to write two essays: one typed and the other handwritten. The students were both males and females between the ages of 22 to 35. The analyses of the students’ scores in the three mentioned criteria through three paired samples t-tests indicate that typed essays are significantly better than handwritten ones in terms of length of texts and grammatical mistakes, but not significantly different in spelling mistakes. Positive effects of typing can provide a logical reason for students, especially TOEFL applicants, to spend more time on acquiring typing skill and also for teachers to encourage their students with higher typing ability to choose typed format in their essays.

Comparing Postsecondary Marketing Student Performance on Computer-Based and Handwritten Essay Tests

Science.gov (United States)

Truell, Allen D.; Alexander, Melody W.; Davis, Rodney E.

2004-01-01

The purpose of this study was to determine if there were differences in postsecondary marketing student performance on essay tests based on test format (i.e., computer-based or handwritten). Specifically, the variables of performance, test completion time, and gender were explored for differences based on essay test format. Results of the study…

A comparative study between MFCC and LSF coefficients in automatic recognition of isolated digits pronounced in Portuguese and English - doi: 10.4025/actascitechnol.v35i4.19825

Directory of Open Access Journals (Sweden)

Diego Furtado Silva

2013-10-01

Full Text Available Recognition of isolated spoken digits is the core procedure for a large number of applications which rely solely on speech for data exchange, as in telephone-based services, such as dialing, airline reservation, bank transaction and price quotation. Spoken digit recognition is generally a challenging task since the signals last for a short period of time and often some digits are acoustically very similar to other digits. The objective of this paper is to investigate the use of machine learning algorithms for spoken digit recognition and disclose the free availability of a database with digits pronounced in English and Portuguese to the scientific community. Since machine learning algorithms are fully dependent on predictive attributes to build precise classifiers, we believe that the most important task for successfully recognizing spoken digits is feature extraction. In this work, we show that Line Spectral Frequencies (LSF provide a set of highly predictive coefficients. We evaluated our classifiers in different settings by altering the sampling rate to simulate low quality channels and varying the number of coefficients.  

Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification.

Science.gov (United States)

Pereira, Clayton R; Pereira, Danilo R; Rosa, Gustavo H; Albuquerque, Victor H C; Weber, Silke A T; Hook, Christian; Papa, João P

2018-04-16

Parkinson's disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering process of its development is not fully understood yet. In this work, we introduce convolutional neural networks to learn features from images produced by handwritten dynamics, which capture different information during the individual's assessment. Additionally, we make available a dataset composed of images and signal-based data to foster the research related to computer-aided PD diagnosis. The proposed approach was compared against raw data and texture-based descriptors, showing suitable results, mainly in the context of early stage detection, with results nearly to 95%. The analysis of handwritten dynamics using deep learning techniques showed to be useful for automatic Parkinson's disease identification, as well as it can outperform handcrafted features. Copyright © 2018 Elsevier B.V. All rights reserved.

Corticospinal excitability during the processing of handwritten and typed words and non-words.

Science.gov (United States)

Gordon, Chelsea L; Spivey, Michael J; Balasubramaniam, Ramesh

2017-06-09

A number of studies have suggested that perception of actions is accompanied by motor simulation of those actions. To further explore this proposal, we applied Transcranial magnetic stimulation (TMS) to the left primary motor cortex during the observation of handwritten and typed language stimuli, including words and non-word consonant clusters. We recorded motor-evoked potentials (MEPs) from the right first dorsal interosseous (FDI) muscle to measure cortico-spinal excitability during written text perception. We observed a facilitation in MEPs for handwritten stimuli, regardless of whether the stimuli were words or non-words, suggesting potential motor simulation during observation. We did not observe a similar facilitation for the typed stimuli, suggesting that motor simulation was not occurring during observation of typed text. By demonstrating potential simulation of written language text during observation, these findings add to a growing literature suggesting that the motor system plays a strong role in the perception of written language. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantum Digital Signatures for Unconditional Safe Authenticity Protection of Medical Documentation

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Arkadiusz Liber

2015-12-01

Full Text Available Modern medical documentation appears most often in an online form which requires some digital methods to ensure its confidentiality, integrity and authenticity. The document authenticity may be secured with the use of a signature. A classical handwritten signature is directly related to its owner by his/her psychomotor character traits. Such a signature is also connected with the material it is written on, and a writing tool. Because of these properties, a handwritten signature reflects certain close material bonds between the owner and the document. In case of modern digital signatures, the document authentication has a mathematical nature. The verification of the authenticity becomes the verification of a key instead of a human. Since 1994 it has been known that classical digital signature algorithms may not be safe because of the Shor’s factorization algorithm. To implement the modern authenticity protection of medical data, some new types of algorithms should be used. One of the groups of such algorithms is based on the quantum computations. In this paper, the analysis of the current knowledge status of Quantum Digital Signature protocols, with its basic principles, phases and common elements such as transmission, comparison and encryption, was outlined. Some of the most promising protocols for signing digital medical documentation, that fulfill the requirements for QDS, were also briefly described. We showed that, a QDS protocol with QKD components requires the equipment similar to the equipment used for a QKD, for its implementation, which is already commercially available. If it is properly implemented, it provides the shortest lifetime of qubits in comparison to other protocols. It can be used not only to sign classical messages but probably it could be well adopted to implement unconditionally safe protection of medical documentation in the nearest future, as well.

Glyph Identification and Character Recognition for Sindhi OCR

Directory of Open Access Journals (Sweden)

NISAR AHMEDMEMON

2017-10-01

Full Text Available A computer can read and write multiple languages and today?s computers are capable of understanding various human languages. A computer can be given instructions through various input methods but OCR (Optical Character Recognition and handwritten character recognition are the input methods in which a scanned page containing text is converted into written or editable text. The change in language text available on scanned page demands different algorithm to recognize text because every language and script pose varying number of challenges to recognize text. The Latin language recognition pose less difficulties compared to Arabic script and languages that use Arabic script for writing and OCR systems for these Latin languages are near to perfection. Very little work has been done on regional languages of Pakistan. In this paper the Sindhi glyphs are identified and the number of characters and connected components are identified for this regional language of Pakistan. A graphical user interface has been created to perform identification task for glyphs and characters of Sindhi language. The glyphs of characters are successfully identified from scanned page and this information can be used to recognize characters. The language glyph identification can be used to apply suitable algorithm to identify language as well as to achieve a higher recognition rate.

High-Performance Neural Networks for Visual Object Classification

OpenAIRE

Cireşan, Dan C.; Meier, Ueli; Masci, Jonathan; Gambardella, Luca M.; Schmidhuber, Jürgen

2011-01-01

We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better ...

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

Science.gov (United States)

Chidananda, H.; Reddy, T. Hanumantha

2017-06-01

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

Spotting handwritten words and REGEX using a two stage BLSTM-HMM architecture

Science.gov (United States)

Bideault, Gautier; Mioulet, Luc; Chatelain, Clément; Paquet, Thierry

2015-01-01

In this article, we propose a hybrid model for spotting words and regular expressions (REGEX) in handwritten documents. The model is made of the state-of-the-art BLSTM (Bidirectional Long Short Time Memory) neural network for recognizing and segmenting characters, coupled with a HMM to build line models able to spot the desired sequences. Experiments on the Rimes database show very promising results.

HWNet v2: An Efficient Word Image Representation for Handwritten Documents

OpenAIRE

Krishnan, Praveen; Jawahar, C. V.

2018-01-01

We present a framework for learning efficient holistic representation for handwritten word images. The proposed method uses a deep convolutional neural network with traditional classification loss. The major strengths of our work lie in: (i) the efficient usage of synthetic data to pre-train a deep network, (ii) an adapted version of ResNet-34 architecture with region of interest pooling (referred as HWNet v2) which learns discriminative features with variable sized word images, and (iii) rea...

Recognition of Handwriting from Electromyography

Science.gov (United States)

Linderman, Michael; Lebedev, Mikhail A.; Erlichman, Joseph S.

2009-01-01

Handwriting – one of the most important developments in human culture – is also a methodological tool in several scientific disciplines, most importantly handwriting recognition methods, graphology and medical diagnostics. Previous studies have relied largely on the analyses of handwritten traces or kinematic analysis of handwriting; whereas electromyographic (EMG) signals associated with handwriting have received little attention. Here we show for the first time, a method in which EMG signals generated by hand and forearm muscles during handwriting activity are reliably translated into both algorithm-generated handwriting traces and font characters using decoding algorithms. Our results demonstrate the feasibility of recreating handwriting solely from EMG signals – the finding that can be utilized in computer peripherals and myoelectric prosthetic devices. Moreover, this approach may provide a rapid and sensitive method for diagnosing a variety of neurogenerative diseases before other symptoms become clear. PMID:19707562

Digital Note-Taking: Discussion of Evidence and Best Practices.

Science.gov (United States)

Grahame, Jason A

2016-03-01

Balancing active course engagement and comprehension with producing quality lecture notes is challenging. Although evidence suggests that handwritten note-taking may improve comprehension and learning outcomes, many students still self-report a preference for digital note-taking and a belief that it is beneficial. Future research is warranted to determine the effects on performance of digitally writing notes. Independent of the methods or software chosen, best practices should be provided to students with information to help them consciously make an educated decision based on the evidence and their personal preference. Optimal note-taking requires self-discipline, focused attention, sufficient working memory, thoughtful rewording, and decreased distractions. Familiarity with the tools and mediums they choose will help students maximize working memory, produce better notes, and aid in their retention of material presented.

The Proximate Unit in Chinese Handwritten Character Production

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Jenn-Yeu eChen

2013-08-01

Full Text Available In spoken word production, a proximate unit is the first phonological unit at the sublexical level that is selectable for production (O’Seaghdha, Chen, & Chen, 2010. The present study investigated whether the proximate unit in Chinese handwritten word production is the stroke, the radical, or something in between. A written version of the form preparation task was adopted. Chinese participants learned sets of two-character words, later were cued with the first character of each word, and had to write down the second character (the target. Response times were measured from the onset of a cue character to the onset of a written response. In Experiment 1, the target characters within a block shared (homogeneous or did not share (heterogeneous the first stroke. In Experiment 2, the first two strokes were shared in the homogeneous blocks. Response times in the homogeneous blocks and in the heterogeneous blocks were comparable in both experiments (Exp. 1: 687 ms vs. 684 ms, Exp. 2: 717 vs. 716. In Experiment 3 and 4, the target characters within a block shared or did not share the first radical. Response times in the homogeneous blocks were significantly faster than those in the heterogeneous blocks (Exp. 3: 685 vs. 704, Exp. 4: 594 vs. 650. In Experiment 5 and 6, the shared component was a Gestalt-like form that is more than a stroke, constitutes a portion of the target character, can be a stand-alone character itself, can be a radical of another character but is not a radical of the target character (e.g., 士in聲, 鼓, 穀, 款; called a logographeme. Response times in the homogeneous blocks were significantly faster than those in the heterogeneous blocks (Exp. 5: 576 vs. 625, Exp. 6: 586 vs. 620. These results suggest a model of Chinese handwritten character production in which the stroke is not a functional unit, the radical plays the role of a morpheme, and the logographeme is the proximate unit.

Auditory Modeling for Noisy Speech Recognition

National Research Council Canada - National Science Library

2000-01-01

... digital filtering for noise cancellation which interfaces to speech recognition software. It uses auditory features in speech recognition training, and provides applications to multilingual spoken language translation...

Digitization of Full-Text Documents Before Publishing on the Internet: A Case Study Reviewing the Latest Optical Character Recognition Technologies.

Science.gov (United States)

McClean, Clare M.

1998-01-01

Reviews strengths and weaknesses of five optical character recognition (OCR) software packages used to digitize paper documents before publishing on the Internet. Outlines options available and stages of the conversion process. Describes the learning experience of Eurotext, a United Kingdom-based electronic libraries project (eLib). (PEN)

Digital speech processing using Matlab

CERN Document Server

Gopi, E S

2014-01-01

Digital Speech Processing Using Matlab deals with digital speech pattern recognition, speech production model, speech feature extraction, and speech compression. The book is written in a manner that is suitable for beginners pursuing basic research in digital speech processing. Matlab illustrations are provided for most topics to enable better understanding of concepts. This book also deals with the basic pattern recognition techniques (illustrated with speech signals using Matlab) such as PCA, LDA, ICA, SVM, HMM, GMM, BPN, and KSOM.

Unsupervised Learning of Digit Recognition Using Spike-Timing-Dependent Plasticity

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Peter U. Diehl

2015-08-01

Full Text Available In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns, since most of such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e. conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.

ALGORITMO PARA O RECONHECIMENTO DE CARACTERES MANUSCRITOS

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Rafael Arthur Rocha Miranda

2013-12-01

Full Text Available The handwritten character recognition in digital images is an important and challenging area of study in Computer Vision, with several possibilities for applications to facilitate the daily work of the people. This paper presents an algorithm for handwritten character recognition with two proposed approaches. The first proposal complements earlier work by some of the authors of this article, including 290 new attributes, based on histograms, Zoning and transformed Hit-or-Miss. The second proposal uses 79 attributes, obtained from frequency information, distance-edge character and densities, which performs classification using an approach based on maximum and minimum values of each attribute for each character type, and a neural network Multilayer Perceptron. The large number of attributes contributes to a more precise discrimination of characters, on the other hand, the extraction of these descriptors is easy because only performs the pixels counting. Thus, the processing time in this task is reduced. Although the classification using a Multilayer Perceptron neural network achieved a higher hit rate, the processing time of the maximum and minimum limits based classification is smaller, allowing its use in applications where the processing time is critical.

Diverse spike-timing-dependent plasticity based on multilevel HfO x memristor for neuromorphic computing

Science.gov (United States)

Lu, Ke; Li, Yi; He, Wei-Fan; Chen, Jia; Zhou, Ya-Xiong; Duan, Nian; Jin, Miao-Miao; Gu, Wei; Xue, Kan-Hao; Sun, Hua-Jun; Miao, Xiang-Shui

2018-06-01

Memristors have emerged as promising candidates for artificial synaptic devices, serving as the building block of brain-inspired neuromorphic computing. In this letter, we developed a Pt/HfO x /Ti memristor with nonvolatile multilevel resistive switching behaviors due to the evolution of the conductive filaments and the variation in the Schottky barrier. Diverse state-dependent spike-timing-dependent-plasticity (STDP) functions were implemented with different initial resistance states. The measured STDP forms were adopted as the learning rule for a three-layer spiking neural network which achieves a 75.74% recognition accuracy for MNIST handwritten digit dataset. This work has shown the capability of memristive synapse in spiking neural networks for pattern recognition application.

Digital Technologies for Social Innovation: An Empirical Recognition on the New Enablers

Directory of Open Access Journals (Sweden)

Riccardo Maiolini

2016-12-01

Full Text Available Even though scholars’ attention has been placed on Social Innovation (SI, little evidence has been provided with regards to which tools are actually used to address social needs and foster Social Innovation initiatives. The purpose of the article is twofold. Firstly, the article offers empirical recognition to SI by investigating, on a large-scale, social and innovative activities conducted by start-ups and small and medium-sized enterprises (SMEs across the world between 2001 and 2014. Secondly, the article intends to capture SI core businesses and underlying complementarities between products, markets, and technologies and show in which way digital media and IT are essentially tracing innovation trajectories over a multitude of industries, leading the current industrial patterns of SI, and continually fostering its cross-industry nature.

Evaluation of Speech Recognition of Cochlear Implant Recipients Using Adaptive, Digital Remote Microphone Technology and a Speech Enhancement Sound Processing Algorithm.

Science.gov (United States)

Wolfe, Jace; Morais, Mila; Schafer, Erin; Agrawal, Smita; Koch, Dawn

2015-05-01

Cochlear implant recipients often experience difficulty with understanding speech in the presence of noise. Cochlear implant manufacturers have developed sound processing algorithms designed to improve speech recognition in noise, and research has shown these technologies to be effective. Remote microphone technology utilizing adaptive, digital wireless radio transmission has also been shown to provide significant improvement in speech recognition in noise. There are no studies examining the potential improvement in speech recognition in noise when these two technologies are used simultaneously. The goal of this study was to evaluate the potential benefits and limitations associated with the simultaneous use of a sound processing algorithm designed to improve performance in noise (Advanced Bionics ClearVoice) and a remote microphone system that incorporates adaptive, digital wireless radio transmission (Phonak Roger). A two-by-two way repeated measures design was used to examine performance differences obtained without these technologies compared to the use of each technology separately as well as the simultaneous use of both technologies. Eleven Advanced Bionics (AB) cochlear implant recipients, ages 11 to 68 yr. AzBio sentence recognition was measured in quiet and in the presence of classroom noise ranging in level from 50 to 80 dBA in 5-dB steps. Performance was evaluated in four conditions: (1) No ClearVoice and no Roger, (2) ClearVoice enabled without the use of Roger, (3) ClearVoice disabled with Roger enabled, and (4) simultaneous use of ClearVoice and Roger. Speech recognition in quiet was better than speech recognition in noise for all conditions. Use of ClearVoice and Roger each provided significant improvement in speech recognition in noise. The best performance in noise was obtained with the simultaneous use of ClearVoice and Roger. ClearVoice and Roger technology each improves speech recognition in noise, particularly when used at the same time

Speech Recognition on Mobile Devices

DEFF Research Database (Denmark)

Tan, Zheng-Hua; Lindberg, Børge

2010-01-01

in the mobile context covering motivations, challenges, fundamental techniques and applications. Three ASR architectures are introduced: embedded speech recognition, distributed speech recognition and network speech recognition. Their pros and cons and implementation issues are discussed. Applications within......The enthusiasm of deploying automatic speech recognition (ASR) on mobile devices is driven both by remarkable advances in ASR technology and by the demand for efficient user interfaces on such devices as mobile phones and personal digital assistants (PDAs). This chapter presents an overview of ASR...

Static human face recognition using artificial neural networks

International Nuclear Information System (INIS)

Qamar, R.; Shah, S.H.; Javed-ur-Rehman

2003-01-01

This paper presents a novel method of human face recognition using digital computers. A digital PC camera is used to take the BMP images of the human faces. An artificial neural network using Back Propagation Algorithm is developed as a recognition engine. The BMP images of the faces serve as the input patterns for this engine. A software 'Face Recognition' has been developed to recognize the human faces for which it is trained. Once the neural network is trained for patterns of the faces, the software is able to detect and recognize them with success rate of about 97%. (author)

Journal of EEA, Vol. 27, 2010 WRITER IDENTIFICATION SYSTEM ...

African Journals Online (AJOL)

messy

Two approaches have been employed for feature extraction from the handwritten images: texture ... gait, keystroke dynamics, signature, handwriting). Identifying the writer of a handwritten sample using automatic image-based methods is an interesting pattern recognition problem with a wide variety of applications including ...

Eye movement analysis for activity recognition using electrooculography.

Science.gov (United States)

Bulling, Andreas; Ward, Jamie A; Gellersen, Hans; Tröster, Gerhard

2011-04-01

In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.

Study on municipal road cracking and surface deformation based on image recognition

Science.gov (United States)

Yuan, Haitao; Wang, Shuai; Tan, Jizong

2017-05-01

In recent years, the digital image recognition technology of concrete structure cracks and deformation of binocular vision technology detection of civil engineering structure have made substantial development. As a result, people's understanding of the road engineering structure cracking and surface deformation recognition gives rise to a new situation. For the research on digital image concrete structure cracking and masonry structure surface deformation recognition technology, the key is to break through in the method, and to improve the traditional recognition technology and mode. Only in this way can we continuously improve the security level of the highway, to adapt to the new requirements of the development of new urbanization and modernization. This thesis focuses on and systematically analyzes the digital image road engineering structure cracking and key technologies of surface deformation recognition and its engineering applications. In addition, we change the concrete structure cracking and masonry structure surface deformation recognition pattern, and realize the breakthrough and innovation of the road structure safety testing means and methods.

Ancient administrative handwritten documents: X-ray analysis and imaging

International Nuclear Information System (INIS)

Albertin, F.; Astolfo, A.; Stampanoni, M.; Peccenini, Eva; Hwu, Y.; Kaplan, F.; Margaritondo, G.

2015-01-01

The heavy-element content of ink in ancient administrative documents makes it possible to detect the characters with different synchrotron imaging techniques, based on attenuation or refraction. This is the first step in the direction of non-interactive virtual X-ray reading. Handwritten characters in administrative antique documents from three centuries have been detected using different synchrotron X-ray imaging techniques. Heavy elements in ancient inks, present even for everyday administrative manuscripts as shown by X-ray fluorescence spectra, produce attenuation contrast. In most cases the image quality is good enough for tomography reconstruction in view of future applications to virtual page-by-page ‘reading’. When attenuation is too low, differential phase contrast imaging can reveal the characters from refractive index effects. The results are potentially important for new information harvesting strategies, for example from the huge Archivio di Stato collection, objective of the Venice Time Machine project

Ancient administrative handwritten documents: X-ray analysis and imaging

Energy Technology Data Exchange (ETDEWEB)

Albertin, F., E-mail: fauzia.albertin@epfl.ch [Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne (Switzerland); Astolfo, A. [Paul Scherrer Institut (PSI), Villigen (Switzerland); Stampanoni, M. [Paul Scherrer Institut (PSI), Villigen (Switzerland); ETHZ, Zürich (Switzerland); Peccenini, Eva [University of Ferrara (Italy); Technopole of Ferrara (Italy); Hwu, Y. [Academia Sinica, Taipei, Taiwan (China); Kaplan, F. [Ecole Polytechnique Fédérale de Lausanne (EPFL) (Switzerland); Margaritondo, G. [Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne (Switzerland)

2015-01-30

The heavy-element content of ink in ancient administrative documents makes it possible to detect the characters with different synchrotron imaging techniques, based on attenuation or refraction. This is the first step in the direction of non-interactive virtual X-ray reading. Handwritten characters in administrative antique documents from three centuries have been detected using different synchrotron X-ray imaging techniques. Heavy elements in ancient inks, present even for everyday administrative manuscripts as shown by X-ray fluorescence spectra, produce attenuation contrast. In most cases the image quality is good enough for tomography reconstruction in view of future applications to virtual page-by-page ‘reading’. When attenuation is too low, differential phase contrast imaging can reveal the characters from refractive index effects. The results are potentially important for new information harvesting strategies, for example from the huge Archivio di Stato collection, objective of the Venice Time Machine project.

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.

Faith in the net: towards the creation of digital networks of religious acknowledgement and recognition

Directory of Open Access Journals (Sweden)

Luis Ignacio SIERRA GUTIÉRREZ

2017-12-01

Full Text Available Undoubtedly, the technological revolution in information and communications causing major changes in citizens’ social interactions. The new reticular rationality offers the possibility of shaping, developing, and strengthening social networks and virtual communities. All of them facilitate the creation of new interactive spaces, new social collectives promoting citizenship and that, from different social fields and levels of experience, articulate and streamline processes of production, circulation and appropriation of new symbolic products. Such products contribute not only to generating new sources of knowledge but, above all, to strengthening processes of citizen interaction. In such processes, the field of media, religiosities and socio-cultural processes are strategically intertwined. In this context, experiences of civic religiosity find in the potential generated by the global network, new possibilities of interaction and religious recognition. Also new forms and spaces to share plural options of faith and socio-religious practices that make sense of the existence of cybernauts. This text is divided into three parts: First, critically contextualizes the global phenomenon of social networks. Second, it makes an approximation to some experiences of digital networks of religious recognition from Latin America. Finally, raises some questions that arise from such virtual practices.

Automatic speech recognition for report generation in computed tomography

International Nuclear Information System (INIS)

Teichgraeber, U.K.M.; Ehrenstein, T.; Lemke, M.; Liebig, T.; Stobbe, H.; Hosten, N.; Keske, U.; Felix, R.

1999-01-01

Purpose: A study was performed to compare the performance of automatic speech recognition (ASR) with conventional transcription. Materials and Methods: 100 CT reports were generated by using ASR and 100 CT reports were dictated and written by medical transcriptionists. The time for dictation and correction of errors by the radiologist was assessed and the type of mistakes was analysed. The text recognition rate was calculated in both groups and the average time between completion of the imaging study by the technologist and generation of the written report was assessed. A commercially available speech recognition technology (ASKA Software, IBM Via Voice) running of a personal computer was used. Results: The time for the dictation using digital voice recognition was 9.4±2.3 min compared to 4.5±3.6 min with an ordinary Dictaphone. The text recognition rate was 97% with digital voice recognition and 99% with medical transcriptionists. The average time from imaging completion to written report finalisation was reduced from 47.3 hours with medical transcriptionists to 12.7 hours with ASR. The analysis of misspellings demonstrated (ASR vs. medical transcriptionists): 3 vs. 4 for syntax errors, 0 vs. 37 orthographic mistakes, 16 vs. 22 mistakes in substance and 47 vs. erroneously applied terms. Conclusions: The use of digital voice recognition as a replacement for medical transcription is recommendable when an immediate availability of written reports is necessary. (orig.) [de

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

Enhancement and character recognition of the erased colophon of a 15th-century Hebrew prayer book

Science.gov (United States)

Walvoord, Derek J.; Easton, Roger L., Jr.; Knox, Keith T.; Heimbueger, Matthew

2005-01-01

A handwritten codex often included an inscription that listed facts about its publication, such as the names of the scribe and patron, date of publication, the city where the book was copied, etc. These facts obviously provide essential information to a historian studying the provenance of the codex. Unfortunately, this page was sometimes erased after the sale of the book to a new owner, often by scraping off the original ink. The importance of recovering this information would be difficult to overstate. This paper reports on the methods of imaging, image enhancement, and character recognition that were applied to this page in a Hebrew prayer book copied in Florence in the 15th century.

Digitized mammograms

International Nuclear Information System (INIS)

Bruneton, J.N.; Balu-Maestro, C.; Rogopoulos, A.; Chauvel, C.; Geoffray, A.

1988-01-01

Two observers conducted a blind evaluation of 100 mammography files, including 47 malignant cases. Films were read both before and after image digitization at 50 μm and 100 μm with the FilmDRSII. Digitization permitted better analysis of the normal anatomic structures and moderately improved diagnostic sensitivity. Searches for microcalcifications before and after digitization at 100 μm and 50 μm showed better analysis of anatomic structures after digitization (especially for solitary microcalcifications). The diagnostic benefit, with discovery of clustered microcalcifications, was more limited (one case at 100 μm, nine cases at 50 μm). Recognition of microcalcifications was clearly improved in dense breasts, which can benefit from reinterpretation after digitization at 50 μm rather 100μm

IMAGE TYPE WATER METER CHARACTER RECOGNITION BASED ON EMBEDDED DSP

OpenAIRE

LIU Ying; HAN Yan-bin; ZHANG Yu-lin

2015-01-01

In the paper, we combined DSP processor with image processing algorithm and studied the method of water meter character recognition. We collected water meter image through camera at a fixed angle, and the projection method is used to recognize those digital images. The experiment results show that the method can recognize the meter characters accurately and artificial meter reading is replaced by automatic digital recognition, which improves working efficiency.

Improving the delivery of care and reducing healthcare costs with the digitization of information.

Science.gov (United States)

Noffsinger, R; Chin, S

2000-01-01

In the coming years, the digitization of information and the Internet will be extremely powerful in reducing healthcare costs while assisting providers in the delivery of care. One example of healthcare inefficiency that can be managed through information digitization is the process of prescription writing. Due to the handwritten and verbal communication surrounding prescription writing, as well as the multiple tiers of authorizations, the prescription drug process causes extensive financial waste as well as medical errors, lost time, and even fatal accidents. Electronic prescription management systems are being designed to address these inefficiencies. By utilizing new electronic prescription systems, physicians not only prescribe more accurately, but also improve formulary compliance thereby reducing pharmacy utilization. These systems expand patient care by presenting proactive alternatives at the point of prescription while reducing costs and providing additional benefits for consumers and healthcare providers.

Multi-font printed Mongolian document recognition system

Science.gov (United States)

Peng, Liangrui; Liu, Changsong; Ding, Xiaoqing; Wang, Hua; Jin, Jianming

2009-01-01

Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of projection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.

Report generation using digital speech recognition in radiology

International Nuclear Information System (INIS)

Vorbeck, F.; Ba-Ssalamah, A.; Kettenbach, J.; Huebsch, P.

2000-01-01

The aim of this study was to evaluate whether the use of a digital continuous speech recognition (CSR) in the field of radiology could lead to relevant time savings in generating a report. A CSR system (SP6000, Philips, Eindhoven, The Netherlands) for German was used to transform fluently spoken sentences into text. Two radiologists dictated a total of 450 reports on five radiological topics. Two typists edited those reports by means of conventional typing using a text editor (WinWord 6.0, Microsoft, Redmond, Wash.) installed on an IBM-compatible personal computer (PC). The same reports were generated using the CSR system and the performance of both systems was then evaluated by comparing the time needed to generate the reports and the error rates of both systems. In addition, the error rate of the CSR system and the time needed to create the reports was evaluated. The mean error rate for the CSR system was 5.5 %, and the mean error rate for conventional typing was 0.4 %. Reports edited with the CSR, on average, were generated 19 % faster compared with the conventional text-editing method. However, the amount of error rates and time savings were different and depended on topics, speakers, and typists. Using CSR the maximum time saving achieved was 28 % for the topic sonography. The CSR system was never slower, under any circumstances, than conventional typing on a PC. When compared with a conventional manual typing method, the CSR system proved to be useful in a clinical setting and saved time in generating radiological reports. The amount of time saved, however, greatly depended on the performance of the typist, the speaker, and on stored vocabulary provided by the CSR system. (orig.)

Mexican sign language recognition using normalized moments and artificial neural networks

Science.gov (United States)

Solís-V., J.-Francisco; Toxqui-Quitl, Carina; Martínez-Martínez, David; H.-G., Margarita

2014-09-01

This work presents a framework designed for the Mexican Sign Language (MSL) recognition. A data set was recorded with 24 static signs from the MSL using 5 different versions, this MSL dataset was captured using a digital camera in incoherent light conditions. Digital Image Processing was used to segment hand gestures, a uniform background was selected to avoid using gloved hands or some special markers. Feature extraction was performed by calculating normalized geometric moments of gray scaled signs, then an Artificial Neural Network performs the recognition using a 10-fold cross validation tested in weka, the best result achieved 95.83% of recognition rate.

Advanced Digital Preservation

CERN Document Server

Giaretta, David

2011-01-01

There is growing recognition of the need to address the fragility of digital information, on which our society heavily depends for smooth operation in all aspects of daily life. This has been discussed in many books and articles on digital preservation, so why is there a need for yet one more? Because, for the most part, those other publications focus on documents, images and webpages -- objects that are normally rendered to be simply displayed by software to a human viewer. Yet there are clearly many more types of digital objects that may need to be preserved, such as databases, scientific da

Anatomy of a digital coherent receiver

DEFF Research Database (Denmark)

Borkowski, Robert; Zibar, Darko; Tafur Monroy, Idelfonso

2014-01-01

, orthonormaliation, chromatic dispersion compensation/nonlinear compensation, resampling a nd timing recovery, polarization demultiplexing and equalization, frequency and phase recovery, digital demodulation. We also describe novel subsystems of a digital coherent receiver: modulation format recognition......Digital coherent receivers have gained significant attention in the last decade. The reason for this is that coherent detection, along with digital signal processing (DSP) allows for substantial increase of the channel capacity by employing advanced detection techniques. In this paper, we first...

Development and Comparative Study of Effects of Training Algorithms on Performance of Artificial Neural Network Based Analog and Digital Automatic Modulation Recognition

Directory of Open Access Journals (Sweden)

Jide Julius Popoola

2015-11-01

Full Text Available This paper proposes two new classifiers that automatically recognise twelve combined analog and digital modulated signals without any a priori knowledge of the modulation schemes and the modulation parameters. The classifiers are developed using pattern recognition approach. Feature keys extracted from the instantaneous amplitude, instantaneous phase and the spectrum symmetry of the simulated signals are used as inputs to the artificial neural network employed in developing the classifiers. The two developed classifiers are trained using scaled conjugate gradient (SCG and conjugate gradient (CONJGRAD training algorithms. Sample results of the two classifiers show good success recognition performance with an average overall recognition rate above 99.50% at signal-to-noise ratio (SNR value from 0 dB and above with the two training algorithms employed and an average overall recognition rate slightly above 99.00% and 96.40% respectively at - 5 dB SNR value for SCG and CONJGRAD training algorithms. The comparative performance evaluation of the two developed classifiers using the two training algorithms shows that the two training algorithms have different effects on both the response rate and efficiency of the two developed artificial neural networks classifiers. In addition, the result of the performance evaluation carried out on the overall success recognition rates between the two developed classifiers in this study using pattern recognition approach with the two training algorithms and one reported classifier in surveyed literature using decision-theoretic approach shows that the classifiers developed in this study perform favourably with regard to accuracy and performance probability as compared to classifier presented in previous study.

Degraded character recognition based on gradient pattern

Science.gov (United States)

Babu, D. R. Ramesh; Ravishankar, M.; Kumar, Manish; Wadera, Kevin; Raj, Aakash

2010-02-01

Degraded character recognition is a challenging problem in the field of Optical Character Recognition (OCR). The performance of an optical character recognition depends upon printed quality of the input documents. Many OCRs have been designed which correctly identifies the fine printed documents. But, very few reported work has been found on the recognition of the degraded documents. The efficiency of the OCRs system decreases if the input image is degraded. In this paper, a novel approach based on gradient pattern for recognizing degraded printed character is proposed. The approach makes use of gradient pattern of an individual character for recognition. Experiments were conducted on character image that is either digitally written or a degraded character extracted from historical documents and the results are found to be satisfactory.

Iris image enhancement for feature recognition and extraction

CSIR Research Space (South Africa)

Mabuza, GP

2012-10-01

Full Text Available the employment of other algorithms and commands so as to better present and demonstrate the obtained results. Edge detection and enhancing images for use in an iris recognition system allow for efficient recognition and extraction of iris patterns. REFERENCES... Gonzalez, R.C. and Woods, R.E. 2002. Digital Image Processing 2nd Edition, Instructor?s manual .Englewood Cliffs, Prentice Hall, pp 17-36. Proen?a, H. and Alexandre, L.A. 2007. Toward Noncooperative Iris Recognition: A classification approach using...

Digital filters

CERN Document Server

Hamming, Richard W

1997-01-01

Digital signals occur in an increasing number of applications: in telephone communications; in radio, television, and stereo sound systems; and in spacecraft transmissions, to name just a few. This introductory text examines digital filtering, the processes of smoothing, predicting, differentiating, integrating, and separating signals, as well as the removal of noise from a signal. The processes bear particular relevance to computer applications, one of the focuses of this book.Readers will find Hamming's analysis accessible and engaging, in recognition of the fact that many people with the s

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

Science.gov (United States)

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

2015-04-01

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

A Record Book of Open Heart Surgical Cases between 1959 and 1982, Hand-Written by a Cardiac Surgeon.

Science.gov (United States)

Kim, Won-Gon

2016-08-01

A book of brief records of open heart surgery underwent between 1959 and 1982 at Seoul National University Hospital was recently found. The book was hand-written by the late professor and cardiac surgeon Yung Kyoon Lee (1921-1994). This book contains valuable information about cardiac patients and surgery at the early stages of the establishment of open heart surgery in Korea, and at Seoul National University Hospital. This report is intended to analyze the content of the book.

Speech recognition implementation in radiology

International Nuclear Information System (INIS)

White, Keith S.

2005-01-01

Continuous speech recognition (SR) is an emerging technology that allows direct digital transcription of dictated radiology reports. The SR systems are being widely deployed in the radiology community. This is a review of technical and practical issues that should be considered when implementing an SR system. (orig.)

Indoor navigation by image recognition

Science.gov (United States)

Choi, Io Teng; Leong, Chi Chong; Hong, Ka Wo; Pun, Chi-Man

2017-07-01

With the progress of smartphones hardware, it is simple on smartphone using image recognition technique such as face detection. In addition, indoor navigation system development is much slower than outdoor navigation system. Hence, this research proves a usage of image recognition technique for navigation in indoor environment. In this paper, we introduced an indoor navigation application that uses the indoor environment features to locate user's location and a route calculating algorithm to generate an appropriate path for user. The application is implemented on Android smartphone rather than iPhone. Yet, the application design can also be applied on iOS because the design is implemented without using special features only for Android. We found that digital navigation system provides better and clearer location information than paper map. Also, the indoor environment is ideal for Image recognition processing. Hence, the results motivate us to design an indoor navigation system using image recognition.

Recognition and inference of crevice processing on digitized paintings

Science.gov (United States)

Karuppiah, S. P.; Srivatsa, S. K.

2013-03-01

This paper is designed to detect and removal of cracks on digitized paintings. The cracks are detected by threshold. Afterwards, the thin dark brush strokes which have been misidentified as cracks are removed using Median radial basis function neural network on hue and saturation data, Semi-automatic procedure based on region growing. Finally, crack is filled using wiener filter. The paper is well designed in such a way that most of the cracks on digitized paintings have identified and removed. The paper % of betterment is 90%. This paper helps us to perform not only on digitized paintings but also the medical images and bmp images. This paper is implemented by Mat Lab.

Reducing weight precision of convolutional neural networks towards large-scale on-chip image recognition

Science.gov (United States)

Ji, Zhengping; Ovsiannikov, Ilia; Wang, Yibing; Shi, Lilong; Zhang, Qiang

2015-05-01

In this paper, we develop a server-client quantization scheme to reduce bit resolution of deep learning architecture, i.e., Convolutional Neural Networks, for image recognition tasks. Low bit resolution is an important factor in bringing the deep learning neural network into hardware implementation, which directly determines the cost and power consumption. We aim to reduce the bit resolution of the network without sacrificing its performance. To this end, we design a new quantization algorithm called supervised iterative quantization to reduce the bit resolution of learned network weights. In the training stage, the supervised iterative quantization is conducted via two steps on server - apply k-means based adaptive quantization on learned network weights and retrain the network based on quantized weights. These two steps are alternated until the convergence criterion is met. In this testing stage, the network configuration and low-bit weights are loaded to the client hardware device to recognize coming input in real time, where optimized but expensive quantization becomes infeasible. Considering this, we adopt a uniform quantization for the inputs and internal network responses (called feature maps) to maintain low on-chip expenses. The Convolutional Neural Network with reduced weight and input/response precision is demonstrated in recognizing two types of images: one is hand-written digit images and the other is real-life images in office scenarios. Both results show that the new network is able to achieve the performance of the neural network with full bit resolution, even though in the new network the bit resolution of both weight and input are significantly reduced, e.g., from 64 bits to 4-5 bits.

Recognition of sport players' numbers using fast-color segmentation

Science.gov (United States)

Verleysen, Cédric; De Vleeschouwer, Christophe

2012-01-01

This paper builds on a prior work for player detection, and proposes an efficient and effective method to distinguish among players based on the numbers printed on their jerseys. To extract the numbers, the dominant colors of the jersey are learnt during an initial phase and used to speed up the segmentation of the candidate digit regions. An additional set of criteria, considering the relative position and size (compared to the player bounding box) and the density (compared to the digit rectangular support) of the digit, are used to filter out the regions that obviously do not correspond to a digit. Once the plausible digit regions have been extracted, their recognition is based on feature-based classification. A number of original features are proposed to increase the robustness against digit appearance changes, resulting from the font thickness variability and from the deformations of the jersey during the game. Finally, the efficiency and the effectiveness of the proposed method are demonstrated on a real-life basketball dataset. It shows that the proposed segmentation runs about ten times faster than the mean-shift algorithm, but also outlines that the proposed additional features significantly increase the digit recognition accuracy. Despite significant deformations, 40% of the samples, that can be visually recognized as digits, are well classified as numbers. Out of these classified samples, more than 80% of them are correctly recognized. Besides, more than 95% of the samples, that are not numbers, are correctly identified as non-numbers.

Extrinsic Cognitive Load Impairs Spoken Word Recognition in High- and Low-Predictability Sentences.

Science.gov (United States)

Hunter, Cynthia R; Pisoni, David B

Listening effort (LE) induced by speech degradation reduces performance on concurrent cognitive tasks. However, a converse effect of extrinsic cognitive load on recognition of spoken words in sentences has not been shown. The aims of the present study were to (a) examine the impact of extrinsic cognitive load on spoken word recognition in a sentence recognition task and (b) determine whether cognitive load and/or LE needed to understand spectrally degraded speech would differentially affect word recognition in high- and low-predictability sentences. Downstream effects of speech degradation and sentence predictability on the cognitive load task were also examined. One hundred twenty young adults identified sentence-final spoken words in high- and low-predictability Speech Perception in Noise sentences. Cognitive load consisted of a preload of short (low-load) or long (high-load) sequences of digits, presented visually before each spoken sentence and reported either before or after identification of the sentence-final word. LE was varied by spectrally degrading sentences with four-, six-, or eight-channel noise vocoding. Level of spectral degradation and order of report (digits first or words first) were between-participants variables. Effects of cognitive load, sentence predictability, and speech degradation on accuracy of sentence-final word identification as well as recall of preload digit sequences were examined. In addition to anticipated main effects of sentence predictability and spectral degradation on word recognition, we found an effect of cognitive load, such that words were identified more accurately under low load than high load. However, load differentially affected word identification in high- and low-predictability sentences depending on the level of sentence degradation. Under severe spectral degradation (four-channel vocoding), the effect of cognitive load on word identification was present for high-predictability sentences but not for low

Exhibits Recognition System for Combining Online Services and Offline Services

Science.gov (United States)

Ma, He; Liu, Jianbo; Zhang, Yuan; Wu, Xiaoyu

2017-10-01

In order to achieve a more convenient and accurate digital museum navigation, we have developed a real-time and online-to-offline museum exhibits recognition system using image recognition method based on deep learning. In this paper, the client and server of the system are separated and connected through the HTTP. Firstly, by using the client app in the Android mobile phone, the user can take pictures and upload them to the server. Secondly, the features of the picture are extracted using the deep learning network in the server. With the help of the features, the pictures user uploaded are classified with a well-trained SVM. Finally, the classification results are sent to the client and the detailed exhibition’s introduction corresponding to the classification results are shown in the client app. Experimental results demonstrate that the recognition accuracy is close to 100% and the computing time from the image uploading to the exhibit information show is less than 1S. By means of exhibition image recognition algorithm, our implemented exhibits recognition system can combine online detailed exhibition information to the user in the offline exhibition hall so as to achieve better digital navigation.

Neural network recognition of mammographic lesions

International Nuclear Information System (INIS)

Oldham, W.J.B.; Downes, P.T.; Hunter, V.

1987-01-01

A method for recognition of mammographic lesions through the use of neural networks is presented. Neural networks have exhibited the ability to learn the shape andinternal structure of patterns. Digitized mammograms containing circumscribed and stelate lesions were used to train a feedfoward synchronous neural network that self-organizes to stable attractor states. Encoding of data for submission to the network was accomplished by performing a fractal analysis of the digitized image. This results in scale invariant representation of the lesions. Results are discussed

Identification of digitized particle trajectories

CERN Document Server

Grote, H; Lassalle, J C; Zanella, P

1973-01-01

High-energy Physics Laboratories make increasing use of particle detectors which directly produce digital measurements of trajectories at very high rates. Data collected in vast amounts during experiments are then analysed by computer programs whose first task is the recognition of tracks and reconstruction of the interesting events. This paper discusses the applicability of various Pattern Recognition approaches. Examples are given of the problems and the practical achievements in this field.

Pattern recognition in high energy physics

International Nuclear Information System (INIS)

Tenner, A.G.

1980-01-01

In high energy physics experiments tracks of elementary particles are recorded by different types of equipment. Coordinates of points of these tracks have to be measured for the geometrical reconstruction and the further analysis of the observed events. Pattern recognition methods may facilitate the detection of tracks or whole events and the separation of relevant from non-relevant information. They may also serve for the automation of measurement. Generally, all work is done by digital computation. In a bubble chamber tracks appear as strings of vapour bubbles that can be recorded photographically. Two methods of pattern recognition are discussed. The flying spot digitizer encodes the pattern on the photograph into point coordinates in the memory of a computer. The computer carries out the pattern recognition procedure entirely on the basis of the stored information. Cathode ray instruments scan the photograph by means of a computer steered optical device. Data acquisition from the film is performed in a feedback loop of the computation. In electronic experimental equipment tracks are defined by the spacial distribution of hits of counters (wire counters, scintillation counters, spark chambers). Pattern recognition is generally performed in various stages both by on-line and off-line equipment. Problems in the data handling arise both from the great abundance of data and from the time limits imposed on the on-line computation by high measuring rates. The on-line computation is carried out by hardwired logic, small computers, and to an increasing extent by microprocessors. (Auth.)

Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

Science.gov (United States)

Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus

2017-01-01

Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

Description and recognition of patterns in stochastic signals. [Electroencephalograms

Energy Technology Data Exchange (ETDEWEB)

Flik, T [Technische Univ. Berlin (F.R. Germany). Informatik-Forschungsgruppe Rechnerorganisation und Schaltwerke

1975-10-01

A method is shown for the description and recognition of patterns in stochastic signals such as electroencephalograms. For pattern extraction the signal is segmented at times of minimum amplitudes. The describing features consist of geometric values of the so defined patterns. The classification algorithm is based on the regression analysis, which is well known in the field of character recognition. For an economic classification a method is proposed which reduces the number of features. The quality of this pattern recognition method is demonstrated by the detection of spike wave complexes in electroencephalograms. The pattern description and recognition are provided for processing on a digital computer. (DE)

Sadhana | Indian Academy of Sciences

Indian Academy of Sciences (India)

Further, it has also been found that increasing radial/angular resolution,with normalization in place, improves EER for proposed iris recognition system. Volume 42 Issue 5 May 2017 pp 701-712. Recognition of online handwritten Gurmukhi characters based on zone and stroke identification · KARUN VERMA R K SHARMA.

Proyectos de digitalización y nuevas perspectivas tecnológicas en el archivo histórico del Congreso de los Diputados de España: conservación de la historia del parlamentarismo y derecho constitucional español en soporte digital

Directory of Open Access Journals (Sweden)

Villarejo Sánchez, Nadia

2006-12-01

Full Text Available Which are the projects to scan handwritten documents and which are the new technologies of information and documentation of the Historic Archive of the Congress of Deputies in Spain? These questions are explained in this article. The Historic Archive of the Congress of Deputies has proposed –in some cases– and put into practice –in others–, projects to scan handwritten documents and to convert them to digital format, with the purpose to conserve antique documentary and bibliographic collections. Thus the researcher has access to the recovered and optimized digital handwritten documentation, by means of the application of new optical formats of retrieval and storage of information. The information and documentation technologies development in this governmental archive until year 2006 are analyzed.

Se explica en este artículo cuáles son los proyectos de digitalización y las nuevas tecnologías de información y documentación, que el Archivo Histórico del Congreso de los Diputados de España ha propuesto –en algunos casos– y está llevando a la práctica –en otros–, para procurar la conservación íntegra de los fondos documentales y bibliográficos antiguos que conserva en sus instalaciones, y de esta manera, presentar al investigador la documentación restaurada, digitalizada y optimizada en su disposición y forma, mediante la aplicación de nuevos soportes ópticos de almacenamiento de información. En definitiva, se analiza el estado de la cuestión que en materia de desarrollo tecnológico acontece en este archivo gubernamental, hasta el año 2006.

Applications of pattern recognition theory in diagnostics of nuclear power plants

International Nuclear Information System (INIS)

Cech, J.

1982-01-01

The questions are discussed of the application of the theory of pattern recognition in the diagnostics of nuclear power plants. For the future use of recognition systems in the diagnostics of nuclear power plants it is obvious that like with other complex systems, optimal models will have to be used which will organize the optimal recognition algorithm. The conclusion is presented that for the needs of nuclear power plants special systems will be more suitable for pattern recognition than digital computers which are flexible and adaptible but have a lower decision rate, an insufficient working memory, complicated programs, etc. (Z.M.)

Digital-forensics based pattern recognition for discovering identities in electronic evidence

NARCIS (Netherlands)

Henseler, Hans; Hofsté, Jop; van Keulen, Maurice

With the pervasiveness of computers and mobile devices, digital forensics becomes more important in law enforcement. Detectives increasingly depend on the scarce support of digital specialists which impedes efficiency of criminal investigations. This paper proposes and algorithm to extract, merge

Batch metadata assignment to archival photograph collections using facial recognition software

Directory of Open Access Journals (Sweden)

Kyle Banerjee

2013-07-01

Full Text Available Useful metadata is essential to giving individual meaning and value within the context of a greater image collection as well as making them more discoverable. However, often little information is available about the photos themselves, so adding consistent metadata to large collections of digital and digitized photographs is a time consuming process requiring highly experienced staff. By using facial recognition software, staff can identify individuals more quickly and reliably. Knowledge of individuals in photos helps staff determine when and where photos are taken and also improves understanding of the subject matter. This article demonstrates simple techniques for using facial recognition software and command line tools to assign, modify, and read metadata for large archival photograph collections.

Real object recognition using moment invariants

Indian Academy of Sciences (India)

are taken from different angles of view are the main features leading us to our objective. ... Two-dimensional moments of a digitally sampled M × M image that has gray function f (x, y), (x, .... in this paper. Information about the original colours of the objects is not used. .... multi-dimensional changes and recognition. Table 1.

Study on recognition algorithm for paper currency numbers based on neural network

Science.gov (United States)

Li, Xiuyan; Liu, Tiegen; Li, Yuanyao; Zhang, Zhongchuan; Deng, Shichao

2008-12-01

Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency. Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original paper currency images can be draw out through image processing, such as image de-noising, skew correction, segmentation, and image normalization. According to the different characteristics between digits and letters in serial number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network (RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate and faster recognition simultaneously, which is worthy of broad application prospect.

Analogies Between Digital Radio and Chemical Orthogonality as a Method for Enhanced Analysis of Molecular Recognition Events

Directory of Open Access Journals (Sweden)

Sang-Hun Lee

2008-02-01

Full Text Available Acoustic wave biosensors are a real-time, label-free biosensor technology, which have been exploited for the detection of proteins and cells. One of the conventional biosensor approaches involves the immobilization of a monolayer of antibodies onto the surface of the acoustic wave device for the detection of a specific analyte. The method described within includes at least two immobilizations of two different antibodies onto the surfaces of two separate acoustic wave devices for the detection of several analogous analytes. The chemical specificity of the molecular recognition event is achieved by virtue of the extremely high (nM to pM binding affinity between the antibody and its antigen. In a standard ELISA (Enzyme-Linked ImmunoSorbent Assay test, there are multiple steps and the end result is a measure of what is bound so tightly that it does not wash away easily. The fact that this “gold standard” is very much not real time, masks the dance that is the molecular recognition event. X-Ray Crystallographer, Ian Wilson, demonstrated more than a decade ago that antibodies undergo conformational change during a binding event[1, 2]. Further, it is known in the arena of immunochemistry that some antibodies exhibit significant cross-reactivity and this is widely termed antibody promiscuity. A third piece of the puzzle that we will exploit in our system of acoustic wave biosensors is the notion of chemical orthogonality. These three biochemical constructs, the dance, antibody promiscuity and chemical orthogonality will be combined in this paper with the notions of Int. J. Mol. Sci. 2008, 9 155 in-phase (I and quadrature (Q signals from digital radio to manifest an approach to molecular recognition that allows a level of discrimination and analysis unobtainable without the aggregate. As an example we present experimental data on the detection of TNT, RDX, C4, ammonium nitrate and musk oil from a system of antibody-coated acoustic

Construction of Graduation Certificate Issuing System Based on Digital Signature Technique

Directory of Open Access Journals (Sweden)

Mohammed Issam Younis

2015-06-01

Full Text Available With the development of computer architecture and its technologies in recent years, applications like e-commerce, e-government, e-governance and e-finance are widely used, and they act as active research areas. In addition, in order to increase the quality and quantity of the ordinary everyday transactions, it is desired to migrate from the paper-based environment to a digital-based computerized environment. Such migration increases efficiency, saves time, eliminates paperwork, increases safety and reduces the cost in an organization. Digital signatures are playing an essential role in many electronic and automatic based systems and facilitate this migration. The digital signatures are used to provide many services and solutions that would not have been possible by the conventional hand-written signature. In the educational environment, the process of issuing the graduation certificates can no longer be restricted to the traditional methods. Hence, a computerized system for issuing certificates of graduation in an electronic form is needed and desired. This paper proposes a Graduation Certificates Issuing System (GCIS based on digital signature technology. In doing so, this research highlights the state-of-the-art and the art-of-the-practice for some existing digital signature-based systems in the literatures. In addition, eight intertwined elected services are identified, namely: message authentication, entity authentication, integrity, non-repudiation, time stamping, distinguished signing authorities, delegating signing capability and supporting workflow systems. Moreover, this research examines nine existing systems, showing their merits and demerits in terms of these elected services. Furthermore, the research describes the architectural design using the Unified Modeling Language (UML and provides the concrete implementation of the proposed GCIS. The GCIS is implemented using Visual Basic.Net programming language and SQL Server database

Compact Acoustic Models for Embedded Speech Recognition

Directory of Open Access Journals (Sweden)

Lévy Christophe

2009-01-01

Full Text Available Speech recognition applications are known to require a significant amount of resources. However, embedded speech recognition only authorizes few KB of memory, few MIPS, and small amount of training data. In order to fit the resource constraints of embedded applications, an approach based on a semicontinuous HMM system using state-independent acoustic modelling is proposed. A transformation is computed and applied to the global model in order to obtain each HMM state-dependent probability density functions, authorizing to store only the transformation parameters. This approach is evaluated on two tasks: digit and voice-command recognition. A fast adaptation technique of acoustic models is also proposed. In order to significantly reduce computational costs, the adaptation is performed only on the global model (using related speaker recognition adaptation techniques with no need for state-dependent data. The whole approach results in a relative gain of more than 20% compared to a basic HMM-based system fitting the constraints.

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

Indian Academy of Sciences (India)

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

a prerequisite for directional code measurement (Chen & Lee 1992). The handwritten PSL ... shows the directional code and an example to illustrate the coding method for a word formed by the concatenation .... Primitive selection. 5.1 Corner ...

Development and validation of a smartphone-based digits-in-noise hearing test in South African English.

Science.gov (United States)

Potgieter, Jenni-Marí; Swanepoel, De Wet; Myburgh, Hermanus Carel; Hopper, Thomas Christopher; Smits, Cas

2015-07-01

The objective of this study was to develop and validate a smartphone-based digits-in-noise hearing test for South African English. Single digits (0-9) were recorded and spoken by a first language English female speaker. Level corrections were applied to create a set of homogeneous digits with steep speech recognition functions. A smartphone application was created to utilize 120 digit-triplets in noise as test material. An adaptive test procedure determined the speech reception threshold (SRT). Experiments were performed to determine headphones effects on the SRT and to establish normative data. Participants consisted of 40 normal-hearing subjects with thresholds ≤15 dB across the frequency spectrum (250-8000 Hz) and 186 subjects with normal-hearing in both ears, or normal-hearing in the better ear. The results show steep speech recognition functions with a slope of 20%/dB for digit-triplets presented in noise using the smartphone application. The results of five headphone types indicate that the smartphone-based hearing test is reliable and can be conducted using standard Android smartphone headphones or clinical headphones. A digits-in-noise hearing test was developed and validated for South Africa. The mean SRT and speech recognition functions correspond to previous developed telephone-based digits-in-noise tests.

Real-time classification and sensor fusion with a spiking deep belief network.

Science.gov (United States)

O'Connor, Peter; Neil, Daniel; Liu, Shih-Chii; Delbruck, Tobi; Pfeiffer, Michael

2013-01-01

Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers. This paper proposes a method based on the Siegert approximation for Integrate-and-Fire neurons to map an offline-trained DBN onto an efficient event-driven spiking neural network suitable for hardware implementation. The method is demonstrated in simulation and by a real-time implementation of a 3-layer network with 2694 neurons used for visual classification of MNIST handwritten digits with input from a 128 × 128 Dynamic Vision Sensor (DVS) silicon retina, and sensory-fusion using additional input from a 64-channel AER-EAR silicon cochlea. The system is implemented through the open-source software in the jAER project and runs in real-time on a laptop computer. It is demonstrated that the system can recognize digits in the presence of distractions, noise, scaling, translation and rotation, and that the degradation of recognition performance by using an event-based approach is less than 1%. Recognition is achieved in an average of 5.8 ms after the onset of the presentation of a digit. By cue integration from both silicon retina and cochlea outputs we show that the system can be biased to select the correct digit from otherwise ambiguous input.

Automatic system for localization and recognition of vehicle plate numbers

OpenAIRE

Vázquez, N.; Nakano, M.; Pérez-Meana, H.

2003-01-01

This paper proposes a vehicle numbers plate identification system, which extracts the characters features of a plate from a captured image by a digital camera. Then identify the symbols of the number plate using a multilayer neural network. The proposed recognition system consists of two processes: The training process and the recognition process. During the training process, a database is created using 310 vehicular plate images. Then using this database a multilayer neural network is traine...

Aplikasi MATLAB untuk Mengenali Karakter Tulisan Tangan

OpenAIRE

ali mahmudi

2017-01-01

Handwriting recognition is one of the very interesting research object in the field of image processing, artificial intelligence and computer vision. This is due to the handwritten characters is varied in every individual. The style, size and orientation of handwriting characters has made every body’s is different, hence handwriting recognition is a very interesting research object. Handwriting recognition application has been used in quite many applications, such as reading the bank deposits...

Preschoolers Explore Interactive Storybook Apps: The Effect on Word Recognition and Story Comprehension

Science.gov (United States)

Zipke, Marcy

2017-01-01

Two experiments explored the effects of reading digital storybooks on tablet computers with 25 preschoolers, aged 4-5. In the first experiment, the students' word recognition scores were found to increase significantly more when students explored a digital storybook and employed the read-aloud function than when they were read to from a comparable…

Two-tier architecture for unconstrained handwritten character ...

Indian Academy of Sciences (India)

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

This approach uses the Kohonen self-organizing neural network for data classification in the ... Traditional classifiers test the ..... Trier O D, Jain A K, Taxt T 1996 Feature extraction methods for character recognition – A survey. Pattern Recogn.

Learner autonomy development through digital gameplay

Directory of Open Access Journals (Sweden)

Alice Chik

2011-04-01

Full Text Available Playing digital games is undeniably a popular leisure activity, and digital gaming is also gaining academic attention and recognition for enhancing digital literacies and learning motivation. One tricky issue when exploring digital gaming in Asian contexts is the popularity of English and Japanese games. Though Chinese and Korean online games are readily available, many of the more popular commercial off-the-shelf (COTS digital games are in English and Japanese. Students in Hong Kong are required to take English as a foreign language, which resulted in a huge range of proficiency, but Japanese is not offered at public schools. So, most Hong Kong gamers are playing foreign language games. Yet language barriers do not diminish the market demand for foreign language digital games. This paper explores the phenomenon of digital gaming in foreign languages. Based on findings from an on-going research project with ten undergraduate video gamers (F=4, M=6, this paper argues that gamers exercise learner autonomy by managing their gaming both as leisure and learning experiences.

Advanced digital video surveillance for safeguard and physical protection

International Nuclear Information System (INIS)

Kumar, R.

2002-01-01

Full text: Video surveillance is a very crucial component in safeguard and physical protection. Digital technology has revolutionized the surveillance scenario and brought in various new capabilities like better image quality, faster search and retrieval of video images, less storage space for recording, efficient transmission and storage of video, better protection of recorded video images, and easy remote accesses to live and recorded video etc. The basic safeguard requirement for verifiably uninterrupted surveillance has remained largely unchanged since its inception. However, changes to the inspection paradigm to admit automated review and remote monitoring have dramatically increased the demands on safeguard surveillance system. Today's safeguard systems can incorporate intelligent motion detection with very low rate of false alarm and less archiving volume, embedded image processing capability for object behavior and event based indexing, object recognition, efficient querying and report generation etc. It also demands cryptographically authenticating, encrypted, and highly compressed video data for efficient, secure, tamper indicating and transmission. In physical protection, intelligent on robust video motion detection, real time moving object detection and tracking from stationary and moving camera platform, multi-camera cooperative tracking, activity detection and recognition, human motion analysis etc. is going to play a key rote in perimeter security. Incorporation of front and video imagery exploitation tools like automatic number plate recognition, vehicle identification and classification, vehicle undercarriage inspection, face recognition, iris recognition and other biometric tools, gesture recognition etc. makes personnel and vehicle access control robust and foolproof. Innovative digital image enhancement techniques coupled with novel sensor design makes low cost, omni-directional vision capable, all weather, day night surveillance a reality

Standard digital reference images for titanium castings

CERN Document Server

American Society for Testing and Materials. Philadelphia

2010-01-01

1.1 The digital reference images provided in the adjunct to this standard illustrate various types and degrees of discontinuities occurring in titanium castings. Use of this standard for the specification or grading of castings requires procurement of the adjunct digital reference images, which illustrate the discontinuity types and severity levels. They are intended to provide the following: 1.1.1 A guide enabling recognition of titanium casting discontinuities and their differentiation both as to type and degree through digital radiographic examination. 1.1.2 Example digital radiographic illustrations of discontinuities and a nomenclature for reference in acceptance standards, specifications and drawings. 1.2 The digital reference images consist of seventeen digital files each illustrating eight grades of increasing severity. The files illustrate seven common discontinuity types representing casting sections up to 1-in. (25.4-mm). 1.3 The reference radiographs were developed for casting sections up to 1...

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

Science.gov (United States)

Diaz-Escobar, Julia; Kober, Vitaly

2015-09-01

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

ASERA: A Spectrum Eye Recognition Assistant

Science.gov (United States)

Yuan, Hailong; Zhang, Haotong; Zhang, Yanxia; Lei, Yajuan; Dong, Yiqiao; Zhao, Yongheng

2018-04-01

ASERA, ASpectrum Eye Recognition Assistant, aids in quasar spectral recognition and redshift measurement and can also be used to recognize various types of spectra of stars, galaxies and AGNs (Active Galactic Nucleus). This interactive software allows users to visualize observed spectra, superimpose template spectra from the Sloan Digital Sky Survey (SDSS), and interactively access related spectral line information. ASERA is an efficient and user-friendly semi-automated toolkit for the accurate classification of spectra observed by LAMOST (the Large Sky Area Multi-object Fiber Spectroscopic Telescope) and is available as a standalone Java application and as a Java applet. The software offers several functions, including wavelength and flux scale settings, zoom in and out, redshift estimation, and spectral line identification.

2nd International Symposium on Signal Processing and Intelligent Recognition Systems

CERN Document Server

Bandyopadhyay, Sanghamitra; Krishnan, Sri; Li, Kuan-Ching; Mosin, Sergey; Ma, Maode

2016-01-01

This Edited Volume contains a selection of refereed and revised papers originally presented at the second International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS-2015), December 16-19, 2015, Trivandrum, India. The program committee received 175 submissions. Each paper was peer reviewed by at least three or more independent referees of the program committee and the 59 papers were finally selected. The papers offer stimulating insights into biometrics, digital watermarking, recognition systems, image and video processing, signal and speech processing, pattern recognition, machine learning and knowledge-based systems. The book is directed to the researchers and scientists engaged in various field of signal processing and related areas. .

Impaired processing of self-face recognition in anorexia nervosa.

Science.gov (United States)

Hirot, France; Lesage, Marine; Pedron, Lya; Meyer, Isabelle; Thomas, Pierre; Cottencin, Olivier; Guardia, Dewi

2016-03-01

Body image disturbances and massive weight loss are major clinical symptoms of anorexia nervosa (AN). The aim of the present study was to examine the influence of body changes and eating attitudes on self-face recognition ability in AN. Twenty-seven subjects suffering from AN and 27 control participants performed a self-face recognition task (SFRT). During the task, digital morphs between their own face and a gender-matched unfamiliar face were presented in a random sequence. Participants' self-face recognition failures, cognitive flexibility, body concern and eating habits were assessed with the Self-Face Recognition Questionnaire (SFRQ), Trail Making Test (TMT), Body Shape Questionnaire (BSQ) and Eating Disorder Inventory-2 (EDI-2), respectively. Subjects suffering from AN exhibited significantly greater difficulties than control participants in identifying their own face (p = 0.028). No significant difference was observed between the two groups for TMT (all p > 0.1, non-significant). Regarding predictors of self-face recognition skills, there was a negative correlation between SFRT and body mass index (p = 0.01) and a positive correlation between SFRQ and EDI-2 (p face recognition.

Flexible frontiers for text division into rows

Directory of Open Access Journals (Sweden)

Dan L. Lacrămă

2009-01-01

Full Text Available This paper presents an original solution for flexible hand-written text division into rows. Unlike the standard procedure, the proposed method avoids the isolated characters extensions amputation and reduces the recognition error rate in the final stage.

Two-tier architecture for unconstrained handwritten character

Indian Academy of Sciences (India)

The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection ...

Targeted Query Expansions as a Method for Searching Mixed Quality Digitized Cultural Heritage Documents

NARCIS (Netherlands)

Keskustalo, H.; Kettunen, K.; Kumpulainen, S.; Ferro, N.; Silvello, G.; Järvelin, A.; Kekäläinen, J.; Arvola, P.; Saastamoinen, M.; Sormunen, E.; Järvelin, K.

2015-01-01

Digitization of cultural heritage is a huge ongoing effort in many countries. In digitized historical documents, words may occur in different surface forms due to three types of variation - morphological variation, historical variation, and errors in optical character recognition (OCR). Because

Advanced techniques in digital mammographic images recognition

International Nuclear Information System (INIS)

Aliu, R. Azir

2011-01-01

Computer Aided Detection and Diagnosis is used in digital radiography as a second thought in the process of determining diagnoses, which reduces the percentage of wrong diagnoses of the established interpretation of mammographic images. The issues that are discussed in the dissertation are the analyses and improvement of advanced technologies in the field of artificial intelligence, more specifically in the field of machine learning for solving diagnostic problems and automatic detection of speculated lesions in digital mammograms. The developed of SVM-based ICAD system with cascade architecture for analyses and comparison mammographic images in both projections (CC and MLO) gives excellent result for detection and masses and microcalcifications. In order to develop a system with optimal performances of sensitivity, specificity and time complexity, a set of relevant characteristics need to be created which will show all the pathological regions that might be present in the mammographic image. The structure of the mammographic image, size and the large number of pathological structures in this area are the reason why the creation of a set of these features is necessary for the presentation of good indicators. These pathological structures are a real challenge today and the world of science is working in that direction. The doctoral dissertation showed that the system has optimal results, which are confirmed by experts, and institutions, which are dealing with these same issues. Also, the thesis presents a new approach for automatic identification of regions of interest in the mammographic image where regions of interest are automatically selected for further processing mammography in cases when the number of examined patients is higher. Out of 480 mammographic images downloaded from MIAS database and tested with ICAD system the author shows that, after separation and selection of relevant features of ICAD system the accuracy is 89.7% (96.4% for microcalcifications

Optical character recognition based on nonredundant correlation measurements.

Science.gov (United States)

Braunecker, B; Hauck, R; Lohmann, A W

1979-08-15

The essence of character recognition is a comparison between the unknown character and a set of reference patterns. Usually, these reference patterns are all possible characters themselves, the whole alphabet in the case of letter characters. Obviously, N analog measurements are highly redundant, since only K = log(2)N binary decisions are enough to identify one out of N characters. Therefore, we devised K reference patterns accordingly. These patterns, called principal components, are found by digital image processing, but used in an optical analog computer. We will explain the concept of principal components, and we will describe experiments with several optical character recognition systems, based on this concept.

Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine.

Science.gov (United States)

Hu, Miao; Graves, Catherine E; Li, Can; Li, Yunning; Ge, Ning; Montgomery, Eric; Davila, Noraica; Jiang, Hao; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei; Strachan, John Paul

2018-03-01

Using memristor crossbar arrays to accelerate computations is a promising approach to efficiently implement algorithms in deep neural networks. Early demonstrations, however, are limited to simulations or small-scale problems primarily due to materials and device challenges that limit the size of the memristor crossbar arrays that can be reliably programmed to stable and analog values, which is the focus of the current work. High-precision analog tuning and control of memristor cells across a 128 × 64 array is demonstrated, and the resulting vector matrix multiplication (VMM) computing precision is evaluated. Single-layer neural network inference is performed in these arrays, and the performance compared to a digital approach is assessed. Memristor computing system used here reaches a VMM accuracy equivalent of 6 bits, and an 89.9% recognition accuracy is achieved for the 10k MNIST handwritten digit test set. Forecasts show that with integrated (on chip) and scaled memristors, a computational efficiency greater than 100 trillion operations per second per Watt is possible. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Recognition of tennis serve performed by a digital player: comparison among polygon, shadow, and stick-figure models.

Directory of Open Access Journals (Sweden)

Hirofumi Ida

Full Text Available The objective of this study was to assess the cognitive effect of human character models on the observer's ability to extract relevant information from computer graphics animation of tennis serve motions. Three digital human models (polygon, shadow, and stick-figure were used to display the computationally simulated serve motions, which were perturbed at the racket-arm by modulating the speed (slower or faster of one of the joint rotations (wrist, elbow, or shoulder. Twenty-one experienced tennis players and 21 novices made discrimination responses about the modulated joint and also specified the perceived swing speeds on a visual analogue scale. The result showed that the discrimination accuracies of the experienced players were both above and below chance level depending on the modulated joint whereas those of the novices mostly remained at chance or guessing levels. As far as the experienced players were concerned, the polygon model decreased the discrimination accuracy as compared with the stick-figure model. This suggests that the complicated pictorial information may have a distracting effect on the recognition of the observed action. On the other hand, the perceived swing speed of the perturbed motion relative to the control was lower for the stick-figure model than for the polygon model regardless of the skill level. This result suggests that the simplified visual information can bias the perception of the motion speed toward slower. It was also shown that the increasing the joint rotation speed increased the perceived swing speed, although the resulting racket velocity had little correlation with this speed sensation. Collectively, observer's recognition of the motion pattern and perception of the motion speed can be affected by the pictorial information of the human model as well as by the perturbation processing applied to the observed motion.

Recognition of tennis serve performed by a digital player: comparison among polygon, shadow, and stick-figure models.

Science.gov (United States)

Ida, Hirofumi; Fukuhara, Kazunobu; Ishii, Motonobu

2012-01-01

The objective of this study was to assess the cognitive effect of human character models on the observer's ability to extract relevant information from computer graphics animation of tennis serve motions. Three digital human models (polygon, shadow, and stick-figure) were used to display the computationally simulated serve motions, which were perturbed at the racket-arm by modulating the speed (slower or faster) of one of the joint rotations (wrist, elbow, or shoulder). Twenty-one experienced tennis players and 21 novices made discrimination responses about the modulated joint and also specified the perceived swing speeds on a visual analogue scale. The result showed that the discrimination accuracies of the experienced players were both above and below chance level depending on the modulated joint whereas those of the novices mostly remained at chance or guessing levels. As far as the experienced players were concerned, the polygon model decreased the discrimination accuracy as compared with the stick-figure model. This suggests that the complicated pictorial information may have a distracting effect on the recognition of the observed action. On the other hand, the perceived swing speed of the perturbed motion relative to the control was lower for the stick-figure model than for the polygon model regardless of the skill level. This result suggests that the simplified visual information can bias the perception of the motion speed toward slower. It was also shown that the increasing the joint rotation speed increased the perceived swing speed, although the resulting racket velocity had little correlation with this speed sensation. Collectively, observer's recognition of the motion pattern and perception of the motion speed can be affected by the pictorial information of the human model as well as by the perturbation processing applied to the observed motion.

Stokes Space-Based Optical Modulation Format Recognition for Digital Coherent Receivers

DEFF Research Database (Denmark)

Borkowski, Robert; Zibar, Darko; Caballero Jambrina, Antonio

2013-01-01

We present a technique for modulation format recognition for heterogeneous reconfigurable optical networks. The method is based on Stokes space signal representation and uses a variational Bayesian expectation maximization machine learning algorithm. Differentiation between diverse common coheren...

Fuzzy logic and neural networks in artificial intelligence and pattern recognition

Science.gov (United States)

Sanchez, Elie

1991-10-01

With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

Digital Archiving: Where the Past Lives Again

Science.gov (United States)

Paxson, K. B.

2012-06-01

The process of digital archiving for variable star data by manual entry with an Excel spreadsheet is described. Excel-based tools including a Step Magnitude Calculator and a Julian Date Calculator for variable star observations where magnitudes and Julian dates have not been reduced are presented. Variable star data in the literature and the AAVSO International Database prior to 1911 are presented and reviewed, with recent archiving work being highlighted. Digitization using optical character recognition software conversion is also demonstrated, with editing and formatting suggestions for the OCR-converted text.

Evaluating a voice recognition system: finding the right product for your department.

Science.gov (United States)

Freeh, M; Dewey, M; Brigham, L

2001-06-01

The Department of Radiology at the University of Utah Health Sciences Center has been in the process of transitioning from the traditional film-based department to a digital imaging department for the past 2 years. The department is now transitioning from the traditional method of dictating reports (dictation by radiologist to transcription to review and signing by radiologist) to a voice recognition system. The transition to digital operations will not be complete until we have the ability to directly interface the dictation process with the image review process. Voice recognition technology has advanced to the level where it can and should be an integral part of the new way of working in radiology and is an integral part of an efficient digital imaging department. The transition to voice recognition requires the task of identifying the product and the company that will best meet a department's needs. This report introduces the methods we used to evaluate the vendors and the products available as we made our purchasing decision. We discuss our evaluation method and provide a checklist that can be used by other departments to assist with their evaluation process. The criteria used in the evaluation process fall into the following major categories: user operations, technical infrastructure, medical dictionary, system interfaces, service support, cost, and company strength. Conclusions drawn from our evaluation process will be detailed, with the intention being to shorten the process for others as they embark on a similar venture. As more and more organizations investigate the many products and services that are now being offered to enhance the operations of a radiology department, it becomes increasingly important that solid methods are used to most effectively evaluate the new products. This report should help others complete the task of evaluating a voice recognition system and may be adaptable to other products as well.

A knowledge-based approach for recognition of handwritten Pitman ...

Indian Academy of Sciences (India)

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

Department of Studies in Computer Science, University of Mysore, ... the successor method based on stochastic regular grammar but makes use of the ... In general, a stroke in PSL represents a character or a word in English at the simplest.

New technique for real-time distortion-invariant multiobject recognition and classification

Science.gov (United States)

Hong, Rutong; Li, Xiaoshun; Hong, En; Wang, Zuyi; Wei, Hongan

2001-04-01

A real-time hybrid distortion-invariant OPR system was established to make 3D multiobject distortion-invariant automatic pattern recognition. Wavelet transform technique was used to make digital preprocessing of the input scene, to depress the noisy background and enhance the recognized object. A three-layer backpropagation artificial neural network was used in correlation signal post-processing to perform multiobject distortion-invariant recognition and classification. The C-80 and NOA real-time processing ability and the multithread programming technology were used to perform high speed parallel multitask processing and speed up the post processing rate to ROIs. The reference filter library was constructed for the distortion version of 3D object model images based on the distortion parameter tolerance measuring as rotation, azimuth and scale. The real-time optical correlation recognition testing of this OPR system demonstrates that using the preprocessing, post- processing, the nonlinear algorithm os optimum filtering, RFL construction technique and the multithread programming technology, a high possibility of recognition and recognition rate ere obtained for the real-time multiobject distortion-invariant OPR system. The recognition reliability and rate was improved greatly. These techniques are very useful to automatic target recognition.

Sadhana | Indian Academy of Sciences

Indian Academy of Sciences (India)

Explicit, closed form algebraic results for the element strains, stresses and errors have been derived using this method. The performance of ... In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses ...

Latent log-linear models for handwritten digit classification.

Science.gov (United States)

Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann

2012-06-01

We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.

Illumination-invariant face recognition with a contrast sensitive silicon retina

Energy Technology Data Exchange (ETDEWEB)

Buhmann, J.M. [Rheinische Friedrich-Wilhelms-Univ., Bonn (Germany). Inst. fuer Informatik II; Lades, M. [Bochum Univ. (Germany). Inst. fuer Neuroinformatik; Eeckman, F. [Lawrence Livermore National Lab., CA (United States)

1993-11-29

Changes in lighting conditions strongly effect the performance and reliability of computer vision systems. We report face recognition results under drastically changing lighting conditions for a computer vision system which concurrently uses a contrast sensitive silicon retina and a conventional, gain controlled CCD camera. For both input devices the face recognition system employs an elastic matching algorithm with wavelet based features to classify unknown faces. To assess the effect of analog on-chip preprocessing by the silicon retina the CCD images have been digitally preprocessed with a bandpass filter to adjust the power spectrum. The silicon retina with its ability to adjust sensitivity increases the recognition rate up to 50 percent. These comparative experiments demonstrate that preprocessing with an analog VLSI silicon retina generates image data enriched with object-constant features.

Digital color imaging

CERN Document Server

Fernandez-Maloigne, Christine; Macaire, Ludovic

2013-01-01

This collective work identifies the latest developments in the field of the automatic processing and analysis of digital color images.For researchers and students, it represents a critical state of the art on the scientific issues raised by the various steps constituting the chain of color image processing.It covers a wide range of topics related to computational color imaging, including color filtering and segmentation, color texture characterization, color invariant for object recognition, color and motion analysis, as well as color image and video indexing and retrieval. <

Enhancing global positioning by image recognition

OpenAIRE

Marimon Sanjuan, David; Adamek, Tomasz; Bonnin, Arturo; Trzcinski, Tomasz

2011-01-01

Current commercial outdoor Mobile AR applications rely mostly on GPS antennas, digital compasses and accelerometers. Due to imprecise readings, the 2D placement of points of interest (POI) on the display can be uncorrelated with reality. We present a novel method to geo-locate a mobile device by rec- ognizing what is captured by its camera. A visual recognition algo- rithm in the cloud is used to identify geo-located reference images that match the camera’s view. Upon correct identification, ...

Static sign language recognition using 1D descriptors and neural networks

Science.gov (United States)

Solís, José F.; Toxqui, Carina; Padilla, Alfonso; Santiago, César

2012-10-01

A frame work for static sign language recognition using descriptors which represents 2D images in 1D data and artificial neural networks is presented in this work. The 1D descriptors were computed by two methods, first one consists in a correlation rotational operator.1 and second is based on contour analysis of hand shape. One of the main problems in sign language recognition is segmentation; most of papers report a special color in gloves or background for hand shape analysis. In order to avoid the use of gloves or special clothing, a thermal imaging camera was used to capture images. Static signs were picked up from 1 to 9 digits of American Sign Language, a multilayer perceptron reached 100% recognition with cross-validation.

Automated Categorization Scheme for Digital Libraries in Distance Learning: A Pattern Recognition Approach

Science.gov (United States)

Gunal, Serkan

2008-01-01

Digital libraries play a crucial role in distance learning. Nowadays, they are one of the fundamental information sources for the students enrolled in this learning system. These libraries contain huge amount of instructional data (text, audio and video) offered by the distance learning program. Organization of the digital libraries is…

Advanced optical correlation and digital methods for pattern matching—50th anniversary of Vander Lugt matched filter

Science.gov (United States)

Millán, María S.

2012-10-01

On the verge of the 50th anniversary of Vander Lugt’s formulation for pattern matching based on matched filtering and optical correlation, we acknowledge the very intense research activity developed in the field of correlation-based pattern recognition during this period of time. The paper reviews some domains that appeared as emerging fields in the last years of the 20th century and have been developed later on in the 21st century. Such is the case of three-dimensional (3D) object recognition, biometric pattern matching, optical security and hybrid optical-digital processors. 3D object recognition is a challenging case of multidimensional image recognition because of its implications in the recognition of real-world objects independent of their perspective. Biometric recognition is essentially pattern recognition for which the personal identification is based on the authentication of a specific physiological characteristic possessed by the subject (e.g. fingerprint, face, iris, retina, and multifactor combinations). Biometric recognition often appears combined with encryption-decryption processes to secure information. The optical implementations of correlation-based pattern recognition processes still rely on the 4f-correlator, the joint transform correlator, or some of their variants. But the many applications developed in the field have been pushing the systems for a continuous improvement of their architectures and algorithms, thus leading towards merged optical-digital solutions.

Advanced optical correlation and digital methods for pattern matching—50th anniversary of Vander Lugt matched filter

International Nuclear Information System (INIS)

Millán, María S

2012-01-01

On the verge of the 50th anniversary of Vander Lugt’s formulation for pattern matching based on matched filtering and optical correlation, we acknowledge the very intense research activity developed in the field of correlation-based pattern recognition during this period of time. The paper reviews some domains that appeared as emerging fields in the last years of the 20th century and have been developed later on in the 21st century. Such is the case of three-dimensional (3D) object recognition, biometric pattern matching, optical security and hybrid optical–digital processors. 3D object recognition is a challenging case of multidimensional image recognition because of its implications in the recognition of real-world objects independent of their perspective. Biometric recognition is essentially pattern recognition for which the personal identification is based on the authentication of a specific physiological characteristic possessed by the subject (e.g. fingerprint, face, iris, retina, and multifactor combinations). Biometric recognition often appears combined with encryption–decryption processes to secure information. The optical implementations of correlation-based pattern recognition processes still rely on the 4f-correlator, the joint transform correlator, or some of their variants. But the many applications developed in the field have been pushing the systems for a continuous improvement of their architectures and algorithms, thus leading towards merged optical–digital solutions. (review article)

Sadhana | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Sadhana. P Nagabhushan. Articles written in Sadhana. Volume 27 Issue 6 December 2002 pp 685-698. A knowledge-based approach for recognition of handwritten Pitman shorthand language strokes · P Nagabhushan Basavaraj S Anami · More Details Abstract Fulltext PDF. The Pitman shorthand ...

Sadhana | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Sadhana. Basavaraj S Anami. Articles written in Sadhana. Volume 27 Issue 6 December 2002 pp 685-698. A knowledge-based approach for recognition of handwritten Pitman shorthand language strokes · P Nagabhushan Basavaraj S Anami · More Details Abstract Fulltext PDF. The Pitman shorthand ...

Bispectral methods of signal processing applications in radar, telecommunications and digital image restoration

CERN Document Server

Totsky, Alexander V; Kravchenko, Victor F

2015-01-01

By studying applications in radar, telecommunications and digital image restoration, this monograph discusses signal processing techniques based on bispectral methods. Improved robustness against different forms of noise as well as preservation of phase information render this method a valuable alternative to common power-spectrum analysis used in radar object recognition, digital wireless communications, and jitter removal in images.

Speech recognition by means of a three-integrated-circuit set

Energy Technology Data Exchange (ETDEWEB)

Zoicas, A.

1983-11-03

The author uses pattern recognition methods for detecting word boundaries, and monitors incoming speech at 12 millisecond intervals. Frequency is divided into eight bands and analysis is achieved in an analogue interface integrated circuit, a pipeline digital processor and a control integrated circuit. Applications are suggested, including speech input to personal computers. 3 references.

A Massively Parallel Face Recognition System

Directory of Open Access Journals (Sweden)

Lahdenoja Olli

2007-01-01

Full Text Available We present methods for processing the LBPs (local binary patterns with a massively parallel hardware, especially with CNN-UM (cellular nonlinear network-universal machine. In particular, we present a framework for implementing a massively parallel face recognition system, including a dedicated highly accurate algorithm suitable for various types of platforms (e.g., CNN-UM and digital FPGA. We study in detail a dedicated mixed-mode implementation of the algorithm and estimate its implementation cost in the view of its performance and accuracy restrictions.

A Massively Parallel Face Recognition System

Directory of Open Access Journals (Sweden)

Ari Paasio

2006-12-01

Full Text Available We present methods for processing the LBPs (local binary patterns with a massively parallel hardware, especially with CNN-UM (cellular nonlinear network-universal machine. In particular, we present a framework for implementing a massively parallel face recognition system, including a dedicated highly accurate algorithm suitable for various types of platforms (e.g., CNN-UM and digital FPGA. We study in detail a dedicated mixed-mode implementation of the algorithm and estimate its implementation cost in the view of its performance and accuracy restrictions.

CASRA+: A Colloquial Arabic Speech Recognition Application

OpenAIRE

Ramzi A. Haraty; Omar El Ariss

2007-01-01

The research proposed here was for an Arabic speech recognition application, concentrating on the Lebanese dialect. The system starts by sampling the speech, which was the process of transforming the sound from analog to digital and then extracts the features by using the Mel-Frequency Cepstral Coefficients (MFCC). The extracted features are then compared with the system's stored model; in this case the stored model chosen was a phoneme-based model. This reference model differs from the direc...

Epistemic agency in an environmental sciences watershed investigation fostered by digital photography

Science.gov (United States)

Zimmerman, Heather Toomey; Weible, Jennifer L.

2018-05-01

This collective case study investigates the role of digital photography to support high school students' engagement in science inquiry practices during a three-week environmental sciences unit. The study's theoretical framework brings together research from digital photography, participation in environmental science practices, and epistemic agency. Data analysed include field notes and video transcripts from two groups of learners (n = 19) that focus on how high school students used digital photography during their participation in two distinct environmental monitoring practices: stream mapping and macroinvertebrate identification. Our study resulted in two findings related to the role of digital photography where students developed knowledge as they engaged in environmental monitoring inquiry practices. First, we found that digital photography was integral to the youths' epistemic agency (defined as their confidence that they could build knowledge related to science in their community) as they engaged in data collection, documenting environmental monitoring procedures, and sharing data in the classroom. Based this finding, an implication of our work is a refined view of the role of digital photography in environmental sciences education where the use of photography enhances epistemic agency in inquiry-based activities. Second, we found that the youths innovated a use of digital photography to foster a recognition that they were capable and competent in scientific procedures during a streamside study. Based on this finding, we offer a theoretical implication that expands the construct of epistemic agency; we posit that epistemic agency includes a subcomponent where the students purposefully formulate an external recognition as producers of scientific knowledge.

Rocket Ozone Data Recovery for Digital Archival

Science.gov (United States)

Hwang, S. H.; Krueger, A. J.; Hilsenrath, E.; Haffner, D. P.; Bhartia, P. K.

2014-12-01

Ozone distributions in the photochemically-controlled upper stratosphere and mesosphere were first measured using spectrometers on V-2 rockets after WWII. The IGY(1957-1958) spurred development of new optical and chemical instruments for flight on meteorological and sounding rockets. In the early 1960's, the US Navy developed an Arcas rocket-borne optical ozonesonde and NASA GSFC developed chemiluminescent ozonesonde onboard Nike_Cajun and Arcas rocket. The Navy optical ozone program was moved in 1969 to GSFC where rocket ozone research was expanded and continued until 1994 using Super Loki-Dart rocket at 11 sites in the range of 0-65N and 35W-160W. Over 300 optical ozone soundings and 40 chemiluminescent soundings were made. The data have been used to produce the US Standard Ozone Atmosphere, determine seasonal and diurnal variations, and validate early photochemical models. The current effort includes soundings conducted by Australia, Japan, and Korea using optical techniques. New satellite ozone sounding techniques were initially calibrated and later validated using the rocket ozone data. As satellite techniques superseded the rocket methods, the sponsoring agencies lost interest in the data and many of those records have been discarded. The current task intends to recover as much of the data as possible from the private records of the experimenters and their publications, and to archive those records in the WOUDC (World Ozone and Ultraviolet Data Centre). The original data records are handwritten tabulations, computer printouts that are scanned with OCR techniques, and plots digitized from publications. This newly recovered digital rocket ozone profile data from 1965 to 2002 could make significant contributions to the Earth science community in atmospheric research including long-term trend analysis.

Feature extraction with deep neural networks by a generalized discriminant analysis.

Science.gov (United States)

Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

2012-04-01

We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

Early detection of the incidence of malignancy in mammograms using digital image correlation

International Nuclear Information System (INIS)

Espitia, J.; Jacome, J.; Torres, C.

2016-01-01

The digital image correlation has proved an effective way for Pattern Recognition, this research to identify the using Findings digitally extracted from a mammographic image, which is the means used by more specialists to determine if a person is a candidate or not, a Suffer Breast Cancer. This shown that early detection of symptom logy 'carcinogenic' is the key . (Author)

Prediction of Word Recognition in the First Half of Grade 1

Science.gov (United States)

Snel, M. J.; Aarnoutse, C. A. J.; Terwel, J.; van Leeuwe, J. F. J.; van der Veld, W. M.

2016-01-01

Early detection of reading problems is important to prevent an enduring lag in reading skills. We studied the relationship between speed of word recognition (after six months of grade 1 education) and four kindergarten pre-literacy skills: letter knowledge, phonological awareness and naming speed for both digits and letters. Our sample consisted…

Recognition of phonetic Arabic figures via wavelet based Mel Frequency Cepstrum using HMMs

Directory of Open Access Journals (Sweden)

Ibrahim M. El-Henawy

2014-04-01

A comparison between different features of speech is given. The features based on the Cepstrum give accuracy of 94% for speech recognition while the features based on the short time energy in time domain give accuracy of 92%. The features based on formant frequencies give accuracy of 95.5%. It is clear that the features based on MFCCs with accuracy of 98% give the best accuracy rate. So the features depend on MFCCs with HMMs may be recommended for recognition of the spoken Arabic digits.

A Malaysian Vehicle License Plate Localization and Recognition System

Directory of Open Access Journals (Sweden)

Ganapathy Velappa

2008-02-01

Full Text Available Technological intelligence is a highly sought after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. In this paper, a license plate localization and recognition system for vehicles in Malaysia is proposed. This system is developed based on digital images and can be easily applied to commercial car park systems for the use of documenting access of parking services, secure usage of parking houses and also to prevent car theft issues. The proposed license plate localization algorithm is based on a combination of morphological processes with a modified Hough Transform approach and the recognition of the license plates is achieved by the implementation of the feed-forward backpropagation artificial neural network. Experimental results show an average of 95% successful license plate localization and recognition in a total of 589 images captured from a complex outdoor environment.

Audio-based deep music emotion recognition

Science.gov (United States)

Liu, Tong; Han, Li; Ma, Liangkai; Guo, Dongwei

2018-05-01

As the rapid development of multimedia networking, more and more songs are issued through the Internet and stored in large digital music libraries. However, music information retrieval on these libraries can be really hard, and the recognition of musical emotion is especially challenging. In this paper, we report a strategy to recognize the emotion contained in songs by classifying their spectrograms, which contain both the time and frequency information, with a convolutional neural network (CNN). The experiments conducted on the l000-song dataset indicate that the proposed model outperforms traditional machine learning method.

Targeted and untargeted-metabolite profiling to track the compositional integrity of ginger during processing using digitally-enhanced HPTLC pattern recognition analysis.

Science.gov (United States)

Ibrahim, Reham S; Fathy, Hoda

2018-03-30

Tracking the impact of commonly applied post-harvesting and industrial processing practices on the compositional integrity of ginger rhizome was implemented in this work. Untargeted metabolite profiling was performed using digitally-enhanced HPTLC method where the chromatographic fingerprints were extracted using ImageJ software then analysed with multivariate Principal Component Analysis (PCA) for pattern recognition. A targeted approach was applied using a new, validated, simple and fast HPTLC image analysis method for simultaneous quantification of the officially recognized markers 6-, 8-, 10-gingerol and 6-shogaol in conjunction with chemometric Hierarchical Clustering Analysis (HCA). The results of both targeted and untargeted metabolite profiling revealed that peeling, drying in addition to storage employed during processing have a great influence on ginger chemo-profile, the different forms of processed ginger shouldn't be used interchangeably. Moreover, it deemed necessary to consider the holistic metabolic profile for comprehensive evaluation of ginger during processing. Copyright © 2018. Published by Elsevier B.V.

IMAGE PROCESSING BASED OPTICAL CHARACTER RECOGNITION USING MATLAB

OpenAIRE

Jyoti Dalal*1 & Sumiran Daiya2

2018-01-01

Character recognition techniques associate a symbolic identity with the image of character. In a typical OCR systems input characters are digitized by an optical scanner. Each character is then located and segmented, and the resulting character image is fed into a pre-processor for noise reduction and normalization. Certain characteristics are the extracted from the character for classification. The feature extraction is critical and many different techniques exist, each having its strengths ...

Object detection and recognition in digital images theory and practice

CERN Document Server

Cyganek, Boguslaw

2013-01-01

Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Key features: Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications.

Polygonal approximation and scale-space analysis of closed digital curves

CERN Document Server

Ray, Kumar S

2013-01-01

This book covers the most important topics in the area of pattern recognition, object recognition, computer vision, robot vision, medical computing, computational geometry, and bioinformatics systems. Students and researchers will find a comprehensive treatment of polygonal approximation and its real life applications. The book not only explains the theoretical aspects but also presents applications with detailed design parameters. The systematic development of the concept of polygonal approximation of digital curves and its scale-space analysis are useful and attractive to scholars in many fi

Digital correlation applied to recognition and identification faces

International Nuclear Information System (INIS)

Arroyave, S.; Hernandez, L. J.; Torres, Cesar; Matos, Lorenzo

2009-01-01

It developed a system capable of recognizing faces of people from their facial features, the images are taken by the software automatically through a process of validating the presence of face to the camera lens, the digitized image is compared with a database that contains previously images captured, to subsequently be recognized and finally identified. The contribution of system set out is the fact that the acquisition of data is done in real time and using a web cam commercial usb interface offering an system equally optimal but much more economical. This tool is very effective in systems where the security is off vital importance, support with a high degree of verification to entities that possess databases with faces of people. (Author)

Digit recognition for Arabic/Jawi and Roman using features from triangle geometry

Science.gov (United States)

Azmi, Mohd Sanusi; Omar, Khairuddin; Nasrudin, Mohamad Faidzul; Idrus, Bahari; Wan Mohd Ghazali, Khadijah

2013-04-01

A novel method is proposed to recognize the Arab/Jawi and Roman digits. This new method is based on features from the triangle geometry, normalized into nine features. The features are used for zoning which results in five and 25 zones. The algorithm is validated by using three standard datasets which are publicly available and used by researchers in this field. The first dataset is HODA that contains 60,000 images for training and 20,000 images for testing. The second dataset is IFHCDB. This dataset has 52,380 isolated characters and 17,740 digits. Only the 17,740 images of digits are used for this research. For the roman digit, MNIST are chosen. MNIST dataset has 60,000 images for training and 10,000 images for testing. Supervised (SML) and Unsupervised Machine Learning (UML) are used to test the nine features. The SML used are Neural Network (NN) and Support Vector Machine (SVM). Whereas the UML uses Euclidean Distance Method with data mining algorithms; namely Mean Average Precision (eMAP) and Frequency Based (eFB). Results for SML testing for HODA dataset are 98.07% accuracy for SVM, and 96.73% for NN. For IFHCDB and MNIST the accuracy are 91.75% and 93.095% respectively. For the UML tests, HODA dataset is 93.91%, IFHCDB 85.94% and MNIST 86.61%. The train and test images are selected using both random and the original dataset's distribution. The results show that the accuracy of proposed algorithm is over 90% for each SML trained datasets where the highest result is the one that uses 25 zones features.

Histogram Equalization to Model Adaptation for Robust Speech Recognition

Directory of Open Access Journals (Sweden)

Suh Youngjoo

2010-01-01

Full Text Available We propose a new model adaptation method based on the histogram equalization technique for providing robustness in noisy environments. The trained acoustic mean models of a speech recognizer are adapted into environmentally matched conditions by using the histogram equalization algorithm on a single utterance basis. For more robust speech recognition in the heavily noisy conditions, trained acoustic covariance models are efficiently adapted by the signal-to-noise ratio-dependent linear interpolation between trained covariance models and utterance-level sample covariance models. Speech recognition experiments on both the digit-based Aurora2 task and the large vocabulary-based task showed that the proposed model adaptation approach provides significant performance improvements compared to the baseline speech recognizer trained on the clean speech data.

An effective approach for iris recognition using phase-based image matching.

Science.gov (United States)

Miyazawa, Kazuyuki; Ito, Koichi; Aoki, Takafumi; Kobayashi, Koji; Nakajima, Hiroshi

2008-10-01

This paper presents an efficient algorithm for iris recognition using phase-based image matching--an image matching technique using phase components in 2D Discrete Fourier Transforms (DFTs) of given images. Experimental evaluation using CASIA iris image databases (versions 1.0 and 2.0) and Iris Challenge Evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier Phase Code (FPC) for representing iris information. The 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art Digital Signal Processing (DSP) technology.

The development of the University of Jordan word recognition test.

Science.gov (United States)

Garadat, Soha N; Abdulbaqi, Khader J; Haj-Tas, Maisa A

2017-06-01

To develop and validate a digitally recorded speech test battery to assess speech perception in Jordanian Arabic-speaking adults. Selected stimuli were digitally recorded and were divided into four lists of 25 words each. Speech audiometry was completed for all listeners. Participants were divided into two equal groups of 30 listeners each with equal male to female ratio. The first group of participants completed speech reception thresholds (SRTs) and word recognition testing on each of the four lists using a fixed intensity. The second group of listeners was tested on each of the four lists at different intensity levels in order to obtain the performance-intensity function. Sixty normal-hearing listeners in the age range of 19-25 years. All participants were native speakers of Jordanian Arabic. Results revealed that there were no significant differences between SRTs and pure tone average. Additionally, there were no differences across lists at multiple intensity levels. In general, the current study was successful in producing recorded speech materials for Jordanian Arabic population. This suggests that the speech stimuli generated by this study are suitable for measuring speech recognition in Jordanian Arabic-speaking listeners.

Apply lightweight recognition algorithms in optical music recognition

Science.gov (United States)

Pham, Viet-Khoi; Nguyen, Hai-Dang; Nguyen-Khac, Tung-Anh; Tran, Minh-Triet

2015-02-01

The problems of digitalization and transformation of musical scores into machine-readable format are necessary to be solved since they help people to enjoy music, to learn music, to conserve music sheets, and even to assist music composers. However, the results of existing methods still require improvements for higher accuracy. Therefore, the authors propose lightweight algorithms for Optical Music Recognition to help people to recognize and automatically play musical scores. In our proposal, after removing staff lines and extracting symbols, each music symbol is represented as a grid of identical M ∗ N cells, and the features are extracted and classified with multiple lightweight SVM classifiers. Through experiments, the authors find that the size of 10 ∗ 12 cells yields the highest precision value. Experimental results on the dataset consisting of 4929 music symbols taken from 18 modern music sheets in the Synthetic Score Database show that our proposed method is able to classify printed musical scores with accuracy up to 99.56%.

Deep Transfer Metric Learning.

Science.gov (United States)

Junlin Hu; Jiwen Lu; Yap-Peng Tan; Jie Zhou

2016-12-01

Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption does not hold in many real visual recognition applications, especially when samples are captured across different data sets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML, where the output of both the hidden layers and the top layer are optimized jointly. To preserve the local manifold of input data points in the metric space, we present two new methods, DTML with autoencoder regularization and DSTML with autoencoder regularization. Experimental results on face verification, person re-identification, and handwritten digit recognition validate the effectiveness of the proposed methods.

Web Surveys to Digital Movies: Technological Tools of the Trade.

Science.gov (United States)

Fetterman, David M.

2002-01-01

Highlights some of the technological tools used by educational researchers today, focusing on data collection related tools such as Web surveys, digital photography, voice recognition and transcription, file sharing and virtual office, videoconferencing on the Internet, instantaneous chat and chat rooms, reporting and dissemination, and digital…

Embedded Face Detection and Recognition

Directory of Open Access Journals (Sweden)

Göksel Günlü

2012-10-01

Full Text Available The need to increase security in open or public spaces has in turn given rise to the requirement to monitor these spaces and analyse those images on-site and on-time. At this point, the use of smart cameras – of which the popularity has been increasing – is one step ahead. With sensors and Digital Signal Processors (DSPs, smart cameras generate ad hoc results by analysing the numeric images transmitted from the sensor by means of a variety of image-processing algorithms. Since the images are not transmitted to a distance processing unit but rather are processed inside the camera, it does not necessitate high-bandwidth networks or high processor powered systems; it can instantaneously decide on the required access. Nonetheless, on account of restricted memory, processing power and overall power, image processing algorithms need to be developed and optimized for embedded processors. Among these algorithms, one of the most important is for face detection and recognition. A number of face detection and recognition methods have been proposed recently and many of these methods have been tested on general-purpose processors. In smart cameras – which are real-life applications of such methods – the widest use is on DSPs. In the present study, the Viola-Jones face detection method – which was reported to run faster on PCs – was optimized for DSPs; the face recognition method was combined with the developed sub-region and mask-based DCT (Discrete Cosine Transform. As the employed DSP is a fixed-point processor, the processes were performed with integers insofar as it was possible. To enable face recognition, the image was divided into sub-regions and from each sub-region the robust coefficients against disruptive elements – like face expression, illumination, etc. – were selected as the features. The discrimination of the selected features was enhanced via LDA (Linear Discriminant Analysis and then employed for recognition. Thanks to its

Automatic Recognition Method for Optical Measuring Instruments Based on Machine Vision

Institute of Scientific and Technical Information of China (English)

SONG Le; LIN Yuchi; HAO Liguo

2008-01-01

Based on a comprehensive study of various algorithms, the automatic recognition of traditional ocular optical measuring instruments is realized. Taking a universal tools microscope (UTM) lens view image as an example, a 2-layer automatic recognition model for data reading is established after adopting a series of pre-processing algorithms. This model is an optimal combination of the correlation-based template matching method and a concurrent back propagation (BP) neural network. Multiple complementary feature extraction is used in generating the eigenvectors of the concurrent network. In order to improve fault-tolerance capacity, rotation invariant features based on Zernike moments are extracted from digit characters and a 4-dimensional group of the outline features is also obtained. Moreover, the operating time and reading accuracy can be adjusted dynamically by setting the threshold value. The experimental result indicates that the newly developed algorithm has optimal recognition precision and working speed. The average reading ratio can achieve 97.23%. The recognition method can automatically obtain the results of optical measuring instruments rapidly and stably without modifying their original structure, which meets the application requirements.

Automatic Modulation Recognition by Support Vector Machines Using Wavelet Kernel

Energy Technology Data Exchange (ETDEWEB)

Feng, X Z; Yang, J; Luo, F L; Chen, J Y; Zhong, X P [College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha (China)

2006-10-15

Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method.

Automatic Modulation Recognition by Support Vector Machines Using Wavelet Kernel

International Nuclear Information System (INIS)

Feng, X Z; Yang, J; Luo, F L; Chen, J Y; Zhong, X P

2006-01-01

Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method

Digital holographic-based cancellable biometric for personal authentication

International Nuclear Information System (INIS)

Verma, Gaurav; Sinha, Aloka

2016-01-01

In this paper, we propose a new digital holographic-based cancellable biometric scheme for personal authentication and verification. The realization of cancellable biometric is presented by using an optoelectronic experimental approach, in which an optically recorded hologram of the fingerprint of a person is numerically reconstructed. Each reconstructed feature has its own perspective, which is utilized to generate user-specific fingerprint features by using a feature-extraction process. New representations of the user-specific fingerprint features can be obtained from the same hologram, by changing the reconstruction distance (d) by an amount Δd between the recording plane and the reconstruction plane. This parameter is the key to make the cancellable user-specific fingerprint features using a digital holographic technique, which allows us to choose different reconstruction distances when reissuing the user-specific fingerprint features in the event of compromise. We have shown theoretically that each user-specific fingerprint feature has a unique identity with a high discrimination ability, and the chances of a match between them are minimal. In this aspect, a recognition system has also been demonstrated using the fingerprint biometric of the enrolled person at a particular reconstruction distance. For the performance evaluation of a fingerprint recognition system—the false acceptance ratio, the false rejection ratio and the equal error rate are calculated using correlation. The obtained results show good discrimination ability between the genuine and the impostor populations with the highest recognition rate of 98.23%. (paper)

Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

Directory of Open Access Journals (Sweden)

Tomasz Ciszewski

2006-01-01

Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

Automatic Type Recognition and Mapping of Global Tropical Cyclone Disaster Chains (TDC

Directory of Open Access Journals (Sweden)

Ran Wang

2016-10-01

Full Text Available The catastrophic events caused by meteorological disasters are becoming more severe in the context of global warming. The disaster chains triggered by Tropical Cyclones induce the serious losses of population and economy. It is necessary to make the regional type recognition of Tropical Cyclone Disaster Chain (TDC effective in order to make targeted preventions. This study mainly explores the method of automatic recognition and the mapping of TDC and designs a software system. We constructed an automatic recognition system in terms of the characteristics of a hazard-formative environment based on the theory of a natural disaster system. The ArcEngine components enable an intelligent software system to present results by the automatic mapping approach. The study data comes from global metadata such as Digital Elevation Model (DEM, terrain slope, population density and Gross Domestic Product (GDP. The result shows that: (1 according to the characteristic of geomorphology type, we establish a type of recognition system for global TDC; (2 based on the recognition principle, we design a software system with the functions of automatic recognition and mapping; and (3 we validate the type of distribution in terms of real cases of TDC. The result shows that the automatic recognition function has good reliability. The study can provide the basis for targeted regional disaster prevention strategy, as well as regional sustainable development.

Edge detection techniques for iris recognition system

International Nuclear Information System (INIS)

Tania, U T; Motakabber, S M A; Ibrahimy, M I

2013-01-01

Nowadays security and authentication are the major parts of our daily life. Iris is one of the most reliable organ or part of human body which can be used for identification and authentication purpose. To develop an iris authentication algorithm for personal identification, this paper examines two edge detection techniques for iris recognition system. Between the Sobel and the Canny edge detection techniques, the experimental result shows that the Canny's technique has better ability to detect points in a digital image where image gray level changes even at slow rate

Sadhana | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Sadhana. Bindu S Moni. Articles written in Sadhana. Volume 39 Issue 6 December 2014 pp 1333-1355. A novel handwritten character recognition system using gradient based features and run length count · G Raju Bindu S Moni Madhu S Nair · More Details Abstract Fulltext PDF. In this paper, we propose ...

Digital signals processing using non-linear orthogonal transformation in frequency domain

Directory of Open Access Journals (Sweden)

Ivanichenko E.V.

2017-12-01

Full Text Available The rapid progress of computer technology in recent decades led to a wide introduction of methods of digital information processing practically in all fields of scientific research. In this case, among various applications of computing one of the most important places is occupied by digital processing systems signals (DSP that are used in data processing remote solution tasks of navigation of aerospace and marine objects, communications, radiophysics, digital optics and in a number of other applications. Digital Signal Processing (DSP is a dynamically developing an area that covers both technical and software tools. Related areas for digital signal processing are theory information, in particular, the theory of optimal signal reception and theory pattern recognition. In the first case, the main problem is signal extraction against a background of noise and interference of a different physical nature, and in the second - automatic recognition, i.e. classification and signal identification. In the digital processing of signals under a signal, we mean its mathematical description, i.e. a certain real function, containing information on the state or behavior of a physical system under an event that can be defined on a continuous or discrete space of time variation or spatial coordinates. In the broad sense, DSP systems mean a complex algorithmic, hardware and software. As a rule, systems contain specialized technical means of preliminary (or primary signal processing and special technical means for secondary processing of signals. Means of pretreatment are designed to process the original signals observed in general case against a background of random noise and interference of a different physical nature and represented in the form of discrete digital samples, for the purpose of detecting and selection (selection of the useful signal and evaluation characteristics of the detected signal. A new method of digital signal processing in the frequency

Qualitative Research in the Digital Era: Obstacles and Opportunities

Directory of Open Access Journals (Sweden)

Ted Palys PhD

2012-09-01

Full Text Available Although the many sites and opportunities available to researchers through the development and proliferation of the Internet are well known, little attention has been paid to what digital technologies and the world's developing digital infrastructure can offer qualitative researchers for the actual process of doing research. This article discusses opportunities that now exist that we have experimented with and implemented in our own research, such as viral sampling strategies, wireless interviewing, and voice recognition transcription, as well as impediments we have encountered that stand in their way. Included in the latter are research ethics boards who often lack expertise in issues that arise in computer-assisted research, hardware/software costs and technological expertise for researchers, and university administrations who have not embraced infrastructure for qualitative research to the same extent they have supported quantitative research. The article closes with a look at the implications of emerging issues, such as the trend to cloud computing, the proliferation of mobile devices, and the maturation of voice recognition software.

Aplikasi MATLAB untuk Mengenali Karakter Tulisan Tangan

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ali mahmudi

2017-03-01

Full Text Available Handwriting recognition is one of the very interesting research object in the field of image processing, artificial intelligence and computer vision. This is due to the handwritten characters is varied in every individual. The style, size and orientation of handwriting characters has made every body’s is different, hence handwriting recognition is a very interesting research object. Handwriting recognition application has been used in quite many applications, such as reading the bank deposits, reading the postal code in letters, and helping peolple in managing documents. This paper presents a handwriting recognition application using Matlab. Matlab toolbox that is used in this research are Image Processing and Neural Network Toolbox.

American Sign Language Alphabet Recognition Using a Neuromorphic Sensor and an Artificial Neural Network

Directory of Open Access Journals (Sweden)

Miguel Rivera-Acosta

2017-09-01

Full Text Available This paper reports the design and analysis of an American Sign Language (ASL alphabet translation system implemented in hardware using a Field-Programmable Gate Array. The system process consists of three stages, the first being the communication with the neuromorphic camera (also called Dynamic Vision Sensor, DVS sensor using the Universal Serial Bus protocol. The feature extraction of the events generated by the DVS is the second part of the process, consisting of a presentation of the digital image processing algorithms developed in software, which aim to reduce redundant information and prepare the data for the third stage. The last stage of the system process is the classification of the ASL alphabet, achieved with a single artificial neural network implemented in digital hardware for higher speed. The overall result is the development of a classification system using the ASL signs contour, fully implemented in a reconfigurable device. The experimental results consist of a comparative analysis of the recognition rate among the alphabet signs using the neuromorphic camera in order to prove the proper operation of the digital image processing algorithms. In the experiments performed with 720 samples of 24 signs, a recognition accuracy of 79.58% was obtained.

The Design of a Single-Bit CMOS Image Sensor for Iris Recognition Applications

Directory of Open Access Journals (Sweden)

Keunyeol Park

2018-02-01

Full Text Available This paper presents a single-bit CMOS image sensor (CIS that uses a data processing technique with an edge detection block for simple iris segmentation. In order to recognize the iris image, the image sensor conventionally captures high-resolution image data in digital code, extracts the iris data, and then compares it with a reference image through a recognition algorithm. However, in this case, the frame rate decreases by the time required for digital signal conversion of multi-bit digital data through the analog-to-digital converter (ADC in the CIS. In order to reduce the overall processing time as well as the power consumption, we propose a data processing technique with an exclusive OR (XOR logic gate to obtain single-bit and edge detection image data instead of multi-bit image data through the ADC. In addition, we propose a logarithmic counter to efficiently measure single-bit image data that can be applied to the iris recognition algorithm. The effective area of the proposed single-bit image sensor (174 × 144 pixel is 2.84 mm2 with a 0.18 μm 1-poly 4-metal CMOS image sensor process. The power consumption of the proposed single-bit CIS is 2.8 mW with a 3.3 V of supply voltage and 520 frame/s of the maximum frame rates. The error rate of the ADC is 0.24 least significant bit (LSB on an 8-bit ADC basis at a 50 MHz sampling frequency.

The Design of a Single-Bit CMOS Image Sensor for Iris Recognition Applications.

Science.gov (United States)

Park, Keunyeol; Song, Minkyu; Kim, Soo Youn

2018-02-24

This paper presents a single-bit CMOS image sensor (CIS) that uses a data processing technique with an edge detection block for simple iris segmentation. In order to recognize the iris image, the image sensor conventionally captures high-resolution image data in digital code, extracts the iris data, and then compares it with a reference image through a recognition algorithm. However, in this case, the frame rate decreases by the time required for digital signal conversion of multi-bit digital data through the analog-to-digital converter (ADC) in the CIS. In order to reduce the overall processing time as well as the power consumption, we propose a data processing technique with an exclusive OR (XOR) logic gate to obtain single-bit and edge detection image data instead of multi-bit image data through the ADC. In addition, we propose a logarithmic counter to efficiently measure single-bit image data that can be applied to the iris recognition algorithm. The effective area of the proposed single-bit image sensor (174 × 144 pixel) is 2.84 mm² with a 0.18 μm 1-poly 4-metal CMOS image sensor process. The power consumption of the proposed single-bit CIS is 2.8 mW with a 3.3 V of supply voltage and 520 frame/s of the maximum frame rates. The error rate of the ADC is 0.24 least significant bit (LSB) on an 8-bit ADC basis at a 50 MHz sampling frequency.

Improved fingercode alignment for accurate and compact fingerprint recognition

CSIR Research Space (South Africa)

Brown, Dane

2016-05-01

Full Text Available Alignment for Accurate and Compact Fingerprint Recognition Dane Brown∗† and Karen Bradshaw∗ ∗Department of Computer Science Rhodes University Grahamstown, South Africa †Council for Scientific and Industrial Research Modelling and Digital Sciences Pretoria.... The experimental analysis and results are discussed in Section IV. Section V concludes the paper. II. RELATED STUDIES FingerCode [1] uses circular tessellation of filtered finger- print images centered at the reference point, which results in a circular ROI...

A Malaysian Vehicle License Plate Localization and Recognition System

OpenAIRE

Ganapathy Velappa; Dennis LUI Wen Lik

2008-01-01

Technological intelligence is a highly sought after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. In this paper, a license plate localization and recognition system for vehicles in Malaysia is proposed. This system is developed based on digital images and can be easily applied to commercial car park systems for the use of documenting access of parking services,...

Factors Affecting Sentence-in-Noise Recognition for Normal Hearing Listeners and Listeners with Hearing Loss.

Science.gov (United States)

Hwang, Jung Sun; Kim, Kyung Hyun; Lee, Jae Hee

2017-07-01

Despite amplified speech, listeners with hearing loss often report more difficulties understanding speech in background noise compared to normalhearing listeners. Various factors such as deteriorated hearing sensitivity, age, suprathreshold temporal resolution, and reduced capacity of working memory and attention can attribute to their sentence-in-noise problems. The present study aims to determine a primary explanatory factor for sentence-in-noise recognition difficulties in adults with or without hearing loss. Forty normal-hearing (NH) listeners (23-73 years) and thirty-four hearing-impaired (HI) listeners (24-80 years) participated for experimental testing. For both NH and HI group, the younger, middle-aged, older listeners were included. The sentence recognition score in noise was measured at 0 dB signal-to-noise ratio. The ability of temporal resolution was evaluated by gap detection performance using the Gaps-In-Noise test. Listeners' short-term auditory working memory span was measured by forward and backward digit spans. Overall, the HI listeners' sentence-in-noise recognition, temporal resolution abilities, and digit forward and backward spans were poorer compared to the NH listeners. Both NH and HI listeners had a substantial variability in performance. For NH listeners, only the digit backward span explained a small proportion of the variance in their sentence-in-noise performance. For the HI listeners, all the performance was influenced by age, and their sentence-in-noise difficulties were associated with various factors such as high-frequency hearing sensitivity, suprathreshold temporal resolution abilities, and working memory span. For the HI listeners, the critical predictors of the sentence-in-noise performance were composite measures of peripheral hearing sensitivity and suprathreshold temporal resolution abilities. The primary explanatory factors for the sentence-in-noise recognition performance differ between NH and HI listeners. Factors

8 CFR 1292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

Science.gov (United States)

2010-01-01

...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 1292.2...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization...

Threshold models of recognition and the recognition heuristic

Directory of Open Access Journals (Sweden)

Edgar Erdfelder

2011-02-01

Full Text Available According to the recognition heuristic (RH theory, decisions follow the recognition principle: Given a high validity of the recognition cue, people should prefer recognized choice options compared to unrecognized ones. Assuming that the memory strength of choice options is strongly correlated with both the choice criterion and recognition judgments, the RH is a reasonable strategy that approximates optimal decisions with a minimum of cognitive effort (Davis-Stober, Dana, and Budescu, 2010. However, theories of recognition memory are not generally compatible with this assumption. For example, some threshold models of recognition presume that recognition judgments can arise from two types of cognitive states: (1 certainty states in which judgments are almost perfectly correlated with memory strength and (2 uncertainty states in which recognition judgments reflect guessing rather than differences in memory strength. We report an experiment designed to test the prediction that the RH applies to certainty states only. Our results show that memory states rather than recognition judgments affect use of recognition information in binary decisions.

Convolutional Neural Network for Image Recognition

CERN Document Server

Seifnashri, Sahand

2015-01-01

The aim of this project is to use machine learning techniques especially Convolutional Neural Networks for image processing. These techniques can be used for Quark-Gluon discrimination using calorimeters data, but unfortunately I didn’t manage to get the calorimeters data and I just used the Jet data fromminiaodsim(ak4 chs). The Jet data was not good enough for Convolutional Neural Network which is designed for ’image’ recognition. This report is made of twomain part, part one is mainly about implementing Convolutional Neural Network on unphysical data such as MNIST digits and CIFAR-10 dataset and part 2 is about the Jet data.

Exploring neural cell dynamics with digital holographic microscopy

KAUST Repository

Marquet, Pierre; Depeursinge, Christian D.; Magistretti, Pierre J.

2013-01-01

In this review, we summarize how the new concept of digital optics applied to the field of holographic microscopy has allowed the development of a reliable and flexible digital holographic quantitative phase microscopy (DH-QPM) technique at the nanoscale particularly suitable for cell imaging. Particular emphasis is placed on the original biological ormation provided by the quantitative phase signal. We present the most relevant DH-QPM applications in the field of cell biology, including automated cell counts, recognition, classification, three-dimensional tracking, discrimination between physiological and pathophysiological states, and the study of cell membrane fluctuations at the nanoscale. In the last part, original results show how DH-QPM can address two important issues in the field of neurobiology, namely, multiple-site optical recording of neuronal activity and noninvasive visualization of dendritic spine dynamics resulting from a full digital holographic microscopy tomographic approach. Copyright © 2013 by Annual Reviews.

Exploring neural cell dynamics with digital holographic microscopy

KAUST Repository

Marquet, Pierre

2013-07-11

In this review, we summarize how the new concept of digital optics applied to the field of holographic microscopy has allowed the development of a reliable and flexible digital holographic quantitative phase microscopy (DH-QPM) technique at the nanoscale particularly suitable for cell imaging. Particular emphasis is placed on the original biological ormation provided by the quantitative phase signal. We present the most relevant DH-QPM applications in the field of cell biology, including automated cell counts, recognition, classification, three-dimensional tracking, discrimination between physiological and pathophysiological states, and the study of cell membrane fluctuations at the nanoscale. In the last part, original results show how DH-QPM can address two important issues in the field of neurobiology, namely, multiple-site optical recording of neuronal activity and noninvasive visualization of dendritic spine dynamics resulting from a full digital holographic microscopy tomographic approach. Copyright © 2013 by Annual Reviews.

Food marketing towards children: brand logo recognition, food-related behavior and BMI among 3-13-year-olds in a south Indian town.

Science.gov (United States)

Ueda, Peter; Tong, Leilei; Viedma, Cristobal; Chandy, Sujith J; Marrone, Gaetano; Simon, Anna; Stålsby Lundborg, Cecilia

2012-01-01

To assess exposure to marketing of unhealthy food products and its relation to food related behavior and BMI in children aged 3-13, from different socioeconomic backgrounds in a south Indian town. Child-parent pairs (n=306) were recruited at pediatric clinics. Exposure to food marketing was assessed by a digital logo recognition test. Children matched 18 logos of unhealthy food (high in fat/sugar/salt) featured in promotion material from the food industry to pictures of corresponding products. Children's nutritional knowledge, food preferences, purchase requests, eating behavior and socioeconomic characteristics were assessed by a digital game and parental questionnaires. Anthropometric measurements were recorded. Recognition rates for the brand logos ranged from 30% to 80%. Logo recognition ability increased with age (pfood preferences or purchase requests. Children from higher socioeconomic groups in the region had higher brand logo recognition ability and are possibly exposed to more food marketing. The study did not lend support to a link between exposure to marketing and poor eating behavior, distorted nutritional knowledge or increased purchase requests. The correlation between logo recognition and BMI warrants further investigation on food marketing towards children and its potential role in the increasing burden of non-communicable diseases in this part of India.

Developmental Changes in Face Recognition during Childhood: Evidence from Upright and Inverted Faces

Science.gov (United States)

de Heering, Adelaide; Rossion, Bruno; Maurer, Daphne

2012-01-01

Adults are experts at recognizing faces but there is controversy about how this ability develops with age. We assessed 6- to 12-year-olds and adults using a digitized version of the Benton Face Recognition Test, a sensitive tool for assessing face perception abilities. Children's response times for correct responses did not decrease between ages 6…

Application of X-ray digital radiography to online automated inspection of interior assembly structures of complex products

International Nuclear Information System (INIS)

Han Yueping; Han Yan; Li Ruihong; Wang Liming

2009-01-01

The paper proposes an application of X-ray digital radiography to online automated inspection and recognition of the interior assembly structures of complex products by means of the multiple views techniques. First, a vertical hybrid projection function (VHPF) is proposed as the recognition feature of a two-dimensional image. VHPF combines an integral projection function and a standard deviation function so that it can reflect the mean and the variance of the pixels in the vertical direction in an image. Secondly, by considering the different importance grades of objects inside the product and the independence of these objects along the circumference, the paper presents a hierarchical recognition method and uses a neural network system to speed up the computation process with parallel operations. Thirdly, using the whole-orientation features of one standard swatch and by extracting its maximal system of linear independence as the feature basis, the issue of blind areas for recognition is resolved. Based on this approach, the first domestic X-ray multi-view digital detection system has been developed and applied to the online detection of objects containing complicated assembly structures.

Higher-order neural network software for distortion invariant object recognition

Science.gov (United States)

Reid, Max B.; Spirkovska, Lilly

1991-01-01

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

Improvement of QR Code Recognition Based on Pillbox Filter Analysis

Directory of Open Access Journals (Sweden)

Jia-Shing Sheu

2013-04-01

Full Text Available The objective of this paper is to perform the innovation design for improving the recognition of a captured QR code image with blur through the Pillbox filter analysis. QR code images can be captured by digital video cameras. Many factors contribute to QR code decoding failure, such as the low quality of the image. Focus is an important factor that affects the quality of the image. This study discusses the out-of-focus QR code image and aims to improve the recognition of the contents in the QR code image. Many studies have used the pillbox filter (circular averaging filter method to simulate an out-of-focus image. This method is also used in this investigation to improve the recognition of a captured QR code image. A blurred QR code image is separated into nine levels. In the experiment, four different quantitative approaches are used to reconstruct and decode an out-of-focus QR code image. These nine reconstructed QR code images using methods are then compared. The final experimental results indicate improvements in identification.

78 FR 68981 - Electronic Retirement Processing

Science.gov (United States)

2013-11-18

.... Digitized signature means a graphical image of a handwritten signature usually created using a special... document. Smart card means a plastic card, typically the size of a credit card, containing an embedded integrated circuit or ``chip'' that can generate, store, or process data. A smart card can be used to...

Contribution to automatic speech recognition. Analysis of the direct acoustical signal. Recognition of isolated words and phoneme identification

International Nuclear Information System (INIS)

Dupeyrat, Benoit

1981-01-01

This report deals with the acoustical-phonetic step of the automatic recognition of the speech. The parameters used are the extrema of the acoustical signal (coded in amplitude and duration). This coding method, the properties of which are described, is simple and well adapted to a digital processing. The quality and the intelligibility of the coded signal after reconstruction are particularly satisfactory. An experiment for the automatic recognition of isolated words has been carried using this coding system. We have designed a filtering algorithm operating on the parameters of the coding. Thus the characteristics of the formants can be derived under certain conditions which are discussed. Using these characteristics the identification of a large part of the phonemes for a given speaker was achieved. Carrying on the studies has required the development of a particular methodology of real time processing which allowed immediate evaluation of the improvement of the programs. Such processing on temporal coding of the acoustical signal is extremely powerful and could represent, used in connection with other methods an efficient tool for the automatic processing of the speech.(author) [fr

Digitizing Villanova University's Eclipsing Binary Card Catalogue

Science.gov (United States)

Guzman, Giannina; Dalton, Briana; Conroy, Kyle; Prsa, Andrej

2018-01-01

Villanova University’s Department of Astrophysics and Planetary Science has years of hand-written archival data on Eclipsing Binaries at its disposal. This card catalog began at Princeton in the 1930’s with notable contributions from scientists such as Henry Norris Russel. During World War II, the archive was moved to the University of Pennsylvania, which was one of the world centers for Eclipsing Binary research, consequently, the contributions to the catalog during this time were immense. It was then moved to University of Florida at Gainesville before being accepted by Villanova in the 1990’s. The catalog has been kept in storage since then. The objective of this project is to digitize this archive and create a fully functional online catalog that contains the information available on the cards, along with the scan of the actual cards. Our group has built a database using a python-powered infrastructure to contain the collected data. The team also built a prototype web-based searchable interface as a front-end to the catalog. Following the data-entry process, information like the Right Ascension and Declination will be run against SIMBAD and any differences between values will be noted as part of the catalog. Information published online from the card catalog and even discrepancies in information for a star, could be a catalyst for new studies on these Eclipsing Binaries. Once completed, the database-driven interface will be made available to astronomers worldwide. The group will also acquire, from the database, a list of referenced articles that have yet to be found online in order to further pursue their digitization. This list will be comprised of references in the cards that were neither found on ADS nor online during the data-entry process. Pursuing the integration of these references to online queries such as ADS will be an ongoing process that will contribute and further facilitate studies on Eclipsing Binaries.

A Tale of Two Transcriptions. Machine-Assisted Transcription of Historical Sources

Directory of Open Access Journals (Sweden)

Gunnar Thorvaldsen

2015-01-01

Full Text Available This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM at the Autonomous University of Barcelona’s Center for Demographic Studies, the Barcelona Historical Marriage Database has been built. More than 600,000 records were transcribed by 150 transcribers working online. The Norwegian material is cross-sectional as it is the 1891 census, recorded on one sheet per person. This format and the underlining of keywords for several variables made it more feasible to semi-automate data entry than when many persons are listed on the same page. While Optical Character Recognition (OCR for printed text is scientifically mature, computer vision research is now focused on more difficult problems such as handwriting recognition. In the marriage project, document analysis methods have been proposed to automatically recognize the marriage licenses. Fully automatic recognition is still a challenge, but some promising results have been obtained. In Spain, Norway and elsewhere the source material is available as scanned pictures on the Internet, opening up the possibility for further international cooperation concerning automating the transcription of historic source materials. Like what is being done in projects to digitize printed materials, the optimal solution is likely to be a combination of manual transcription and machine-assisted recognition also for hand-written sources.

Recognition of facial and musical emotions in Parkinson's disease.

Science.gov (United States)

Saenz, A; Doé de Maindreville, A; Henry, A; de Labbey, S; Bakchine, S; Ehrlé, N

2013-03-01

Patients with amygdala lesions were found to be impaired in recognizing the fear emotion both from face and from music. In patients with Parkinson's disease (PD), impairment in recognition of emotions from facial expressions was reported for disgust, fear, sadness and anger, but no studies had yet investigated this population for the recognition of emotions from both face and music. The ability to recognize basic universal emotions (fear, happiness and sadness) from both face and music was investigated in 24 medicated patients with PD and 24 healthy controls. The patient group was tested for language (verbal fluency tasks), memory (digit and spatial span), executive functions (Similarities and Picture Completion subtests of the WAIS III, Brixton and Stroop tests), visual attention (Bells test), and fulfilled self-assessment tests for anxiety and depression. Results showed that the PD group was significantly impaired for recognition of both fear and sadness emotions from facial expressions, whereas their performance in recognition of emotions from musical excerpts was not different from that of the control group. The scores of fear and sadness recognition from faces were neither correlated to scores in tests for executive and cognitive functions, nor to scores in self-assessment scales. We attributed the observed dissociation to the modality (visual vs. auditory) of presentation and to the ecological value of the musical stimuli that we used. We discuss the relevance of our findings for the care of patients with PD. © 2012 The Author(s) European Journal of Neurology © 2012 EFNS.

Adults' strategies for simple addition and multiplication: verbal self-reports and the operand recognition paradigm.

Science.gov (United States)

Metcalfe, Arron W S; Campbell, Jamie I D

2011-05-01

Accurate measurement of cognitive strategies is important in diverse areas of psychological research. Strategy self-reports are a common measure, but C. Thevenot, M. Fanget, and M. Fayol (2007) proposed a more objective method to distinguish different strategies in the context of mental arithmetic. In their operand recognition paradigm, speed of recognition memory for problem operands after solving a problem indexes strategy (e.g., direct memory retrieval vs. a procedural strategy). Here, in 2 experiments, operand recognition time was the same following simple addition or multiplication, but, consistent with a wide variety of previous research, strategy reports indicated much greater use of procedures (e.g., counting) for addition than multiplication. Operation, problem size (e.g., 2 + 3 vs. 8 + 9), and operand format (digits vs. words) had interactive effects on reported procedure use that were not reflected in recognition performance. Regression analyses suggested that recognition time was influenced at least as much by the relative difficulty of the preceding problem as by the strategy used. The findings indicate that the operand recognition paradigm is not a reliable substitute for strategy reports and highlight the potential impact of difficulty-related carryover effects in sequential cognitive tasks.

Randomized Prediction Games for Adversarial Machine Learning.

Science.gov (United States)

Rota Bulo, Samuel; Biggio, Battista; Pillai, Ignazio; Pelillo, Marcello; Roli, Fabio

In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different evasion attacks and modifying the classification function accordingly. However, both the classification function and the simulated data manipulations have been modeled in a deterministic manner, without accounting for any form of randomization. In this paper, we overcome this limitation by proposing a randomized prediction game, namely, a noncooperative game-theoretic formulation in which the classifier and the attacker make randomized strategy selections according to some probability distribution defined over the respective strategy set. We show that our approach allows one to improve the tradeoff between attack detection and false alarms with respect to the state-of-the-art secure classifiers, even against attacks that are different from those hypothesized during design, on application examples including handwritten digit recognition, spam, and malware detection.In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different

Comparison of eye imaging pattern recognition using neural network

Science.gov (United States)

Bukhari, W. M.; Syed A., M.; Nasir, M. N. M.; Sulaima, M. F.; Yahaya, M. S.

2015-05-01

The beauty of eye recognition system that it is used in automatic identifying and verifies a human weather from digital images or video source. There are various behaviors of the eye such as the color of the iris, size of pupil and shape of the eye. This study represents the analysis, design and implementation of a system for recognition of eye imaging. All the eye images that had been captured from the webcam in RGB format must through several techniques before it can be input for the pattern and recognition processes. The result shows that the final value of weight and bias after complete training 6 eye images for one subject is memorized by the neural network system and be the reference value of the weight and bias for the testing part. The target classifies to 5 different types for 5 subjects. The eye images can recognize the subject based on the target that had been set earlier during the training process. When the values between new eye image and the eye image in the database are almost equal, it is considered the eye image is matched.

Food Marketing towards Children: Brand Logo Recognition, Food-Related Behavior and BMI among 3–13-Year-Olds in a South Indian Town

Science.gov (United States)

Ueda, Peter; Tong, Leilei; Viedma, Cristobal; Chandy, Sujith J.; Marrone, Gaetano; Simon, Anna; Stålsby Lundborg, Cecilia

2012-01-01

Objectives To assess exposure to marketing of unhealthy food products and its relation to food related behavior and BMI in children aged 3–13, from different socioeconomic backgrounds in a south Indian town. Methods Child-parent pairs (n = 306) were recruited at pediatric clinics. Exposure to food marketing was assessed by a digital logo recognition test. Children matched 18 logos of unhealthy food (high in fat/sugar/salt) featured in promotion material from the food industry to pictures of corresponding products. Children's nutritional knowledge, food preferences, purchase requests, eating behavior and socioeconomic characteristics were assessed by a digital game and parental questionnaires. Anthropometric measurements were recorded. Results Recognition rates for the brand logos ranged from 30% to 80%. Logo recognition ability increased with age (pfood preferences or purchase requests. Conclusions Children from higher socioeconomic groups in the region had higher brand logo recognition ability and are possibly exposed to more food marketing. The study did not lend support to a link between exposure to marketing and poor eating behavior, distorted nutritional knowledge or increased purchase requests. The correlation between logo recognition and BMI warrants further investigation on food marketing towards children and its potential role in the increasing burden of non-communicable diseases in this part of India. PMID:23082137

Sign Language Recognition System using Neural Network for Digital Hardware Implementation

International Nuclear Information System (INIS)

Vargas, Lorena P; Barba, Leiner; Torres, C O; Mattos, L

2011-01-01

This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

A RECOGNITION METHOD FOR AIRPLANE TARGETS USING 3D POINT CLOUD DATA

Directory of Open Access Journals (Sweden)

M. Zhou

2012-07-01

Full Text Available LiDAR is capable of obtaining three dimension coordinates of the terrain and targets directly and is widely applied in digital city, emergent disaster mitigation and environment monitoring. Especially because of its ability of penetrating the low density vegetation and canopy, LiDAR technique has superior advantages in hidden and camouflaged targets detection and recognition. Based on the multi-echo data of LiDAR, and combining the invariant moment theory, this paper presents a recognition method for classic airplanes (even hidden targets mainly under the cover of canopy using KD-Tree segmented point cloud data. The proposed algorithm firstly uses KD-tree to organize and manage point cloud data, and makes use of the clustering method to segment objects, and then the prior knowledge and invariant recognition moment are utilized to recognise airplanes. The outcomes of this test verified the practicality and feasibility of the method derived in this paper. And these could be applied in target measuring and modelling of subsequent data processing.

Optical and digital techniques for information security

CERN Document Server

2005-01-01

Optical and Digital Techniques for Information Security is the first book in a series focusing on Advanced Sciences and Technologies for Security Applications. This book encompases the results of research investigation and technologies used to secure, verify, recognize, track, and authenticate objects and information from theft, counterfeiting, and manipulation by unauthorized persons and agencies. This Information Security book will draw on the diverse expertise in optical sciences and engineering, digital image processing, imaging systems, information processing, computer based information systems, sensors, detectors, and biometrics to report innovative technologies that can be applied to information security issues. The Advanced Sciences and Technologies for Security Applications series focuses on research monographs in the areas of: -Recognition and identification (including optical imaging, biometrics, authentication, verification, and smart surveillance systems) -Biological and chemical threat detection...

The Spatiotemporal Dynamics of Digital News Audiences

DEFF Research Database (Denmark)

Peters, Chris

2016-01-01

of changing the socially-situated affordances of news use. Having sketched these contours, the chapter then highlights analytical challenges for understanding and conceptualizing the new interrelations between digital news content, production, and consumption, grounding this analysis with theoretical insights...... that emphasize the significance of spatiotemporal dynamics. The emphasis here is on the interrelations and mobilities of digital news audiences, based on a recognition of the productive impacts of media use while being careful to note the limitations of a paradigm shift that points solely to the possibilities...... generated by the ubiquitous presence of media in our everyday lives. Aspects of interaction and personalization beget by new media technologies certainly shape the possibilities, practices and power audiences have to choose news wherever, whenever, and however they want. However, this simultaneously...

Copyright and mass digitization a cross-jurisdictional perspective

CERN Document Server

Borghi, Maurizio

2013-01-01

In an age where works are increasingly being used, not only as works in the traditional sense, but also as carriers of data from which information may be automatically extracted for various purposes, Borghi and Karapapa consider whether mass digitisation is consistent with existing copyright principles, and ultimately whether copyright protection needs to be redefined, and if so how? The work considers the activities involved in the process of mass digitization identifying impediments to the increasing number of such projects such as the inapplicability of copyright exceptions, difficulties in rights clearance, and the issue of 'orphan' and out-of-print works. It goes on to examine the concept of 'use' of works in light of mass digital technologies and how it impinges on copyright law and principles; for example considering whether scanning and using optical character recognition in mass digital projects qualify as transformative use, or whether text mining on digitial repositories should be a permitted act...

Standard digital reference images for investment steel castings for aerospace applications

CERN Document Server

American Society for Testing and Materials. Philadelphia

2010-01-01

1.1 The digital reference images provided in the adjunct to this standard illustrate various types and degrees of discontinuities occurring in thin-wall steel investment castings. Use of this standard for the specification or grading of castings requires procurement of the adjunct digital reference images which illustrate the discontinuity types and severity levels. They are intended to provide the following: 1.1.1 A guide enabling recognition of thin-wall steel casting discontinuities and their differentiation both as to type and degree through digital radiographic examination. 1.1.2 Example digital radiographic illustrations of discontinuities and a nomenclature for reference in acceptance standards, specifications and drawings. 1.2 Two illustration categories are covered as follows: 1.2.1 Graded—Six common discontinuity types each illustrated in eight degrees of progressively increasing severity. 1.2.2 Ungraded—Twelve single illustrations of additional discontinuity types and of patterns and imper...

Working Memory Load Affects Processing Time in Spoken Word Recognition: Evidence from Eye-Movements

Science.gov (United States)

Hadar, Britt; Skrzypek, Joshua E.; Wingfield, Arthur; Ben-David, Boaz M.

2016-01-01

In daily life, speech perception is usually accompanied by other tasks that tap into working memory capacity. However, the role of working memory on speech processing is not clear. The goal of this study was to examine how working memory load affects the timeline for spoken word recognition in ideal listening conditions. We used the “visual world” eye-tracking paradigm. The task consisted of spoken instructions referring to one of four objects depicted on a computer monitor (e.g., “point at the candle”). Half of the trials presented a phonological competitor to the target word that either overlapped in the initial syllable (onset) or at the last syllable (offset). Eye movements captured listeners' ability to differentiate the target noun from its depicted phonological competitor (e.g., candy or sandal). We manipulated working memory load by using a digit pre-load task, where participants had to retain either one (low-load) or four (high-load) spoken digits for the duration of a spoken word recognition trial. The data show that the high-load condition delayed real-time target discrimination. Specifically, a four-digit load was sufficient to delay the point of discrimination between the spoken target word and its phonological competitor. Our results emphasize the important role working memory plays in speech perception, even when performed by young adults in ideal listening conditions. PMID:27242424

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.

Pattern recognition

CERN Document Server

Theodoridis, Sergios

2003-01-01

Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to ""learn"" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10

Semi-automated contour recognition using DICOMautomaton

International Nuclear Information System (INIS)

Clark, H; Duzenli, C; Wu, J; Moiseenko, V; Lee, R; Gill, B; Thomas, S

2014-01-01

Purpose: A system has been developed which recognizes and classifies Digital Imaging and Communication in Medicine contour data with minimal human intervention. It allows researchers to overcome obstacles which tax analysis and mining systems, including inconsistent naming conventions and differences in data age or resolution. Methods: Lexicographic and geometric analysis is used for recognition. Well-known lexicographic methods implemented include Levenshtein-Damerau, bag-of-characters, Double Metaphone, Soundex, and (word and character)-N-grams. Geometrical implementations include 3D Fourier Descriptors, probability spheres, boolean overlap, simple feature comparison (e.g. eccentricity, volume) and rule-based techniques. Both analyses implement custom, domain-specific modules (e.g. emphasis differentiating left/right organ variants). Contour labels from 60 head and neck patients are used for cross-validation. Results: Mixed-lexicographical methods show an effective improvement in more than 10% of recognition attempts compared with a pure Levenshtein-Damerau approach when withholding 70% of the lexicon. Domain-specific and geometrical techniques further boost performance. Conclusions: DICOMautomaton allows users to recognize contours semi-automatically. As usage increases and the lexicon is filled with additional structures, performance improves, increasing the overall utility of the system.

Real-time Multiresolution Crosswalk Detection with Walk Light Recognition for the Blind

Directory of Open Access Journals (Sweden)

ROMIC, K.

2018-02-01

Full Text Available Real-time image processing and object detection techniques have a great potential to be applied in digital assistive tools for the blind and visually impaired persons. In this paper, algorithm for crosswalk detection and walk light recognition is proposed with the main aim to help blind person when crossing the road. The proposed algorithm is optimized to work in real-time on portable devices using standard cameras. Images captured by camera are processed while person is moving and decision about detected crosswalk is provided as an output along with the information about walk light if one is present. Crosswalk detection method is based on multiresolution morphological image processing, while the walk light recognition is performed by proposed 6-stage algorithm. The main contributions of this paper are accurate crosswalk detection with small processing time due to multiresolution processing and the recognition of the walk lights covering only small amount of pixels in image. The experiment is conducted using images from video sequences captured in realistic situations on crossings. The results show 98.3% correct crosswalk detections and 89.5% correct walk lights recognition with average processing speed of about 16 frames per second.

Giro form reading machine

Science.gov (United States)

Minh Ha, Thien; Niggeler, Dieter; Bunke, Horst; Clarinval, Jose

1995-08-01

Although giro forms are used by many people in daily life for money remittance in Switzerland, the processing of these forms at banks and post offices is only partly automated. We describe an ongoing project for building an automatic system that is able to recognize various items printed or written on a giro form. The system comprises three main components, namely, an automatic form feeder, a camera system, and a computer. These components are connected in such a way that the system is able to process a bunch of forms without any human interactions. We present two real applications of our system in the field of payment services, which require the reading of both machine printed and handwritten information that may appear on a giro form. One particular feature of giro forms is their flexible layout, i.e., information items are located differently from one form to another, thus requiring an additional analysis step to localize them before recognition. A commercial optical character recognition software package is used for recognition of machine-printed information, whereas handwritten information is read by our own algorithms, the details of which are presented. The system is implemented by using a client/server architecture providing a high degree of flexibility to change. Preliminary results are reported supporting our claim that the system is usable in practice.

Evaluation of display on CRT by various processing digital images

International Nuclear Information System (INIS)

Toyama, Yasuhiko; Akagi, Naoki; Ohara, Shuichi; Maeda, Tomoho; Kitazoe, Yasuhiro; Yamamoto, Kouji

1986-01-01

In this study, we digitized three sheets of thin line chart X-ray photographs altered the photographic density. By selecting the width of the photographic density at displaying the images on the CRT, We could augment the contrast of images and more easily recognize line images compared with original X-ray photos. This characteristic was clearly observed within the region of low wave length. Though the easy recognition was got by adjusting the contrast, the sharpness of line images was not in accordance with it. As mentioned above, we discussed the relation between the contrast and the sharpness of digitized images obtained with a multi-format camera. (author)

Evaluation of display on CRT by various processing digital images

Energy Technology Data Exchange (ETDEWEB)

Toyama, Yasuhiko; Akagi, Naoki; Ohara, Shuichi; Maeda, Tomoho; Kitazoe, Yasuhiro; Yamamoto, Kouji

1986-12-01

In this study, we digitized three sheets of thin line chart X-ray photographs altered the photographic density. By selecting the width of the photographic density at displaying the images on the CRT, We could augment the contrast of images and more easily recognize line images compared with original X-ray photos. This characteristic was clearly observed within the region of low wave length. Though the easy recognition was got by adjusting the contrast, the sharpness of line images was not in accordance with it. As mentioned above, we discussed the relation between the contrast and the sharpness of digitized images obtained with a multi-format camera.

Preliminary study towards the development of copying skill assessment on dyslexic children in Jawi handwriting

Science.gov (United States)

Rahim, Kartini Abdul; Kahar, Rosmila Abdul; Khalid, Halimi Mohd.; Salleh, Rohayu Mohd; Hashim, Rathiah

2015-05-01

Recognition of Arabic handwritten and its variants such as Farsi (Persian) and Urdu had been receiving considerable attention in recent years. Being contrast to Arabic handwritten, Jawi, as a second method of Malay handwritten, has not been studied yet, but if any, there were a few references on it. The recent transformation in Malaysian education, the Special Education is one of the priorities in the Malaysia Blueprint. One of the special needs quoted in Malaysia education is dyslexia. A dyslexic student is considered as student with learning disability. Concluding a student is truly dyslexia might be incorrect for they were only assessed through Roman alphabet, without considering assessment via Jawi handwriting. A study was conducted on dyslexic students attending a special class for dyslexia in Malay Language to determine whether they are also dyslexia in Jawi handwriting. The focus of the study is to test the copying skills in relation to word reading and writing in Malay Language with and without dyslexia through both characters. A total of 10 dyslexic children and 10 normal children were recruited. In conclusion for future study, dyslexic students have less difficulty in performing Jawi handwriting in Malay Language through statistical analysis.

General digitalized system on nuclear power plants

International Nuclear Information System (INIS)

Akagi, Katsumi; Kadohara, Hozumi; Taniguchi, Manabu

2000-01-01

Hitherto, instrumentation control system in a PWR nuclear power plant has stepwisely adopted digital technology such as application of digital instrumentation control device to ordinary use (primary/secondary system control device, and so on), application of CRT display system to monitoring function, and so forth, to realize load reduction of an operator due to expansion of operation automation range, upgrading of reliability and maintenance due to self-diagnosis function, reduction of mass in cables due to multiple transfer, and upgrading of visual recognition due to information integration. In next term PWR plant instrumentation control system, under consideration of application practice of conventional digital technology, application of general digitalisation system to adopt digitalisation of overall instrumentation control system containing safety protection system, and central instrumentation system (new type of instrumentation system) and to intend to further upgrade economics, maintenance, operability/monitoring under security of reliability/safety is planned. And, together with embodiment of construction program of the next-term plant, verification at the general digitalisation proto-system aiming at establishment of basic technology on the system is carried out. Then, here was described on abstract of the general digitalisation system and characteristics of a digital type safety protection apparatus to be adopted in the next-term plant. (G.K.)

Retrieving handwriting by combining word spotting and manifold ranking

Science.gov (United States)

Peña Saldarriaga, Sebastián; Morin, Emmanuel; Viard-Gaudin, Christian

2012-01-01

Online handwritten data, produced with Tablet PCs or digital pens, consists in a sequence of points (x, y). As the amount of data available in this form increases, algorithms for retrieval of online data are needed. Word spotting is a common approach used for the retrieval of handwriting. However, from an information retrieval (IR) perspective, word spotting is a primitive keyword based matching and retrieval strategy. We propose a framework for handwriting retrieval where an arbitrary word spotting method is used, and then a manifold ranking algorithm is applied on the initial retrieval scores. Experimental results on a database of more than 2,000 handwritten newswires show that our method can improve the performances of a state-of-the-art word spotting system by more than 10%.

8 CFR 292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

Science.gov (United States)

2010-01-01

...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 292.2...; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization established in the United...

Connected word recognition using a cascaded neuro-computational model

Science.gov (United States)

Hoya, Tetsuya; van Leeuwen, Cees

2016-10-01

We propose a novel framework for processing a continuous speech stream that contains a varying number of words, as well as non-speech periods. Speech samples are segmented into word-tokens and non-speech periods. An augmented version of an earlier-proposed, cascaded neuro-computational model is used for recognising individual words within the stream. Simulation studies using both a multi-speaker-dependent and speaker-independent digit string database show that the proposed method yields a recognition performance comparable to that obtained by a benchmark approach using hidden Markov models with embedded training.

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

Science.gov (United States)

Islam, Kh Tohidul; Raj, Ram Gopal

2017-01-01

Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are ‘traffic light ahead’ or ‘pedestrian crossing’ indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications. PMID:28406471

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network.

Science.gov (United States)

Islam, Kh Tohidul; Raj, Ram Gopal

2017-04-13

Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are 'traffic light ahead' or 'pedestrian crossing' indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications.

Increasing the efficiency of digitization workflows for herbarium specimens.

Science.gov (United States)

Tulig, Melissa; Tarnowsky, Nicole; Bevans, Michael; Anthony Kirchgessner; Thiers, Barbara M

2012-01-01

The New York Botanical Garden Herbarium has been databasing and imaging its estimated 7.3 million plant specimens for the past 17 years. Due to the size of the collection, we have been selectively digitizing fundable subsets of specimens, making successive passes through the herbarium with each new grant. With this strategy, the average rate for databasing complete records has been 10 specimens per hour. With 1.3 million specimens databased, this effort has taken about 130,000 hours of staff time. At this rate, to complete the herbarium and digitize the remaining 6 million specimens, another 600,000 hours would be needed. Given the current biodiversity and economic crises, there is neither the time nor money to complete the collection at this rate.Through a combination of grants over the last few years, The New York Botanical Garden has been testing new protocols and tactics for increasing the rate of digitization through combinations of data collaboration, field book digitization, partial data entry and imaging, and optical character recognition (OCR) of specimen images. With the launch of the National Science Foundation's new Advancing Digitization of Biological Collections program, we hope to move forward with larger, more efficient digitization projects, capturing data from larger portions of the herbarium at a fraction of the cost and time.

Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition

Directory of Open Access Journals (Sweden)

Chia-Hung Lin

2010-01-01

Full Text Available This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katz's algorithm is employed to estimate the fractal dimension (FD from a two-dimensional (2D image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network (PNN as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

Facial recognition software success rates for the identification of 3D surface reconstructed facial images: implications for patient privacy and security.

Science.gov (United States)

Mazura, Jan C; Juluru, Krishna; Chen, Joseph J; Morgan, Tara A; John, Majnu; Siegel, Eliot L

2012-06-01

Image de-identification has focused on the removal of textual protected health information (PHI). Surface reconstructions of the face have the potential to reveal a subject's identity even when textual PHI is absent. This study assessed the ability of a computer application to match research subjects' 3D facial reconstructions with conventional photographs of their face. In a prospective study, 29 subjects underwent CT scans of the head and had frontal digital photographs of their face taken. Facial reconstructions of each CT dataset were generated on a 3D workstation. In phase 1, photographs of the 29 subjects undergoing CT scans were added to a digital directory and tested for recognition using facial recognition software. In phases 2-4, additional photographs were added in groups of 50 to increase the pool of possible matches and the test for recognition was repeated. As an internal control, photographs of all subjects were tested for recognition against an identical photograph. Of 3D reconstructions, 27.5% were matched correctly to corresponding photographs (95% upper CL, 40.1%). All study subject photographs were matched correctly to identical photographs (95% lower CL, 88.6%). Of 3D reconstructions, 96.6% were recognized simply as a face by the software (95% lower CL, 83.5%). Facial recognition software has the potential to recognize features on 3D CT surface reconstructions and match these with photographs, with implications for PHI.

Food marketing towards children: brand logo recognition, food-related behavior and BMI among 3-13-year-olds in a south Indian town.

Directory of Open Access Journals (Sweden)

Peter Ueda

Full Text Available OBJECTIVES: To assess exposure to marketing of unhealthy food products and its relation to food related behavior and BMI in children aged 3-13, from different socioeconomic backgrounds in a south Indian town. METHODS: Child-parent pairs (n=306 were recruited at pediatric clinics. Exposure to food marketing was assessed by a digital logo recognition test. Children matched 18 logos of unhealthy food (high in fat/sugar/salt featured in promotion material from the food industry to pictures of corresponding products. Children's nutritional knowledge, food preferences, purchase requests, eating behavior and socioeconomic characteristics were assessed by a digital game and parental questionnaires. Anthropometric measurements were recorded. RESULTS: Recognition rates for the brand logos ranged from 30% to 80%. Logo recognition ability increased with age (p<0.001 and socioeconomic level (p<0.001 comparing children in the highest and lowest of three socioeconomic groups. Adjusted for gender, age and socioeconomic group, logo recognition was associated with higher BMI (p=0.022 and nutritional knowledge (p<0.001 but not to unhealthy food preferences or purchase requests. CONCLUSIONS: Children from higher socioeconomic groups in the region had higher brand logo recognition ability and are possibly exposed to more food marketing. The study did not lend support to a link between exposure to marketing and poor eating behavior, distorted nutritional knowledge or increased purchase requests. The correlation between logo recognition and BMI warrants further investigation on food marketing towards children and its potential role in the increasing burden of non-communicable diseases in this part of India.

UN RESEAU DE NEURONES MULTICOUCHES POUR LA RECONNAISSANCE HORS LIGNE DES CARACTERES MANUSCRITS ARABES

Directory of Open Access Journals (Sweden)

S OUCHTATI

2002-06-01

Full Text Available In this paper, we present an off line method of Arabic Handwritten Characters Recognition. The study is based on the analysis of several performances of feature vectors. It is hoped that the results of the evaluation contribute to the conception of operational systems. The futures are the projection moments, Barr-features, and Fourier descriptors. The classification is achieved by a standard multi-layer perception.

Automatic analysis of digitized TV-images by a computer-driven optical microscope

International Nuclear Information System (INIS)

Rosa, G.; Di Bartolomeo, A.; Grella, G.; Romano, G.

1997-01-01

New methods of image analysis and three-dimensional pattern recognition were developed in order to perform the automatic scan of nuclear emulsion pellicles. An optical microscope, with a motorized stage, was equipped with a CCD camera and an image digitizer, and interfaced to a personal computer. Selected software routines inspired the design of a dedicated hardware processor. Fast operation, high efficiency and accuracy were achieved. First applications to high-energy physics experiments are reported. Further improvements are in progress, based on a high-resolution fast CCD camera and on programmable digital signal processors. Applications to other research fields are envisaged. (orig.)

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

Directory of Open Access Journals (Sweden)

Kan Li

2018-04-01

Full Text Available This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM speech processing as well as neuromorphic implementations based on spiking neural network (SNN, yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR regime.

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space.

Science.gov (United States)

Li, Kan; Príncipe, José C

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime.

Recognizing Chinese characters in digital ink from non-native language writers using hierarchical models

Science.gov (United States)

Bai, Hao; Zhang, Xi-wen

2017-06-01

While Chinese is learned as a second language, its characters are taught step by step from their strokes to components, radicals to components, and their complex relations. Chinese Characters in digital ink from non-native language writers are deformed seriously, thus the global recognition approaches are poorer. So a progressive approach from bottom to top is presented based on hierarchical models. Hierarchical information includes strokes and hierarchical components. Each Chinese character is modeled as a hierarchical tree. Strokes in one Chinese characters in digital ink are classified with Hidden Markov Models and concatenated to the stroke symbol sequence. And then the structure of components in one ink character is extracted. According to the extraction result and the stroke symbol sequence, candidate characters are traversed and scored. Finally, the recognition candidate results are listed by descending. The method of this paper is validated by testing 19815 copies of the handwriting Chinese characters written by foreign students.

Digital subtraction angiography in the assessment of cardiovascular disease

International Nuclear Information System (INIS)

Harrington, D.P.; Boxt, L.M.

1985-01-01

Digital subtraction angiography (DSA) is a new radiographic method for evaluating the cardiovascular system. It represents another in a continuing series of computer-assisted diagnostic imaging modalities. The advantages of this technique are its relatively noninvasive nature combined with diagnostically acceptable angiographic images of a variety of cardiovascular structures. Major clinical applications of DSA include its use in imaging of localized regions of peripheral arterial disease and as a screening procedure in evaluating extracranial carotid and vertebral artery disease and renovascular hypertension. Cardiac applications of DSA include assessment of ventricular function, recognition and quantification of intracardiac shunts, visualization of coronary artery bypass grafts, and the study of complex congenital cardiac malformations. Digital subtraction angiography may also be used to evaluate intracranial aneurysms and vascular tumors

Sound quality measures for speech in noise through a commercial hearing aid implementing digital noise reduction.

Science.gov (United States)

Ricketts, Todd A; Hornsby, Benjamin W Y

2005-05-01

This brief report discusses the affect of digital noise reduction (DNR) processing on aided speech recognition and sound quality measures in 14 adults fitted with a commercial hearing aid. Measures of speech recognition and sound quality were obtained in two different speech-in-noise conditions (71 dBA speech, +6 dB SNR and 75 dBA speech, +1 dB SNR). The results revealed that the presence or absence of DNR processing did not impact speech recognition in noise (either positively or negatively). Paired comparisons of sound quality for the same speech in noise signals, however, revealed a strong preference for DNR processing. These data suggest that at least one implementation of DNR processing is capable of providing improved sound quality, for speech in noise, in the absence of improved speech recognition.

Working memory affects older adults' use of context in spoken-word recognition.

Science.gov (United States)

Janse, Esther; Jesse, Alexandra

2014-01-01

Many older listeners report difficulties in understanding speech in noisy situations. Working memory and other cognitive skills may modulate older listeners' ability to use context information to alleviate the effects of noise on spoken-word recognition. In the present study, we investigated whether verbal working memory predicts older adults' ability to immediately use context information in the recognition of words embedded in sentences, presented in different listening conditions. In a phoneme-monitoring task, older adults were asked to detect as fast and as accurately as possible target phonemes in sentences spoken by a target speaker. Target speech was presented without noise, with fluctuating speech-shaped noise, or with competing speech from a single distractor speaker. The gradient measure of contextual probability (derived from a separate offline rating study) affected the speed of recognition. Contextual facilitation was modulated by older listeners' verbal working memory (measured with a backward digit span task) and age across listening conditions. Working memory and age, as well as hearing loss, were also the most consistent predictors of overall listening performance. Older listeners' immediate benefit from context in spoken-word recognition thus relates to their ability to keep and update a semantic representation of the sentence content in working memory.

Speech Recognition in Adults With Cochlear Implants: The Effects of Working Memory, Phonological Sensitivity, and Aging.

Science.gov (United States)

Moberly, Aaron C; Harris, Michael S; Boyce, Lauren; Nittrouer, Susan

2017-04-14

Models of speech recognition suggest that "top-down" linguistic and cognitive functions, such as use of phonotactic constraints and working memory, facilitate recognition under conditions of degradation, such as in noise. The question addressed in this study was what happens to these functions when a listener who has experienced years of hearing loss obtains a cochlear implant. Thirty adults with cochlear implants and 30 age-matched controls with age-normal hearing underwent testing of verbal working memory using digit span and serial recall of words. Phonological capacities were assessed using a lexical decision task and nonword repetition. Recognition of words in sentences in speech-shaped noise was measured. Implant users had only slightly poorer working memory accuracy than did controls and only on serial recall of words; however, phonological sensitivity was highly impaired. Working memory did not facilitate speech recognition in noise for either group. Phonological sensitivity predicted sentence recognition for implant users but not for listeners with normal hearing. Clinical speech recognition outcomes for adult implant users relate to the ability of these users to process phonological information. Results suggest that phonological capacities may serve as potential clinical targets through rehabilitative training. Such novel interventions may be particularly helpful for older adult implant users.

Recognition of diamond grains on surface of fine diamond grinding wheel

Institute of Scientific and Technical Information of China (English)

Fengwei HUO; Zhuji JIN; Renke KANG; Dongming GUO; Chun YANG

2008-01-01

The accurate evaluation of grinding wheel sur-face topography, which is necessary for the investigation of the grinding principle, optimism, modeling, and simu-lation of a grinding process, significantly depends on the accurate recognition of abrasive grains from the measured wheel surface. A detailed analysis of the grain size distri-bution characteristics and grain profile wavelength of the fine diamond grinding wheel used for ultra-precision grinding is presented. The requirements of the spatial sampling interval and sampling area for instruments to measure the surface topography of a diamond grinding wheel are discussed. To recognize diamond grains, digital filtering is used to eliminate the high frequency disturb-ance from the measured 3D digital surface of the grinding wheel, the geometric features of diamond grains are then extracted from the filtered 3D digital surface, and a method based on the grain profile frequency characteris-tics, diamond grain curvature, and distance between two adjacent diamond grains is proposed. A 3D surface pro-filer based on scanning white light interferometry is used to measure the 3D surface topography of a #3000 mesh resin bonded diamond grinding wheel, and the diamond grains are then recognized from the 3D digital surface. The experimental result shows that the proposed method is reasonable and effective.

Digital native magazines in the field of Sports in Spain: the case of MARCA Plus

Directory of Open Access Journals (Sweden)

Ignacio LABARGA-ADÁN

2018-01-01

Full Text Available Since its appearance in the summer of 2014, MARCA Plus has become a benchmark for digital native publications. A proof of the latter is the recognition made by Apple as one of the best 'apps' for iPad and to win the 'Digital Magazine Awards' in 2015 and 2016. This article aims to address the characteristics of this new format in the current media landscape: the one of the digital native magazines. The journal under study is one of the bets of Unidad Editorial for new technologies, having achieved an exceptional positioning in the digital market in three years. MARCA Plus stands out for its design, creativity, innovative character, interactivity and new audiovisual narratives, in addition to be an ideal support for new trends in advertising.

Some factors underlying individual differences in speech recognition on PRESTO: a first report.

Science.gov (United States)

Tamati, Terrin N; Gilbert, Jaimie L; Pisoni, David B

2013-01-01

Previous studies investigating speech recognition in adverse listening conditions have found extensive variability among individual listeners. However, little is currently known about the core underlying factors that influence speech recognition abilities. To investigate sensory, perceptual, and neurocognitive differences between good and poor listeners on the Perceptually Robust English Sentence Test Open-set (PRESTO), a new high-variability sentence recognition test under adverse listening conditions. Participants who fell in the upper quartile (HiPRESTO listeners) or lower quartile (LoPRESTO listeners) on key word recognition on sentences from PRESTO in multitalker babble completed a battery of behavioral tasks and self-report questionnaires designed to investigate real-world hearing difficulties, indexical processing skills, and neurocognitive abilities. Young, normal-hearing adults (N = 40) from the Indiana University community participated in the current study. Participants' assessment of their own real-world hearing difficulties was measured with a self-report questionnaire on situational hearing and hearing health history. Indexical processing skills were assessed using a talker discrimination task, a gender discrimination task, and a forced-choice regional dialect categorization task. Neurocognitive abilities were measured with the Auditory Digit Span Forward (verbal short-term memory) and Digit Span Backward (verbal working memory) tests, the Stroop Color and Word Test (attention/inhibition), the WordFam word familiarity test (vocabulary size), the Behavioral Rating Inventory of Executive Function-Adult Version (BRIEF-A) self-report questionnaire on executive function, and two performance subtests of the Wechsler Abbreviated Scale of Intelligence (WASI) Performance Intelligence Quotient (IQ; nonverbal intelligence). Scores on self-report questionnaires and behavioral tasks were tallied and analyzed by listener group (HiPRESTO and LoPRESTO). The extreme

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

Science.gov (United States)

Schafer, Phillip B; Jin, Dezhe Z

2014-03-01

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

Vision-based obstacle recognition system for automated lawn mower robot development

Science.gov (United States)

Mohd Zin, Zalhan; Ibrahim, Ratnawati

2011-06-01

Digital image processing techniques (DIP) have been widely used in various types of application recently. Classification and recognition of a specific object using vision system require some challenging tasks in the field of image processing and artificial intelligence. The ability and efficiency of vision system to capture and process the images is very important for any intelligent system such as autonomous robot. This paper gives attention to the development of a vision system that could contribute to the development of an automated vision based lawn mower robot. The works involve on the implementation of DIP techniques to detect and recognize three different types of obstacles that usually exist on a football field. The focus was given on the study on different types and sizes of obstacles, the development of vision based obstacle recognition system and the evaluation of the system's performance. Image processing techniques such as image filtering, segmentation, enhancement and edge detection have been applied in the system. The results have shown that the developed system is able to detect and recognize various types of obstacles on a football field with recognition rate of more 80%.

Filter and Filter Bank Design for Image Texture Recognition

Energy Technology Data Exchange (ETDEWEB)

Randen, Trygve

1997-12-31

The relevance of this thesis to energy and environment lies in its application to remote sensing such as for instance sea floor mapping and seismic pattern recognition. The focus is on the design of two-dimensional filters for feature extraction, segmentation, and classification of digital images with textural content. The features are extracted by filtering with a linear filter and estimating the local energy in the filter response. The thesis gives a review covering broadly most previous approaches to texture feature extraction and continues with proposals of some new techniques. 143 refs., 59 figs., 7 tabs.

Increasing the efficiency of digitization workflows for herbarium specimens

Directory of Open Access Journals (Sweden)

Melissa Tulig

2012-07-01

Full Text Available The New York Botanical Garden Herbarium has been databasing and imaging its estimated 7.3 million plant specimens for the past 17 years. Due to the size of the collection, we have been selectively digitizing fundable subsets of specimens, making successive passes through the herbarium with each new grant. With this strategy, the average rate for databasing complete records has been 10 specimens per hour. With 1.3 million specimens databased, this effort has taken about 130,000 hours of staff time. At this rate, to complete the herbarium and digitize the remaining 6 million specimens, another 600,000 hours would be needed. Given the current biodiversity and economic crises, there is neither the time nor money to complete the collection at this rate.Through a combination of grants over the last few years, The New York Botanical Garden has been testing new protocols and tactics for increasing the rate of digitization through combinations of data collaboration, field book digitization, partial data entry and imaging, and optical character recognition (OCR of specimen images. With the launch of the National Science Foundation’s new Advancing Digitization of Biological Collections program, we hope to move forward with larger, more efficient digitization projects, capturing data from larger portions of the herbarium at a fraction of the cost and time.

SAR: Stroke Authorship Recognition

KAUST Repository

Shaheen, Sara; Rockwood, Alyn; Ghanem, Bernard

2015-01-01

Are simple strokes unique to the artist or designer who renders them? If so, can this idea be used to identify authorship or to classify artistic drawings? Also, could training methods be devised to develop particular styles? To answer these questions, we propose the Stroke Authorship Recognition (SAR) approach, a novel method that distinguishes the authorship of 2D digitized drawings. SAR converts a drawing into a histogram of stroke attributes that is discriminative of authorship. We provide extensive classification experiments on a large variety of data sets, which validate SAR's ability to distinguish unique authorship of artists and designers. We also demonstrate the usefulness of SAR in several applications including the detection of fraudulent sketches, the training and monitoring of artists in learning a particular new style and the first quantitative way to measure the quality of automatic sketch synthesis tools. © 2015 The Eurographics Association and John Wiley & Sons Ltd.

SAR: Stroke Authorship Recognition

KAUST Repository

Shaheen, Sara

2015-10-15

Are simple strokes unique to the artist or designer who renders them? If so, can this idea be used to identify authorship or to classify artistic drawings? Also, could training methods be devised to develop particular styles? To answer these questions, we propose the Stroke Authorship Recognition (SAR) approach, a novel method that distinguishes the authorship of 2D digitized drawings. SAR converts a drawing into a histogram of stroke attributes that is discriminative of authorship. We provide extensive classification experiments on a large variety of data sets, which validate SAR\\'s ability to distinguish unique authorship of artists and designers. We also demonstrate the usefulness of SAR in several applications including the detection of fraudulent sketches, the training and monitoring of artists in learning a particular new style and the first quantitative way to measure the quality of automatic sketch synthesis tools. © 2015 The Eurographics Association and John Wiley & Sons Ltd.

Optical Pattern Recognition

Science.gov (United States)

Yu, Francis T. S.; Jutamulia, Suganda

2008-10-01

Contributors; Preface; 1. Pattern recognition with optics Francis T. S. Yu and Don A. Gregory; 2. Hybrid neural networks for nonlinear pattern recognition Taiwei Lu; 3. Wavelets, optics, and pattern recognition Yao Li and Yunglong Sheng; 4. Applications of the fractional Fourier transform to optical pattern recognition David Mendlovic, Zeev Zalesky and Haldum M. Oxaktas; 5. Optical implementation of mathematical morphology Tien-Hsin Chao; 6. Nonlinear optical correlators with improved discrimination capability for object location and recognition Leonid P. Yaroslavsky; 7. Distortion-invariant quadratic filters Gregory Gheen; 8. Composite filter synthesis as applied to pattern recognition Shizhou Yin and Guowen Lu; 9. Iterative procedures in electro-optical pattern recognition Joseph Shamir; 10. Optoelectronic hybrid system for three-dimensional object pattern recognition Guoguang Mu, Mingzhe Lu and Ying Sun; 11. Applications of photrefractive devices in optical pattern recognition Ziangyang Yang; 12. Optical pattern recognition with microlasers Eung-Gi Paek; 13. Optical properties and applications of bacteriorhodopsin Q. Wang Song and Yu-He Zhang; 14. Liquid-crystal spatial light modulators Aris Tanone and Suganda Jutamulia; 15. Representations of fully complex functions on real-time spatial light modulators Robert W. Cohn and Laurence G. Hassbrook; Index.

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

Directory of Open Access Journals (Sweden)

Feng Qin

Full Text Available Common leaf spot (caused by Pseudopeziza medicaginis, rust (caused by Uromyces striatus, Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana and Cercospora leaf spot (caused by Cercospora medicaginis are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis. After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection, disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features was the optimal model. For this SVM model, the

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

Science.gov (United States)

Qin, Feng; Liu, Dongxia; Sun, Bingda; Ruan, Liu; Ma, Zhanhong; Wang, Haiguang

2016-01-01

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the

A new selective developmental deficit: Impaired object recognition with normal face recognition.

Science.gov (United States)

Germine, Laura; Cashdollar, Nathan; Düzel, Emrah; Duchaine, Bradley

2011-05-01

Studies of developmental deficits in face recognition, or developmental prosopagnosia, have shown that individuals who have not suffered brain damage can show face recognition impairments coupled with normal object recognition (Duchaine and Nakayama, 2005; Duchaine et al., 2006; Nunn et al., 2001). However, no developmental cases with the opposite dissociation - normal face recognition with impaired object recognition - have been reported. The existence of a case of non-face developmental visual agnosia would indicate that the development of normal face recognition mechanisms does not rely on the development of normal object recognition mechanisms. To see whether a developmental variant of non-face visual object agnosia exists, we conducted a series of web-based object and face recognition tests to screen for individuals showing object recognition memory impairments but not face recognition impairments. Through this screening process, we identified AW, an otherwise normal 19-year-old female, who was then tested in the lab on face and object recognition tests. AW's performance was impaired in within-class visual recognition memory across six different visual categories (guns, horses, scenes, tools, doors, and cars). In contrast, she scored normally on seven tests of face recognition, tests of memory for two other object categories (houses and glasses), and tests of recall memory for visual shapes. Testing confirmed that her impairment was not related to a general deficit in lower-level perception, object perception, basic-level recognition, or memory. AW's results provide the first neuropsychological evidence that recognition memory for non-face visual object categories can be selectively impaired in individuals without brain damage or other memory impairment. These results indicate that the development of recognition memory for faces does not depend on intact object recognition memory and provide further evidence for category-specific dissociations in visual

Colorimetric Recognition of Aldehydes and Ketones.

Science.gov (United States)

Li, Zheng; Fang, Ming; LaGasse, Maria K; Askim, Jon R; Suslick, Kenneth S

2017-08-07

A colorimetric sensor array has been designed for the identification of and discrimination among aldehydes and ketones in vapor phase. Due to rapid chemical reactions between the solid-state sensor elements and gaseous analytes, distinct color difference patterns were produced and digitally imaged for chemometric analysis. The sensor array was developed from classical spot tests using aniline and phenylhydrazine dyes that enable molecular recognition of a wide variety of aliphatic or aromatic aldehydes and ketones, as demonstrated by hierarchical cluster, principal component, and support vector machine analyses. The aldehyde/ketone-specific sensors were further employed for differentiation among and identification of ten liquor samples (whiskies, brandy, vodka) and ethanol controls, showing its potential applications in the beverage industry. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Sea Level Data Archaeology for the Global Sea Level Observing System (GLOSS)

Science.gov (United States)

Bradshaw, Elizabeth; Matthews, Andy; Rickards, Lesley; Jevrejeva, Svetlana

2015-04-01

The Global Sea Level Observing System (GLOSS) was set up in 1985 to collect long term tide gauge observations and has carried out a number of data archaeology activities over the past decade, including sending member organisations questionnaires to report on their repositories. The GLOSS Group of Experts (GLOSS GE) is looking to future developments in sea level data archaeology and will provide its user community with guidance on finding, digitising, quality controlling and distributing historic records. Many records may not be held in organisational archives and may instead by in national libraries, archives and other collections. GLOSS will promote a Citizen Science approach to discovering long term records by providing tools for volunteers to report data. Tide gauge data come in two different formats, charts and hand-written ledgers. Charts are paper analogue records generated by the mechanical instrument driving a pen trace. Several GLOSS members have developed software to automatically digitise these charts and the various methods were reported in a paper on automated techniques for the digitization of archived mareograms, delivered to the GLOSS GE 13th meeting. GLOSS is creating a repository of software for scanning analogue charts. NUNIEAU is the only publically available software for digitising tide gauge charts but other organisations have developed their own tide gauge digitising software that is available internally. There are several other freely available software packages that convert image data to numerical values. GLOSS could coordinate a comparison study of the various different digitising software programs by: Sending the same charts to each organisation and asking everyone to digitise them using their own procedures Comparing the digitised data Providing recommendations to the GLOSS community The other major form of analogue sea level data is handwritten ledgers, which are usually observations of high and low waters, but sometimes contain higher

Research on improving image recognition robustness by combining multiple features with associative memory

Science.gov (United States)

Guo, Dongwei; Wang, Zhe

2018-05-01

Convolutional neural networks (CNN) achieve great success in computer vision, it can learn hierarchical representation from raw pixels and has outstanding performance in various image recognition tasks [1]. However, CNN is easy to be fraudulent in terms of it is possible to produce images totally unrecognizable to human eyes that CNNs believe with near certainty are familiar objects. [2]. In this paper, an associative memory model based on multiple features is proposed. Within this model, feature extraction and classification are carried out by CNN, T-SNE and exponential bidirectional associative memory neural network (EBAM). The geometric features extracted from CNN and the digital features extracted from T-SNE are associated by EBAM. Thus we ensure the recognition of robustness by a comprehensive assessment of the two features. In our model, we can get only 8% error rate with fraudulent data. In systems that require a high safety factor or some key areas, strong robustness is extremely important, if we can ensure the image recognition robustness, network security will be greatly improved and the social production efficiency will be extremely enhanced.

[Recognition of facial emotions and theory of mind in schizophrenia: could the theory of mind deficit be due to the non-recognition of facial emotions?].

Science.gov (United States)

Besche-Richard, C; Bourrin-Tisseron, A; Olivier, M; Cuervo-Lombard, C-V; Limosin, F

2012-06-01

The deficits of recognition of facial emotions and attribution of mental states are now well-documented in schizophrenic patients. However, we don't clearly know about the link between these two complex cognitive functions, especially in schizophrenia. In this study, we attempted to test the link between the recognition of facial emotions and the capacities of mentalization, notably the attribution of beliefs, in health and schizophrenic participants. We supposed that the level of performance of recognition of facial emotions, compared to the working memory and executive functioning, was the best predictor of the capacities to attribute a belief. Twenty schizophrenic participants according to DSM-IVTR (mean age: 35.9 years, S.D. 9.07; mean education level: 11.15 years, S.D. 2.58) clinically stabilized, receiving neuroleptic or antipsychotic medication participated in the study. They were matched on age (mean age: 36.3 years, S.D. 10.9) and educational level (mean educational level: 12.10, S.D. 2.25) with 30 matched healthy participants. All the participants were evaluated with a pool of tasks testing the recognition of facial emotions (the faces of Baron-Cohen), the attribution of beliefs (two stories of first order and two stories of second order), the working memory (the digit span of the WAIS-III and the Corsi test) and the executive functioning (Trail Making Test A et B, Wisconsin Card Sorting Test brief version). Comparing schizophrenic and healthy participants, our results confirmed a difference between the performances of the recognition of facial emotions and those of the attribution of beliefs. The result of the simple linear regression showed that the recognition of facial emotions, compared to the performances of working memory and executive functioning, was the best predictor of the performances in the theory of mind stories. Our results confirmed, in a sample of schizophrenic patients, the deficits in the recognition of facial emotions and in the

Dynamic Features for Iris Recognition.

Science.gov (United States)

da Costa, R M; Gonzaga, A

2012-08-01

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

ASERA: A spectrum eye recognition assistant for quasar spectra

Science.gov (United States)

Yuan, Hailong; Zhang, Haotong; Zhang, Yanxia; Lei, Yajuan; Dong, Yiqiao; Zhao, Yongheng

2013-11-01

Spectral type recognition is an important and fundamental step of large sky survey projects in the data reduction for further scientific research, like parameter measurement and statistic work. It tends out to be a huge job to manually inspect the low quality spectra produced from the massive spectroscopic survey, where the automatic pipeline may not provide confident type classification results. In order to improve the efficiency and effectiveness of spectral classification, we develop a semi-automated toolkit named ASERA, ASpectrum Eye Recognition Assistant. The main purpose of ASERA is to help the user in quasar spectral recognition and redshift measurement. Furthermore it can also be used to recognize various types of spectra of stars, galaxies and AGNs (Active Galactic Nucleus). It is an interactive software allowing the user to visualize observed spectra, superimpose template spectra from the Sloan Digital Sky Survey (SDSS), and interactively access related spectral line information. It is an efficient and user-friendly toolkit for the accurate classification of spectra observed by LAMOST (the Large Sky Area Multi-object Fiber Spectroscopic Telescope). The toolkit is available in two modes: a Java standalone application and a Java applet. ASERA has a few functions, such as wavelength and flux scale setting, zoom in and out, redshift estimation, spectral line identification, which helps user to improve the spectral classification accuracy especially for low quality spectra and reduce the labor of eyeball check. The function and performance of this tool is displayed through the recognition of several quasar spectra and a late type stellar spectrum from the LAMOST Pilot survey. Its future expansion capabilities are discussed.

Object Recognition System-on-Chip Using the Support Vector Machines

Directory of Open Access Journals (Sweden)

Houzet Dominique

2005-01-01

Full Text Available The first aim of this work is to propose the design of a system-on-chip (SoC platform dedicated to digital image and signal processing, which is tuned to implement efficiently multiply-and-accumulate (MAC vector/matrix operations. The second aim of this work is to implement a recent promising neural network method, namely, the support vector machine (SVM used for real-time object recognition, in order to build a vision machine. With such a reconfigurable and programmable SoC platform, it is possible to implement any SVM function dedicated to any object recognition problem. The final aim is to obtain an automatic reconfiguration of the SoC platform, based on the results of the learning phase on an objects' database, which makes it possible to recognize practically any object without manual programming. Recognition can be of any kind that is from image to signal data. Such a system is a general-purpose automatic classifier. Many applications can be considered as a classification problem, but are usually treated specifically in order to optimize the cost of the implemented solution. The cost of our approach is more important than a dedicated one, but in a near future, hundreds of millions of gates will be common and affordable compared to the design cost. What we are proposing here is a general-purpose classification neural network implemented on a reconfigurable SoC platform. The first version presented here is limited in size and thus in object recognition performances, but can be easily upgraded according to technology improvements.

Speech Recognition

Directory of Open Access Journals (Sweden)

Adrian Morariu

2009-01-01

Full Text Available This paper presents a method of speech recognition by pattern recognition techniques. Learning consists in determining the unique characteristics of a word (cepstral coefficients by eliminating those characteristics that are different from one word to another. For learning and recognition, the system will build a dictionary of words by determining the characteristics of each word to be used in the recognition. Determining the characteristics of an audio signal consists in the following steps: noise removal, sampling it, applying Hamming window, switching to frequency domain through Fourier transform, calculating the magnitude spectrum, filtering data, determining cepstral coefficients.

Psychometrically equivalent bisyllabic words for speech recognition threshold testing in Vietnamese.

Science.gov (United States)

Harris, Richard W; McPherson, David L; Hanson, Claire M; Eggett, Dennis L

2017-08-01

This study identified, digitally recorded, edited and evaluated 89 bisyllabic Vietnamese words with the goal of identifying homogeneous words that could be used to measure the speech recognition threshold (SRT) in native talkers of Vietnamese. Native male and female talker productions of 89 Vietnamese bisyllabic words were recorded, edited and then presented at intensities ranging from -10 to 20 dBHL. Logistic regression was used to identify the best words for measuring the SRT. Forty-eight words were selected and digitally edited to have 50% intelligibility at a level equal to the mean pure-tone average (PTA) for normally hearing participants (5.2 dBHL). Twenty normally hearing native Vietnamese participants listened to and repeated bisyllabic Vietnamese words at intensities ranging from -10 to 20 dBHL. A total of 48 male and female talker recordings of bisyllabic words with steep psychometric functions (>9.0%/dB) were chosen for the final bisyllabic SRT list. Only words homogeneous with respect to threshold audibility with steep psychometric function slopes were chosen for the final list. Digital recordings of bisyllabic Vietnamese words are now available for use in measuring the SRT for patients whose native language is Vietnamese.

Recognition and Toleration

DEFF Research Database (Denmark)

Lægaard, Sune

2010-01-01

Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms...... or interpretations of recognition and toleration are considered, confusing and problematic uses of the terms are noted, and the compatibility of toleration and recognition is discussed. The article argues that there is a range of legitimate and importantly different conceptions of both toleration and recognition...

Object-Oriented Wavelet-Layered Digital Watermarking Technique

Institute of Scientific and Technical Information of China (English)

LIU Xiao-yun; YU Jue-bang; LI Ming-yu

2005-01-01

In this paper, an object-oriented digital watermarking technique is proposed in the wavelet domain for still images. According to the difference of recognition degree of the human eye to the different region of the image, the image is divided into the interested region and uninterested region of human eye vision in this scheme. Using the relativity of position and the difference to ocular sensitivity of the multiresolution wavelet among each subband, the image is processed with layered watermarking append technique. Experimental results show that the proposed technique successfully survives image processing operations, additive noise and JPEG compression.

Digital Culture and Digital Library

Directory of Open Access Journals (Sweden)

Yalçın Yalçınkaya

2016-12-01

Full Text Available In this study; digital culture and digital library which have a vital connection with each other are examined together. The content of the research consists of the interaction of culture, information, digital culture, intellectual technologies, and digital library concepts. The study is an entry work to integrity of digital culture and digital library theories and aims to expand the symmetry. The purpose of the study is to emphasize the relation between the digital culture and digital library theories acting intersection of the subjects that are examined. Also the perspective of the study is based on examining the literature and analytical evaluation in both studies (digital culture and digital library. Within this context, the methodology of the study is essentially descriptive and has an attribute for the transmission and synthesis of distributed findings produced in the field of the research. According to the findings of the study results, digital culture is an inclusive term that describes the effects of intellectual technologies in the field of information and communication. Information becomes energy and the spectrum of the information is expanding in the vertical rise through the digital culture. In this context, the digital library appears as a new living space of a new environment. In essence, the digital library is information-oriented; has intellectual technology support and digital platform; is in a digital format; combines information resources and tools in relationship/communication/cooperation by connectedness, and also it is the dynamic face of the digital culture in time and space independence. Resolved with the study is that the digital libraries are active and effective in the formation of global knowing and/or mass wisdom in the process of digital culture.

Curated Collections for Educators: Five Key Papers on Evaluating Digital Scholarship.

Science.gov (United States)

Quinn, Antonia; Chan, Teresa M; Sampson, Christopher; Grossman, Catherine; Butts, Christine; Casey, John; Caretta-Weyer, Holly; Gottlieb, Michael

2018-01-03

Traditionally, scholarship that was recognized for promotion and tenure consisted of clinical research, bench research, and grant funding. Recent trends have allowed for differing approaches to scholarship, including digital publication. As increasing numbers of trainees and faculty turn to online educational resources, it is imperative to critically evaluate these resources. This article summarizes five key papers that address the appraisal of digital scholarship and describes their relevance to junior clinician educators and faculty developers. In May 2017, the Academic Life in Emergency Medicine Faculty Incubator program focused on the topic of digital scholarship, providing and discussing papers relevant to the topic. We augmented this list of papers with further suggestions by guest experts and by an open call via Twitter for other important papers. Through this process, we created a list of 38 papers in total on the topic of evaluating digital scholarship. In order to determine which of these papers best describe how to evaluate digital scholarship, the authorship group assessed the papers using a modified Delphi approach to build consensus. In this paper we present the five most highly rated papers from our process about evaluating digital scholarship. We summarize each paper and discuss its specific relevance to junior faculty members and to faculty developers. These papers provide a framework for assessing the quality of digital scholarship, so that junior faculty can recommend high-quality educational resources to their trainees. These papers help guide educators on how to produce high quality digital scholarship and maximize recognition and credit in respect to receiving promotion and tenure.

Investigating the Learning Challenges Presented by Digital Technologies to the College of Education in Kuwait University

Science.gov (United States)

Aldhafeeri, Fayiz; Male, Trevor

2016-01-01

There is now widespread recognition that digital technologies, particularly portable hand held devices capable of Internet connection, present opportunities and challenges to the way in which student learning is organized in schools, colleges and institutions of higher education in the 21st Century. Traxler, "Journal of the Research Centre…

Digital atlas of fetal brain MRI

Energy Technology Data Exchange (ETDEWEB)

Chapman, Teresa; Weinberger, E. [Department of Radiology, Seattle Children' s Hospital, Seattle, WA (United States); Matesan, Manuela [University of Washington, Department of Radiology, Seattle, WA (United States); Bulas, Dorothy I. [Division of Diagnostic Imaging and Radiology, Children' s National Medical Center, Washington, DC (United States)

2010-02-15

Fetal MRI can be performed in the second and third trimesters. During this time, the fetal brain undergoes profound structural changes. Interpretation of appropriate development might require comparison with normal age-based models. Consultation of a hard-copy atlas is limited by the inability to compare multiple ages simultaneously. To provide images of normal fetal brains from weeks 18 through 37 in a digital format that can be reviewed interactively. This will facilitate recognition of abnormal brain development. T2-W images for the atlas were obtained from fetal MR studies of normal brains scanned for other indications from 2005 to 2007. Images were oriented in standard axial, coronal and sagittal projections, with laterality established by situs. Gestational age was determined by last menstrual period, earliest US measurements and sonogram performed on the same day as the MR. The software program used for viewing the atlas, written in C, permits linked scrolling and resizing the images. Simultaneous comparison of varying gestational ages is permissible. Fetal brain images across gestational ages 18 to 37 weeks are provided as an interactive digital atlas and are available for free download. Improved interpretation of fetal brain abnormalities can be facilitated by the use of digital atlas cataloging of the normal changes throughout fetal development. Here we provide a description of the atlas and a discussion of normal fetal brain development. (orig.)

Digital atlas of fetal brain MRI

International Nuclear Information System (INIS)

Chapman, Teresa; Weinberger, E.; Matesan, Manuela; Bulas, Dorothy I.

2010-01-01

Fetal MRI can be performed in the second and third trimesters. During this time, the fetal brain undergoes profound structural changes. Interpretation of appropriate development might require comparison with normal age-based models. Consultation of a hard-copy atlas is limited by the inability to compare multiple ages simultaneously. To provide images of normal fetal brains from weeks 18 through 37 in a digital format that can be reviewed interactively. This will facilitate recognition of abnormal brain development. T2-W images for the atlas were obtained from fetal MR studies of normal brains scanned for other indications from 2005 to 2007. Images were oriented in standard axial, coronal and sagittal projections, with laterality established by situs. Gestational age was determined by last menstrual period, earliest US measurements and sonogram performed on the same day as the MR. The software program used for viewing the atlas, written in C, permits linked scrolling and resizing the images. Simultaneous comparison of varying gestational ages is permissible. Fetal brain images across gestational ages 18 to 37 weeks are provided as an interactive digital atlas and are available for free download. Improved interpretation of fetal brain abnormalities can be facilitated by the use of digital atlas cataloging of the normal changes throughout fetal development. Here we provide a description of the atlas and a discussion of normal fetal brain development. (orig.)

Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification

OpenAIRE

Hwang, Kyuyeon; Sung, Wonyong

2015-01-01

Connectionist temporal classification (CTC) based supervised sequence training of recurrent neural networks (RNNs) has shown great success in many machine learning areas including end-to-end speech and handwritten character recognition. For the CTC training, however, it is required to unroll (or unfold) the RNN by the length of an input sequence. This unrolling requires a lot of memory and hinders a small footprint implementation of online learning or adaptation. Furthermore, the length of tr...

Audio-Visual Speech Recognition Using Lip Information Extracted from Side-Face Images

Directory of Open Access Journals (Sweden)

Koji Iwano

2007-03-01

Full Text Available This paper proposes an audio-visual speech recognition method using lip information extracted from side-face images as an attempt to increase noise robustness in mobile environments. Our proposed method assumes that lip images can be captured using a small camera installed in a handset. Two different kinds of lip features, lip-contour geometric features and lip-motion velocity features, are used individually or jointly, in combination with audio features. Phoneme HMMs modeling the audio and visual features are built based on the multistream HMM technique. Experiments conducted using Japanese connected digit speech contaminated with white noise in various SNR conditions show effectiveness of the proposed method. Recognition accuracy is improved by using the visual information in all SNR conditions. These visual features were confirmed to be effective even when the audio HMM was adapted to noise by the MLLR method.

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

Directory of Open Access Journals (Sweden)

Ahmadi Majid

2003-01-01

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

[Industrial production of the LDRD "Siberia-N" digital radiographic devices].

Science.gov (United States)

Baru, S E; Ukraintsev, Iu G

2004-01-01

It is envisaged, as a key task, in the Federal Program on Tuberculosis Monitoring, that preventive measures and early TB detection is a priority. Fluorography, which is important for the recognition of pulmonary tuberculosis at its early stages, has been used in the diagnostics of pulmonary pathologies. However, according to the statistics provided by the Russian Ministry of Healthcare, around 80% of available medical equipment is now worn and obsolete. Owing to a fruitful research activity related with designing a digital low-dose X-Ray unit (Siberia-N) carried out by the Budker Institute of Nuclear Physics, Siberian Branch of the Russian Academy of Sciences (Novosibirsk), a certain progress can be stated in perfecting the fluorography equipment in Russia. The above unit incorporates all advanced achievements in the field of digital X-Ray diagnostics.

Retrospection. Uranium mining Wismut und the legal casualty insurance; Erinnerungen. Uranerzbergbau Wismut und die gesetzliche Unfallversicherung

Energy Technology Data Exchange (ETDEWEB)

Breuer, Joachim [Deutsche Gesetzliche Unfallversicherung (DGUV), Berlin (Germany)

2015-07-01

Although the Wismut uranium mining company in the former DDR had 600.000 employees, the company was not mentioned in the contract on the German reunification. The expenses for the health consequences imposed manifold challenges to the legal casualty insurance. The question of responsibility, the conservation, digitalization and evaluation of data concerning the personnel and health information, partially handwritten is a tremendous amount of work.

Retrospection. Uranium mining Wismut und the legal casualty insurance

International Nuclear Information System (INIS)

Breuer, Joachim

2015-01-01

Although the Wismut uranium mining company in the former DDR had 600.000 employees, the company was not mentioned in the contract on the German reunification. The expenses for the health consequences imposed manifold challenges to the legal casualty insurance. The question of responsibility, the conservation, digitalization and evaluation of data concerning the personnel and health information, partially handwritten is a tremendous amount of work.

Interactive Evolutionary Computing for the binarization of degenerated handwritten images

NARCIS (Netherlands)

van der Zant, Tijn; Schomaker, Lambert; Brink, Axel; Yanikoglu, BA; Berkner, K

2008-01-01

The digital cleaning of dirty and old documents and the binarization into a black/white image can be a tedious process. It is usually done by experts. In this article a method is shown that is easy for the end user. Untrained persons are able to do this task now while before an expert was needed.

Advances in image processing and pattern recognition. Proceedings of the international conference, Pisa, Italy, December 10-12, 1985

Energy Technology Data Exchange (ETDEWEB)

Cappellini, V [Florence Univ. (Italy); Consiglio Nazionale delle Ricerche, Florence (Italy). Ist. di Ricerca sulle Onde Elettromagnetiche); Marconi, R [IBM Scientific Center, Pisa (Italy); eds.

1986-01-01

The conference papers reported provide an authorative and permanent record of the contributions. Some papers are more theoretical or of review nature, while others contain new implementations and applications. They are conveniently grouped into the following 7 fields (after a general overview): Acquisition and Presentation of 2-D and 3-D Images; Static and Dynamic Image Processing; Determination of Object's Position and Orientation; Objects and Characters Recognition; Semantic Models and Image Understanding; Robotics and Computer Vision in Manufacturing; Specialized Processing Techniques and Structures. In particular, new digital image processing and recognition methods, implementation architectures and special advanced applications (industrial automation, robotics, remote sensing, biomedicine, etc.) are presented. (Auth.).

Models of digital competence and online activity of Russian adolescents

Directory of Open Access Journals (Sweden)

Galina U. Soldatova

2016-06-01

Full Text Available Having established the conception of digital competence consisting of four components (knowledge, skills, motivation and responsibility implemented in four areas (content, communication, consumption, and the techno-sphere, we propose the idea of models of digital competence as a specific systems of adolescents’ beliefs about their abilities and desires in the online world. These models (1 may be realistic or illusory, (2 their development is mediated by the motivation and online activity and (3 they regulate further online activities as well as the further development of digital competence. On the basis of nationwide study of digital competence (N=1203 Russian adolescents of 12-17 years using latent class method we revealed 5 models of digital competence corresponding to its lowest level, the average level at high and low motivation, high specific (in the components of skill and safety and high general level. It has been shown that higher appraisal of their digital competence is related to the opportunity of a more prolonged and self-service access to the Internet as well as the history of independent development of skills online. The illusion of digital competence is associated with a wide but shallow exploration activities online. Motivational component is related to the participation and recognition of the role of others in the development of digital competence, in comparison with others’ online skills and knowledge, as well as subjectively lower «digital divide» with parents. We suggest that the motivational component of the digital competence is developed if adolescent has a successful interaction via Internet, learn from other people and also if the range of her activities and interests online activity involves and requires the development of new skills. Based on digital competence model’s analysis, we have figured out 3 main types of Internet-users: (1 beginners, (2 experienced users, (3 advanced users. All these types fall into

Digital Collections, Digital Libraries & the Digitization of Cultural Heritage Information.

Science.gov (United States)

Lynch, Clifford

2002-01-01

Discusses digital collections and digital libraries. Topics include broadband availability; digital rights protection; content, both non-profit and commercial; digitization of cultural content; sustainability; metadata harvesting protocol; infrastructure; authorship; linking multiple resources; data mining; digitization of reference works;…

Re-thinking employee recognition: understanding employee experiences of recognition

OpenAIRE

Smith, Charlotte

2013-01-01

Despite widespread acceptance of the importance of employee recognition for both individuals and organisations and evidence of its increasing use in organisations, employee recognition has received relatively little focused attention from academic researchers. Particularly lacking is research exploring the lived experience of employee recognition and the interpretations and meanings which individuals give to these experiences. Drawing on qualitative interviews conducted as part of my PhD rese...

H. Sapiens Digital: From Digital Immigrants and Digital Natives to Digital Wisdom

Science.gov (United States)

Prensky, Marc

2009-01-01

As we move further into the 21st century, the digital native/digital immigrant paradigm created by Marc Prensky in 2001 is becoming less relevant. In this article, Prensky suggests that we should focus instead on the development of what he calls "digital wisdom." Arguing that digital technology can make us not just smarter but truly wiser, Prensky…

Digital Collections, Digital Libraries and the Digitization of Cultural Heritage Information.

Science.gov (United States)

Lynch, Clifford

2002-01-01

Discusses the development of digital collections and digital libraries. Topics include digitization of cultural heritage information; broadband issues; lack of compelling content; training issues; types of materials being digitized; sustainability; digital preservation; infrastructure; digital images; data mining; and future possibilities for…

Use of the recognition heuristic depends on the domain's recognition validity, not on the recognition validity of selected sets of objects.

Science.gov (United States)

Pohl, Rüdiger F; Michalkiewicz, Martha; Erdfelder, Edgar; Hilbig, Benjamin E

2017-07-01

According to the recognition-heuristic theory, decision makers solve paired comparisons in which one object is recognized and the other not by recognition alone, inferring that recognized objects have higher criterion values than unrecognized ones. However, success-and thus usefulness-of this heuristic depends on the validity of recognition as a cue, and adaptive decision making, in turn, requires that decision makers are sensitive to it. To this end, decision makers could base their evaluation of the recognition validity either on the selected set of objects (the set's recognition validity), or on the underlying domain from which the objects were drawn (the domain's recognition validity). In two experiments, we manipulated the recognition validity both in the selected set of objects and between domains from which the sets were drawn. The results clearly show that use of the recognition heuristic depends on the domain's recognition validity, not on the set's recognition validity. In other words, participants treat all sets as roughly representative of the underlying domain and adjust their decision strategy adaptively (only) with respect to the more general environment rather than the specific items they are faced with.

Morphological characterization of Mycobacterium tuberculosis in a MODS culture for an automatic diagnostics through pattern recognition.

Directory of Open Access Journals (Sweden)

Alicia Alva

Full Text Available Tuberculosis control efforts are hampered by a mismatch in diagnostic technology: modern optimal diagnostic tests are least available in poor areas where they are needed most. Lack of adequate early diagnostics and MDR detection is a critical problem in control efforts. The Microscopic Observation Drug Susceptibility (MODS assay uses visual recognition of cording patterns from Mycobacterium tuberculosis (MTB to diagnose tuberculosis infection and drug susceptibility directly from a sputum sample in 7-10 days with a low cost. An important limitation that laboratories in the developing world face in MODS implementation is the presence of permanent technical staff with expertise in reading MODS. We developed a pattern recognition algorithm to automatically interpret MODS results from digital images. The algorithm using image processing, feature extraction and pattern recognition determined geometrical and illumination features used in an object-model and a photo-model to classify TB-positive images. 765 MODS digital photos were processed. The single-object model identified MTB (96.9% sensitivity and 96.3% specificity and was able to discriminate non-tuberculous mycobacteria with a high specificity (97.1% M. avium, 99.1% M. chelonae, and 93.8% M. kansasii. The photo model identified TB-positive samples with 99.1% sensitivity and 99.7% specificity. This algorithm is a valuable tool that will enable automatic remote diagnosis using Internet or cellphone telephony. The use of this algorithm and its further implementation in a telediagnostics platform will contribute to both faster TB detection and MDR TB determination leading to an earlier initiation of appropriate treatment.

Graphical symbol recognition

OpenAIRE

K.C. , Santosh; Wendling , Laurent

2015-01-01

International audience; The chapter focuses on one of the key issues in document image processing i.e., graphical symbol recognition. Graphical symbol recognition is a sub-field of a larger research domain: pattern recognition. The chapter covers several approaches (i.e., statistical, structural and syntactic) and specially designed symbol recognition techniques inspired by real-world industrial problems. It, in general, contains research problems, state-of-the-art methods that convey basic s...

AN EFFICIENT SELF-UPDATING FACE RECOGNITION SYSTEM FOR PLASTIC SURGERY FACE

Directory of Open Access Journals (Sweden)

A. Devi

2016-08-01

Full Text Available Facial recognition system is fundamental a computer application for the automatic identification of a person through a digitized image or a video source. The major cause for the overall poor performance is related to the transformations in appearance of the user based on the aspects akin to ageing, beard growth, sun-tan etc. In order to overcome the above drawback, Self-update process has been developed in which, the system learns the biometric attributes of the user every time the user interacts with the system and the information gets updated automatically. The procedures of Plastic surgery yield a skilled and endurable means of enhancing the facial appearance by means of correcting the anomalies in the feature and then treating the facial skin with the aim of getting a youthful look. When plastic surgery is performed on an individual, the features of the face undergo reconstruction either locally or globally. But, the changes which are introduced new by plastic surgery remain hard to get modeled by the available face recognition systems and they deteriorate the performances of the face recognition algorithm. Hence the Facial plastic surgery produces changes in the facial features to larger extent and thereby creates a significant challenge to the face recognition system. This work introduces a fresh Multimodal Biometric approach making use of novel approaches to boost the rate of recognition and security. The proposed method consists of various processes like Face segmentation using Active Appearance Model (AAM, Face Normalization using Kernel Density Estimate/ Point Distribution Model (KDE-PDM, Feature extraction using Local Gabor XOR Patterns (LGXP and Classification using Independent Component Analysis (ICA. Efficient techniques have been used in each phase of the FRAS in order to obtain improved results.

Loose, Falling Characters and Sentences: The Persistence of the OCR Problem in Digital Repository E-Books

Science.gov (United States)

Kichuk, Diana

2015-01-01

The electronic conversion of scanned image files to readable text using optical character recognition (OCR) software and the subsequent migration of raw OCR text to e-book text file formats are key remediation or media conversion technologies used in digital repository e-book production. Despite real progress, the OCR problem of reliability and…

Problems and image processing in X-ray film digitization

International Nuclear Information System (INIS)

Kato, Syousuke; Yoshita, Hisashi; Kuranishi, Makoto; Itoh, Hajime; Mori, Kouichi; Konishi, Minoru

1992-01-01

Aiming at the realization of PACS, a study was conducted on the present state of, and various problems associated with, X-ray film digitization using a He-Ne laser-type film digitizer. Image quality was evaluated physically and clinically. With regard to the gradation specificity, the linear specificity was shown in a dynamic range of 4 figures. With regard to resolution specificity, visual evaluation was performed using a Hawlet Chart, with almost no difference being found between the CRT and laser printer output images and the decrease in resolution becoming more pronounced as the sampling pitch became greater. Clinical evaluation was performed with reference to the literature. The general evaluation of the clinicians was that although there was some deterioration for all of the shadows, (I have read this many times, but could not understand the last part.) by performing each of the kinds of image-processing enhancement of diagnostic ability was achieved, with a diagnosis being possible. The problem of unhindered diagnosis due to the development of artifacts from optical interference of the grid images projected onto the clinical pictures and digitizer sampling pitch was studied. As countermeasures, the use of a high density grid and adoption of a low-pass filter were useful in impending the development of artifacts. Regarding the operating problems, the inputting of index information requires a considerable number of manhours and a method of automatic recognition from digital data was introduced to overcome this problem. As future-prospects, the concepts of a practical system of X-ray film digitization and a film-screen system adapted to digitization were described. (author)

Problems and image processing in X-ray film digitization

Energy Technology Data Exchange (ETDEWEB)

Kato, Syousuke; Yoshita, Hisashi; Kuranishi, Makoto; Itoh, Hajime; Mori, Kouichi; Konishi, Minoru (Toyama Medical and Pharmaceutical Univ. (Japan). Hospital)

1992-11-01

Aiming at the realization of PACS, a study was conducted on the present state of, and various problems associated with, X-ray film digitization using a He-Ne laser-type film digitizer. Image quality was evaluated physically and clinically. With regard to the gradation specificity, the linear specificity was shown in a dynamic range of 4 figures. With regard to resolution specificity, visual evaluation was performed using a Hawlet Chart, with almost no difference being found between the CRT and laser printer output images and the decrease in resolution becoming more pronounced as the sampling pitch became greater. Clinical evaluation was performed with reference to the literature. The general evaluation of the clinicians was that although there was some deterioration for all of the shadows, (I have read this many times, but could not understand the last part.) by performing each of the kinds of image-processing enhancement of diagnostic ability was achieved, with a diagnosis being possible. The problem of unhindered diagnosis due to the development of artifacts from optical interference of the grid images projected onto the clinical pictures and digitizer sampling pitch was studied. As countermeasures, the use of a high density grid and adoption of a low-pass filter were useful in impending the development of artifacts. Regarding the operating problems, the inputting of index information requires a considerable number of manhours and a method of automatic recognition from digital data was introduced to overcome this problem. As future-prospects, the concepts of a practical system of X-ray film digitization and a film-screen system adapted to digitization were described. (author).

Searchable Signatures: Context and the Struggle for Recognition

Directory of Open Access Journals (Sweden)

Gina Schlesselman-Tarango

2013-09-01

Full Text Available Social networking sites made possible through Web 2.0 allow for unique user-generated tags called “searchable signatures.”  These tags move beyond the descriptive and act as means for users to assert online individual and group identities.  A study of searchable signatures on the Instagram application demonstrates that these types of tags are valuable not only because they allow for both individuals and groups to engage in what social theorist Axel Honneth calls the struggle for recognition, but also because they provide contextual use data and sociohistorical information so important to the understanding of digital objects.  This article explores how searchable signatures might be used by both patrons and staff in library environments.

3-D OBJECT RECOGNITION FROM POINT CLOUD DATA

Directory of Open Access Journals (Sweden)

W. Smith

2012-09-01

Full Text Available The market for real-time 3-D mapping includes not only traditional geospatial applications but also navigation of unmanned autonomous vehicles (UAVs. Massively parallel processes such as graphics processing unit (GPU computing make real-time 3-D object recognition and mapping achievable. Geospatial technologies such as digital photogrammetry and GIS offer advanced capabilities to produce 2-D and 3-D static maps using UAV data. The goal is to develop real-time UAV navigation through increased automation. It is challenging for a computer to identify a 3-D object such as a car, a tree or a house, yet automatic 3-D object recognition is essential to increasing the productivity of geospatial data such as 3-D city site models. In the past three decades, researchers have used radiometric properties to identify objects in digital imagery with limited success, because these properties vary considerably from image to image. Consequently, our team has developed software that recognizes certain types of 3-D objects within 3-D point clouds. Although our software is developed for modeling, simulation and visualization, it has the potential to be valuable in robotics and UAV applications. The locations and shapes of 3-D objects such as buildings and trees are easily recognizable by a human from a brief glance at a representation of a point cloud such as terrain-shaded relief. The algorithms to extract these objects have been developed and require only the point cloud and minimal human inputs such as a set of limits on building size and a request to turn on a squaring option. The algorithms use both digital surface model (DSM and digital elevation model (DEM, so software has also been developed to derive the latter from the former. The process continues through the following steps: identify and group 3-D object points into regions; separate buildings and houses from trees; trace region boundaries; regularize and simplify boundary polygons; construct complex

3-D Object Recognition from Point Cloud Data

Science.gov (United States)

Smith, W.; Walker, A. S.; Zhang, B.

2011-09-01

The market for real-time 3-D mapping includes not only traditional geospatial applications but also navigation of unmanned autonomous vehicles (UAVs). Massively parallel processes such as graphics processing unit (GPU) computing make real-time 3-D object recognition and mapping achievable. Geospatial technologies such as digital photogrammetry and GIS offer advanced capabilities to produce 2-D and 3-D static maps using UAV data. The goal is to develop real-time UAV navigation through increased automation. It is challenging for a computer to identify a 3-D object such as a car, a tree or a house, yet automatic 3-D object recognition is essential to increasing the productivity of geospatial data such as 3-D city site models. In the past three decades, researchers have used radiometric properties to identify objects in digital imagery with limited success, because these properties vary considerably from image to image. Consequently, our team has developed software that recognizes certain types of 3-D objects within 3-D point clouds. Although our software is developed for modeling, simulation and visualization, it has the potential to be valuable in robotics and UAV applications. The locations and shapes of 3-D objects such as buildings and trees are easily recognizable by a human from a brief glance at a representation of a point cloud such as terrain-shaded relief. The algorithms to extract these objects have been developed and require only the point cloud and minimal human inputs such as a set of limits on building size and a request to turn on a squaring option. The algorithms use both digital surface model (DSM) and digital elevation model (DEM), so software has also been developed to derive the latter from the former. The process continues through the following steps: identify and group 3-D object points into regions; separate buildings and houses from trees; trace region boundaries; regularize and simplify boundary polygons; construct complex roofs. Several case

Digital Humanities and networked digital media

DEFF Research Database (Denmark)

Finnemann, Niels Ole

2014-01-01

This article discusses digital humanities and the growing diversity of digital media, digital materials and digital methods. The first section describes the humanities computing tradition formed around the interpretation of computation as a rule-based process connected to a concept of digital...... materials centred on the digitisation of non-digital, finite works, corpora and oeuvres. The second section discusses “the big tent” of contemporary digital humanities. It is argued that there can be no unifying interpretation of digital humanities above the level of studying digital materials with the help...... of software-supported methods. This is so, in part, because of the complexity of the world and, in part, because digital media remain open to the projection of new epistemologies onto the functional architecture of these media. The third section discusses the heterogeneous character of digital materials...

Convolutional Sparse Coding for Static and Dynamic Images Analysis

Directory of Open Access Journals (Sweden)

B. A. Knyazev

2014-01-01

Full Text Available The objective of this work is to improve performance of static and dynamic objects recognition. For this purpose a new image representation model and a transformation algorithm are proposed. It is examined and illustrated that limitations of previous methods make it difficult to achieve this objective. Static images, specifically handwritten digits of the widely used MNIST dataset, are the primary focus of this work. Nevertheless, preliminary qualitative results of image sequences analysis based on the suggested model are presented.A general analytical form of the Gabor function, often employed to generate filters, is described and discussed. In this research, this description is required for computing parameters of responses returned by our algorithm. The recursive convolution operator is introduced, which allows extracting free shape features of visual objects. The developed parametric representation model is compared with sparse coding based on energy function minimization.In the experimental part of this work, errors of estimating the parameters of responses are determined. Also, parameters statistics and their correlation coefficients for more than 106 responses extracted from the MNIST dataset are calculated. It is demonstrated that these data correspond well with previous research studies on Gabor filters as well as with works on visual cortex primary cells of mammals, in which similar responses were observed. A comparative test of the developed model with three other approaches is conducted; speed and accuracy scores of handwritten digits classification are presented. A support vector machine with a linear or radial basic function is used for classification of images and their representations while principal component analysis is used in some cases to prepare data beforehand. High accuracy is not attained due to the specific difficulties of combining our model with a support vector machine (a 3.99% error rate. However, another method is

MFIRE-2: A Multi Agent System for Flow-Based Intrusion Detection Using Stochastic Search

Science.gov (United States)

2012-03-01

Algorithms to pattern recognition comes from Radtke et al. [72]. The authors apply Multi- Objective Genetic Algorithms (MOGAs) to two parts of a handwritten...Postel, J.B. “User Datagram Protocol. RFC 768”, 1980. [72] Radtke , Paulo V. W., Robert Sabourin, and Tony Wong. “Classification system optimization...Rennes 1, Suvisoft, La Baule (France), 10 2006. URL http://hal.inria.fr/inria-00104200/en/. [73] Radtke , P.V.W., T. Wong, and R. Sabourin. “A multi

IJIMAI Editor's Note - Vol. 4 Issue 6

Directory of Open Access Journals (Sweden)

Elena Verdú

2017-12-01

Full Text Available The International Journal of Interactive Multimedia and Artificial Intelligence provides an interdisciplinary forum in which scientists and professionals can share their research results and report new advances on Artificial Intelligence and Interactive Multimedia techniques. The research works presented in this regular issue are based on various topics of interest, among which are included: nature inspired optimization algorithms, multi-agent systems, fast motion estimation, handwritten recognition, supervised and unsupervised machine learning methods, or web mining.

Optical demodulation system for digitally encoded suspension array in fluoroimmunoassay

Science.gov (United States)

He, Qinghua; Li, Dongmei; He, Yonghong; Guan, Tian; Zhang, Yilong; Shen, Zhiyuan; Chen, Xuejing; Liu, Siyu; Lu, Bangrong; Ji, Yanhong

2017-09-01

A laser-induced breakdown spectroscopy and fluorescence spectroscopy-coupled optical system is reported to demodulate digitally encoded suspension array in fluoroimmunoassay. It takes advantage of the plasma emissions of assembled elemental materials to digitally decode the suspension array, providing a more stable and accurate recognition to target biomolecules. By separating the decoding procedure of suspension array and adsorption quantity calculation of biomolecules into two independent channels, the cross talk between decoding and label signals in traditional methods had been successfully avoided, which promoted the accuracy of both processes and realized more sensitive quantitative detection of target biomolecules. We carried a multiplexed detection of several types of anti-IgG to verify the quantitative analysis performance of the system. A limit of detection of 1.48×10-10 M was achieved, demonstrating the detection sensitivity of the optical demodulation system.

Statistical Pattern Recognition

CERN Document Server

Webb, Andrew R

2011-01-01

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.  It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields,

Digital atlas of fetal brain MRI.

Science.gov (United States)

Chapman, Teresa; Matesan, Manuela; Weinberger, Ed; Bulas, Dorothy I

2010-02-01

Fetal MRI can be performed in the second and third trimesters. During this time, the fetal brain undergoes profound structural changes. Interpretation of appropriate development might require comparison with normal age-based models. Consultation of a hard-copy atlas is limited by the inability to compare multiple ages simultaneously. To provide images of normal fetal brains from weeks 18 through 37 in a digital format that can be reviewed interactively. This will facilitate recognition of abnormal brain development. T2-W images for the atlas were obtained from fetal MR studies of normal brains scanned for other indications from 2005 to 2007. Images were oriented in standard axial, coronal and sagittal projections, with laterality established by situs. Gestational age was determined by last menstrual period, earliest US measurements and sonogram performed on the same day as the MR. The software program used for viewing the atlas, written in C#, permits linked scrolling and resizing the images. Simultaneous comparison of varying gestational ages is permissible. Fetal brain images across gestational ages 18 to 37 weeks are provided as an interactive digital atlas and are available for free download from http://radiology.seattlechildrens.org/teaching/fetal_brain . Improved interpretation of fetal brain abnormalities can be facilitated by the use of digital atlas cataloging of the normal changes throughout fetal development. Here we provide a description of the atlas and a discussion of normal fetal brain development.

3D interactive augmented reality-enhanced digital learning systems for mobile devices

Science.gov (United States)

Feng, Kai-Ten; Tseng, Po-Hsuan; Chiu, Pei-Shuan; Yang, Jia-Lin; Chiu, Chun-Jie

2013-03-01

With enhanced processing capability of mobile platforms, augmented reality (AR) has been considered a promising technology for achieving enhanced user experiences (UX). Augmented reality is to impose virtual information, e.g., videos and images, onto a live-view digital display. UX on real-world environment via the display can be e ectively enhanced with the adoption of interactive AR technology. Enhancement on UX can be bene cial for digital learning systems. There are existing research works based on AR targeting for the design of e-learning systems. However, none of these work focuses on providing three-dimensional (3-D) object modeling for en- hanced UX based on interactive AR techniques. In this paper, the 3-D interactive augmented reality-enhanced learning (IARL) systems will be proposed to provide enhanced UX for digital learning. The proposed IARL systems consist of two major components, including the markerless pattern recognition (MPR) for 3-D models and velocity-based object tracking (VOT) algorithms. Realistic implementation of proposed IARL system is conducted on Android-based mobile platforms. UX on digital learning can be greatly improved with the adoption of proposed IARL systems.

Effects of compression and individual variability on face recognition performance

Science.gov (United States)

McGarry, Delia P.; Arndt, Craig M.; McCabe, Steven A.; D'Amato, Donald P.

2004-08-01

The Enhanced Border Security and Visa Entry Reform Act of 2002 requires that the Visa Waiver Program be available only to countries that have a program to issue to their nationals machine-readable passports incorporating biometric identifiers complying with applicable standards established by the International Civil Aviation Organization (ICAO). In June 2002, the New Technologies Working Group of ICAO unanimously endorsed the use of face recognition (FR) as the globally interoperable biometric for machine-assisted identity confirmation with machine-readable travel documents (MRTDs), although Member States may elect to use fingerprint and/or iris recognition as additional biometric technologies. The means and formats are still being developed through which biometric information might be stored in the constrained space of integrated circuit chips embedded within travel documents. Such information will be stored in an open, yet unalterable and very compact format, probably as digitally signed and efficiently compressed images. The objective of this research is to characterize the many factors that affect FR system performance with respect to the legislated mandates concerning FR. A photograph acquisition environment and a commercial face recognition system have been installed at Mitretek, and over 1,400 images have been collected of volunteers. The image database and FR system are being used to analyze the effects of lossy image compression, individual differences, such as eyeglasses and facial hair, and the acquisition environment on FR system performance. Images are compressed by varying ratios using JPEG2000 to determine the trade-off points between recognition accuracy and compression ratio. The various acquisition factors that contribute to differences in FR system performance among individuals are also being measured. The results of this study will be used to refine and test efficient face image interchange standards that ensure highly accurate recognition, both

Rotation-invariant neural pattern recognition system with application to coin recognition.

Science.gov (United States)

Fukumi, M; Omatu, S; Takeda, F; Kosaka, T

1992-01-01

In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.

Dichotic and dichoptic digit perception in normal adults.

Science.gov (United States)

Lawfield, Angela; McFarland, Dennis J; Cacace, Anthony T

2011-06-01

Verbally based dichotic-listening experiments and reproduction-mediated response-selection strategies have been used for over four decades to study perceptual/cognitive aspects of auditory information processing and make inferences about hemispheric asymmetries and language lateralization in the brain. Test procedures using dichotic digits have also been used to assess for disorders of auditory processing. However, with this application, limitations exist and paradigms need to be developed to improve specificity of the diagnosis. Use of matched tasks in multiple sensory modalities is a logical approach to address this issue. Herein, we use dichotic listening and dichoptic viewing of visually presented digits for making this comparison. To evaluate methodological issues involved in using matched tasks of dichotic listening and dichoptic viewing in normal adults. A multivariate assessment of the effects of modality (auditory vs. visual), digit-span length (1-3 pairs), response selection (recognition vs. reproduction), and ear/visual hemifield of presentation (left vs. right) on dichotic and dichoptic digit perception. Thirty adults (12 males, 18 females) ranging in age from 18 to 30 yr with normal hearing sensitivity and normal or corrected-to-normal visual acuity. A computerized, custom-designed program was used for all data collection and analysis. A four-way repeated measures analysis of variance (ANOVA) evaluated the effects of modality, digit-span length, response selection, and ear/visual field of presentation. The ANOVA revealed that performances on dichotic listening and dichoptic viewing tasks were dependent on complex interactions between modality, digit-span length, response selection, and ear/visual hemifield of presentation. Correlation analysis suggested a common effect on overall accuracy of performance but isolated only an auditory factor for a laterality index. The variables used in this experiment affected performances in the auditory modality to a

A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS

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Taiji Lan

2017-10-01

Full Text Available The digital time delay integration (digital TDI technology of the complementary metal-oxide-semiconductor (CMOS image sensor has been widely adopted and developed in the optical remote sensing field. However, the details of targets that have low illumination or low contrast in scenarios of high contrast are often drowned out because of the superposition of multi-stage images in digital domain multiplies the read noise and the dark noise, thus limiting the imaging dynamic range. Through an in-depth analysis of the information transfer model of digital TDI, this paper attempts to explore effective ways to overcome this issue. Based on the evaluation and analysis of multi-stage images, the entropy-maximized adaptive histogram equalization (EMAHE algorithm is proposed to improve the ability of images to express the details of dark or low-contrast targets. Furthermore, in this paper, an image fusion method is utilized based on gradient pyramid decomposition and entropy weighting of different TDI stage images, which can improve the detection ability of the digital TDI CMOS for complex scenes with high contrast, and obtain images that are suitable for recognition by the human eye. The experimental results show that the proposed methods can effectively improve the high-dynamic-range imaging (HDRI capability of the digital TDI CMOS. The obtained images have greater entropy and average gradients.

A modern optical character recognition system in a real world clinical setting: some accuracy and feasibility observations.

Science.gov (United States)

Biondich, Paul G; Overhage, J Marc; Dexter, Paul R; Downs, Stephen M; Lemmon, Larry; McDonald, Clement J

2002-01-01

Advances in optical character recognition (OCR) software and computer hardware have stimulated a reevaluation of the technology and its ability to capture structured clinical data from preexisting paper forms. In our pilot evaluation, we measured the accuracy and feasibility of capturing vitals data from a pediatric encounter form that has been in use for over twenty years. We found that the software had a digit recognition rate of 92.4% (95% confidence interval: 91.6 to 93.2) overall. More importantly, this system was approximately three times as fast as our existing method of data entry. These preliminary results suggest that with further refinements in the approach and additional development, we may be able to incorporate OCR as another method for capturing structured clinical data.

Analysis And Voice Recognition In Indonesian Language Using MFCC And SVM Method

Directory of Open Access Journals (Sweden)

Harvianto Harvianto

2016-06-01

Full Text Available Voice recognition technology is one of biometric technology. Sound is a unique part of the human being which made an individual can be easily distinguished one from another. Voice can also provide information such as gender, emotion, and identity of the speaker. This research will record human voices that pronounce digits between 0 and 9 with and without noise. Features of this sound recording will be extracted using Mel Frequency Cepstral Coefficient (MFCC. Mean, standard deviation, max, min, and the combination of them will be used to construct the feature vectors. This feature vectors then will be classified using Support Vector Machine (SVM. There will be two classification models. The first one is based on the speaker and the other one based on the digits pronounced. The classification model then will be validated by performing 10-fold cross-validation.The best average accuracy from two classification model is 91.83%. This result achieved using Mean + Standard deviation + Min + Max as features.

Involving a young person in the development of a digital resource in nurse education.

Science.gov (United States)

Fenton, Gaynor

2014-01-01

Health policies across western societies have embedded the need for service user and carer perspectives in service design and delivery of educational programmes. There is a growing recognition of the need to include the perspectives of children and young people as service users in the design and delivery of child focused educational programmes. Digital storytelling provides a strategy for student nurses to gain insight into the lived experiences of children and young people. Engaging with these stories enables students to develop an understanding of a young persons' experience of healthcare. This paper outlines a project that developed a digital learning object based upon a young person's experience of cancer and student evaluations of the digital learning object as a teaching and learning strategy. Over 80% of students rated the digital learning object as interesting and were motivated to explore its content. In addition, the evaluation highlighted that listening to the young person's experiences of her treatment regimes was informative and assisted understanding of a patients' perspective of care delivery. Copyright © 2013 Elsevier Ltd. All rights reserved.

Digitizing Dissertations for an Institutional Repository: A Process and Cost Analysis*

Science.gov (United States)

Piorun, Mary; Palmer, Lisa A.

2008-01-01

Objective: This paper describes the Lamar Soutter Library's process and costs associated with digitizing 300 doctoral dissertations for a newly implemented institutional repository at the University of Massachusetts Medical School. Methodology: Project tasks included identifying metadata elements, obtaining and tracking permissions, converting the dissertations to an electronic format, and coordinating workflow between library departments. Each dissertation was scanned, reviewed for quality control, enhanced with a table of contents, processed through an optical character recognition function, and added to the institutional repository. Results: Three hundred and twenty dissertations were digitized and added to the repository for a cost of $23,562, or $0.28 per page. Seventy-four percent of the authors who were contacted (n = 282) granted permission to digitize their dissertations. Processing time per title was 170 minutes, for a total processing time of 906 hours. In the first 17 months, full-text dissertations in the collection were downloaded 17,555 times. Conclusion: Locally digitizing dissertations or other scholarly works for inclusion in institutional repositories can be cost effective, especially if small, defined projects are chosen. A successful project serves as an excellent recruitment strategy for the institutional repository and helps libraries build new relationships. Challenges include workflow, cost, policy development, and copyright permissions. PMID:18654648

Digitizing dissertations for an institutional repository: a process and cost analysis.

Science.gov (United States)

Piorun, Mary; Palmer, Lisa A

2008-07-01

This paper describes the Lamar Soutter Library's process and costs associated with digitizing 300 doctoral dissertations for a newly implemented institutional repository at the University of Massachusetts Medical School. Project tasks included identifying metadata elements, obtaining and tracking permissions, converting the dissertations to an electronic format, and coordinating workflow between library departments. Each dissertation was scanned, reviewed for quality control, enhanced with a table of contents, processed through an optical character recognition function, and added to the institutional repository. Three hundred and twenty dissertations were digitized and added to the repository for a cost of $23,562, or $0.28 per page. Seventy-four percent of the authors who were contacted (n = 282) granted permission to digitize their dissertations. Processing time per title was 170 minutes, for a total processing time of 906 hours. In the first 17 months, full-text dissertations in the collection were downloaded 17,555 times. Locally digitizing dissertations or other scholarly works for inclusion in institutional repositories can be cost effective, especially if small, defined projects are chosen. A successful project serves as an excellent recruitment strategy for the institutional repository and helps libraries build new relationships. Challenges include workflow, cost, policy development, and copyright permissions.

The Effects of Digital Communication in Cinema: ‘A New Film Language’

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Hidayet Hale Künüçen

2014-12-01

Full Text Available Delving into the digital world and technical development beneath it, the applications on communication have appeared as a promising source for transformation. Rapid progressing internet technology has turned the previously underdeveloped visual communication into an important domain as it is a needed practice to consider the means of communication in conjunction with the content and as far as the image in visual media is concerned, the question of how digital visual communication process is effected emerges. Beginning in 2000s, developments in digital communication technologies have led to a change in the perception of the film language in cinema. Processes dedicated to film-making such as dramatization, visualization, editing and distribution have gone through significant transition. A major contributing fact, DV (Digital Video, has made filmmaking available to amateur shooters which had before been dominated solely by professionals. Amateur filmmakers have begun shooting films of reasonably low costs and sharing them through internet platforms such as Youtube and Vimeo. The unexpected recognition by the audience and considerably high box office earnings have drawn attention to the language used in those films. The experiences and developments in digital communication require academics to correctly interpret the recent changes in filmmaking. Therefore, in this study, it is aimed to discuss the characteristics of the somewhat revolutionary ‘new film language’ introduced by amateur filmmakers and the effects of digital communication technologies in cinema.

PARTICLE SWARM OPTIMIZATION (PSO FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN

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Arie Rachmad Syulistyo

2016-02-01

Full Text Available Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO in Convolutional Neural Networks (CNNs, which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN.  The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.

Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning

Science.gov (United States)

Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

2016-07-01

Spiking Neural Networks (SNNs) have emerged as a powerful neuromorphic computing paradigm to carry out classification and recognition tasks. Nevertheless, the general purpose computing platforms and the custom hardware architectures implemented using standard CMOS technology, have been unable to rival the power efficiency of the human brain. Hence, there is a need for novel nanoelectronic devices that can efficiently model the neurons and synapses constituting an SNN. In this work, we propose a heterostructure composed of a Magnetic Tunnel Junction (MTJ) and a heavy metal as a stochastic binary synapse. Synaptic plasticity is achieved by the stochastic switching of the MTJ conductance states, based on the temporal correlation between the spiking activities of the interconnecting neurons. Additionally, we present a significance driven long-term short-term stochastic synapse comprising two unique binary synaptic elements, in order to improve the synaptic learning efficiency. We demonstrate the efficacy of the proposed synaptic configurations and the stochastic learning algorithm on an SNN trained to classify handwritten digits from the MNIST dataset, using a device to system-level simulation framework. The power efficiency of the proposed neuromorphic system stems from the ultra-low programming energy of the spintronic synapses.

Multirate Digital Filters Based on FPGA and Its Applications

International Nuclear Information System (INIS)

Sharaf El-Din, R.M.A.

2013-01-01

Digital Signal Processing (DSP) is one of the fastest growing techniques in the electronics industry. It is used in a wide range of application fields such as, telecommunications, data communications, image enhancement and processing, video signals, digital TV broadcasting, and voice synthesis and recognition. Field Programmable Gate Array (FPGA) offers good solution for addressing the needs of high performance DSP systems. The focus of this thesis is on one of the basic DSP functions, namely filtering signals to remove unwanted frequency bands. Multi rate Digital Filters (MDFs) are the main theme here. Theory and implementation of MDF, as a special class of digital filters, will be discussed. Multi rate digital filters represent a class of digital filters having a number of attractive features like, low requirements for the coefficient word lengths, significant saving in computation and storage requirements results in a significant reduction in its dynamic power consumption. This thesis introduces an efficient FPGA realization of a multi rate decimation filter with narrow pass-band and narrow transition band to reduce the frequency sample rate by factor of 64 for noise thermometer applications. The proposed multi rate decimation filter is composed of three stages; the first stage is a Cascaded Integrator Comb (CIC) decimation filter, the second stage is a two-coefficient Half-Band (HB) filter and the last stage is a sharper transition HB filter. The frequency responses of individual stages as well as the overall filter response have been demonstrated with full simulation using MATLAB. The design and implementation of the proposed MDF on FPGA (XILINX Virtex XCV800 BG432-4), using VHSIC Hardware Description Language (VHDL), has been introduced. The implementation areas of the proposed filter stages are compared. Using CIC-HB technique saves 18% of the design area, compared to using six stages HB decimation filters.

Digital Signal Processing for In-Vehicle Systems and Safety

CERN Document Server

Boyraz, Pinar; Takeda, Kazuya; Abut, Hüseyin

2012-01-01

Compiled from papers of the 4th Biennial Workshop on DSP (Digital Signal Processing) for In-Vehicle Systems and Safety this edited collection features world-class experts from diverse fields focusing on integrating smart in-vehicle systems with human factors to enhance safety in automobiles. Digital Signal Processing for In-Vehicle Systems and Safety presents new approaches on how to reduce driver inattention and prevent road accidents. The material addresses DSP technologies in adaptive automobiles, in-vehicle dialogue systems, human machine interfaces, video and audio processing, and in-vehicle speech systems. The volume also features: Recent advances in Smart-Car technology – vehicles that take into account and conform to the driver Driver-vehicle interfaces that take into account the driving task and cognitive load of the driver Best practices for In-Vehicle Corpus Development and distribution Information on multi-sensor analysis and fusion techniques for robust driver monitoring and driver recognition ...

Digital broadcasting

International Nuclear Information System (INIS)

Park, Ji Hyeong

1999-06-01

This book contains twelve chapters, which deals with digitization of broadcast signal such as digital open, digitization of video signal and sound signal digitization of broadcasting equipment like DTPP and digital VTR, digitization of equipment to transmit such as digital STL, digital FPU and digital SNG, digitization of transmit about digital TV transmit and radio transmit, digital broadcasting system on necessity and advantage, digital broadcasting system abroad and Korea, digital broadcasting of outline, advantage of digital TV, ripple effect of digital broadcasting and consideration of digital broadcasting, ground wave digital broadcasting of DVB-T in Europe DTV in U.S.A and ISDB-T in Japan, HDTV broadcasting, satellite broadcasting, digital TV broadcasting in Korea, digital radio broadcasting and new broadcasting service.

Preliminary studies on the impact of smoke on digital equipment

International Nuclear Information System (INIS)

Tanaka, T.J.; Korsah, K.; Antonescu, C.

1995-01-01

Last year the USNRC initiated a program at Sandia National Laboratories to determine the potential impact of smoke on advanced safety-related digitial instrumentation. In recognition of the fact that the reliability of safety-related equipment during or shortly after a fire in a nuclear power plant is more risk significant than long-term effects, we are concentrating on short-term failures. We exposed a multiplexer module board to three different types of smoke to determine whether the smoke would affect its operation. The operation of the multiplexer board was halted by one out of the three smoke exposures. In coordination with Oak Ridge National Laboratory, an experimental digital safety system was also smoke tested. The series of tests showed that smoke can cause potentially serious failures of a safety system. Most of these failures were intermittent and showed that smoke can temporarily interrupt communication between digital systems

From Digital Imaging to Computer Image Analysis of Fine Art

Science.gov (United States)

Stork, David G.

An expanding range of techniques from computer vision, pattern recognition, image analysis, and computer graphics are being applied to problems in the history of art. The success of these efforts is enabled by the growing corpus of high-resolution multi-spectral digital images of art (primarily paintings and drawings), sophisticated computer vision methods, and most importantly the engagement of some art scholars who bring questions that may be addressed through computer methods. This paper outlines some general problem areas and opportunities in this new inter-disciplinary research program.

Familiar Person Recognition: Is Autonoetic Consciousness More Likely to Accompany Face Recognition Than Voice Recognition?

Science.gov (United States)

Barsics, Catherine; Brédart, Serge

2010-11-01

Autonoetic consciousness is a fundamental property of human memory, enabling us to experience mental time travel, to recollect past events with a feeling of self-involvement, and to project ourselves in the future. Autonoetic consciousness is a characteristic of episodic memory. By contrast, awareness of the past associated with a mere feeling of familiarity or knowing relies on noetic consciousness, depending on semantic memory integrity. Present research was aimed at evaluating whether conscious recollection of episodic memories is more likely to occur following the recognition of a familiar face than following the recognition of a familiar voice. Recall of semantic information (biographical information) was also assessed. Previous studies that investigated the recall of biographical information following person recognition used faces and voices of famous people as stimuli. In this study, the participants were presented with personally familiar people's voices and faces, thus avoiding the presence of identity cues in the spoken extracts and allowing a stricter control of frequency exposure with both types of stimuli (voices and faces). In the present study, the rate of retrieved episodic memories, associated with autonoetic awareness, was significantly higher from familiar faces than familiar voices even though the level of overall recognition was similar for both these stimuli domains. The same pattern was observed regarding semantic information retrieval. These results and their implications for current Interactive Activation and Competition person recognition models are discussed.

Development of children's identity and position processing for letter, digit, and symbol strings: A cross-sectional study of the primary school years.

Science.gov (United States)

Schubert, Teresa; Badcock, Nicholas; Kohnen, Saskia

2017-10-01

Letter recognition and digit recognition are critical skills for literate adults, yet few studies have considered the development of these skills in children. We conducted a nine-alternative forced-choice (9AFC) partial report task with strings of letters and digits, with typographical symbols (e.g., $, @) as a control, to investigate the development of identity and position processing in children. This task allows for the delineation of identity processing (as overall accuracy) and position coding (as the proportion of position errors). Our participants were students in Grade 1 to Grade 6, allowing us to track the development of these abilities across the primary school years. Our data suggest that although digit processing and letter processing end up with many similarities in adult readers, the developmental trajectories for identity and position processing for the two character types differ. Symbol processing showed little developmental change in terms of identity or position accuracy. We discuss the implications of our results for theories of identity and position coding: modified receptive field, multiple-route model, and lexical tuning. Despite moderate success for some theories, considerable theoretical work is required to explain the developmental trajectories of letter processing and digit processing, which might not be as closely tied in child readers as they are in adult readers. Copyright © 2017 Elsevier Inc. All rights reserved.

Landsat TM band 431 combine on clustering analysis for pattern recognition land use using idrisi 4.2 software

International Nuclear Information System (INIS)

Wiweka, Arief H.; Izzawati, Tjahyaningsih A.

1997-01-01

The recognition of earth object's pattern which is recorded on remote sensing digital image can do by classification process based on the group of spectral pixel value. The spectral assessment on a spatial which represent the object characteristic can be helped through supervised or unsupervised. On certain case, there no media, such as maps, airborne, photo, the capability of field observation and the knowledge of object's location. Classification process can be done by clustering. The group of pixel based on the wide of the whole value interval of spectral image, then the class group base on the desired accuracy. The clustering method in Idris 4.2 software equipments are sequential method, statistic, iso data, and RGB. The clustering existence can help pre-process pattern recognition

[Prosopagnosia and facial expression recognition].

Science.gov (United States)

Koyama, Shinichi

2014-04-01

This paper reviews clinical neuropsychological studies that have indicated that the recognition of a person's identity and the recognition of facial expressions are processed by different cortical and subcortical areas of the brain. The fusiform gyrus, especially the right fusiform gyrus, plays an important role in the recognition of identity. The superior temporal sulcus, amygdala, and medial frontal cortex play important roles in facial-expression recognition. Both facial recognition and facial-expression recognition are highly intellectual processes that involve several regions of the brain.

Incorporating Speech Recognition into a Natural User Interface

Science.gov (United States)

Chapa, Nicholas

2017-01-01

The Augmented/ Virtual Reality (AVR) Lab has been working to study the applicability of recent virtual and augmented reality hardware and software to KSC operations. This includes the Oculus Rift, HTC Vive, Microsoft HoloLens, and Unity game engine. My project in this lab is to integrate voice recognition and voice commands into an easy to modify system that can be added to an existing portion of a Natural User Interface (NUI). A NUI is an intuitive and simple to use interface incorporating visual, touch, and speech recognition. The inclusion of speech recognition capability will allow users to perform actions or make inquiries using only their voice. The simplicity of needing only to speak to control an on-screen object or enact some digital action means that any user can quickly become accustomed to using this system. Multiple programs were tested for use in a speech command and recognition system. Sphinx4 translates speech to text using a Hidden Markov Model (HMM) based Language Model, an Acoustic Model, and a word Dictionary running on Java. PocketSphinx had similar functionality to Sphinx4 but instead ran on C. However, neither of these programs were ideal as building a Java or C wrapper slowed performance. The most ideal speech recognition system tested was the Unity Engine Grammar Recognizer. A Context Free Grammar (CFG) structure is written in an XML file to specify the structure of phrases and words that will be recognized by Unity Grammar Recognizer. Using Speech Recognition Grammar Specification (SRGS) 1.0 makes modifying the recognized combinations of words and phrases very simple and quick to do. With SRGS 1.0, semantic information can also be added to the XML file, which allows for even more control over how spoken words and phrases are interpreted by Unity. Additionally, using a CFG with SRGS 1.0 produces a Finite State Machine (FSM) functionality limiting the potential for incorrectly heard words or phrases. The purpose of my project was to

Pupil dilation during recognition memory: Isolating unexpected recognition from judgment uncertainty.

Science.gov (United States)

Mill, Ravi D; O'Connor, Akira R; Dobbins, Ian G

2016-09-01

Optimally discriminating familiar from novel stimuli demands a decision-making process informed by prior expectations. Here we demonstrate that pupillary dilation (PD) responses during recognition memory decisions are modulated by expectations, and more specifically, that pupil dilation increases for unexpected compared to expected recognition. Furthermore, multi-level modeling demonstrated that the time course of the dilation during each individual trial contains separable early and late dilation components, with the early amplitude capturing unexpected recognition, and the later trailing slope reflecting general judgment uncertainty or effort. This is the first demonstration that the early dilation response during recognition is dependent upon observer expectations and that separate recognition expectation and judgment uncertainty components are present in the dilation time course of every trial. The findings provide novel insights into adaptive memory-linked orienting mechanisms as well as the general cognitive underpinnings of the pupillary index of autonomic nervous system activity. Copyright © 2016 Elsevier B.V. All rights reserved.

PERSEPSI GURU TENTANG DIGITAL NATIVES, SUMBER BELAJAR DIGITAL DAN MOTIVASI MEMANFAATKAN SUMBER BELAJAR DIGITAL

Directory of Open Access Journals (Sweden)

Ferdinandus Bate Dopo

2016-06-01

TEACHER’S PERCEPTION OF DIGITAL NATIVES, DIGITAL LEARNING RESOURCES AND MOTIVATION TO UTILIZE DIGITAL LEARNING RESOURCES Abstract This study aims to reveal (1 the influence of teacher's perception of digital natives toward teacher’s motivation to utilize digital learning resources. (2 the influence of teacher's perception of digital learning resources toward teacher’s motivation to utilize digital learning resources and (3 the influence both of teacher's perception of digital natives and digital learning resources toward teacher’s motivation to utilize digital learning resources. This study used the descriptive-correlational quantitative approach. The Population and sample were high school teachers of Regina Pacis Bajawa, SMA Seminari Mataloko and SMA Negeri 1 Golewa. Sampling technique in this research was proportional random sampling. A questionnaire was used to obtain the data. The data were analyzed using the Likert scale. The instrument was developed based on lattice theory of assessment instruments relevant to the study variables. The analysis technique used is a regression followed by statistic technique of t test and F test with the significance level of 0.05. The results are as follows. (1 There is a positive and significant influence of teacher's perception of digital natives toward teacher’s motivation to utilize digital learning resources. (2 There is a positive and significant influence of teacher's perception of digital learning resources and teacher’s motivation to utilize digital learning resources. (3 There is a positive and significant influence both of teacher's perception of digital learning resources and teacher’s motivation to utilize digital learning resources. Keywords: perception, digital natives, digital learning resources, motivation

Kernel learning algorithms for face recognition

CERN Document Server

Li, Jun-Bao; Pan, Jeng-Shyang

2013-01-01

Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its new

Photonic quantum digital signatures operating over kilometer ranges in installed optical fiber

Science.gov (United States)

Collins, Robert J.; Fujiwara, Mikio; Amiri, Ryan; Honjo, Toshimori; Shimizu, Kaoru; Tamaki, Kiyoshi; Takeoka, Masahiro; Andersson, Erika; Buller, Gerald S.; Sasaki, Masahide

2016-10-01

The security of electronic communications is a topic that has gained noteworthy public interest in recent years. As a result, there is an increasing public recognition of the existence and importance of mathematically based approaches to digital security. Many of these implement digital signatures to ensure that a malicious party has not tampered with the message in transit, that a legitimate receiver can validate the identity of the signer and that messages are transferable. The security of most digital signature schemes relies on the assumed computational difficulty of solving certain mathematical problems. However, reports in the media have shown that certain implementations of such signature schemes are vulnerable to algorithmic breakthroughs and emerging quantum processing technologies. Indeed, even without quantum processors, the possibility remains that classical algorithmic breakthroughs will render these schemes insecure. There is ongoing research into information-theoretically secure signature schemes, where the security is guaranteed against an attacker with arbitrary computational resources. One such approach is quantum digital signatures. Quantum signature schemes can be made information-theoretically secure based on the laws of quantum mechanics while comparable classical protocols require additional resources such as anonymous broadcast and/or a trusted authority. Previously, most early demonstrations of quantum digital signatures required dedicated single-purpose hardware and operated over restricted ranges in a laboratory environment. Here, for the first time, we present a demonstration of quantum digital signatures conducted over several kilometers of installed optical fiber. The system reported here operates at a higher signature generation rate than previous fiber systems.

Technology Assessment and High-Speed Trains: facing the challenge of emergent digital society

OpenAIRE

Moretto, Susana Cristina dos Santos Gomes Martins

2017-01-01

The present PhD dissertation addresses the extension of selective environments of new technologies within the high-speed train technological system from business and regulations to the wider society. And, it argues the recognition of society as an actor in that system. Motivating it is the observed ever increase exposure of high-speed trains to public acceptance, caused by empowered society from fast ICT advancements. They refer to digitalization - the rise of social media and big data, co...

A Novel Sub-pixel Measurement Algorithm Based on Mixed the Fractal and Digital Speckle Correlation in Frequency Domain

Directory of Open Access Journals (Sweden)

Zhangfang Hu

2014-10-01

Full Text Available The digital speckle correlation is a non-contact in-plane displacement measurement method based on machine vision. Motivated by the facts that the low accuracy and large amount of calculation produced by the traditional digital speckle correlation method in spatial domain, we introduce a sub-pixel displacement measurement algorithm which employs a fast interpolation method based on fractal theory and digital speckle correlation in frequency domain. This algorithm can overcome either the blocking effect or the blurring caused by the traditional interpolation methods, and the frequency domain processing also avoids the repeated searching in the correlation recognition of the spatial domain, thus the operation quantity is largely reduced and the information extracting speed is improved. The comparative experiment is given to verify that the proposed algorithm in this paper is effective.

The Digital Image Processing And Quantitative Analysis In Microscopic Image Characterization

International Nuclear Information System (INIS)

Ardisasmita, M. Syamsa

2000-01-01

Many electron microscopes although have produced digital images, but not all of them are equipped with a supporting unit to process and analyse image data quantitatively. Generally the analysis of image has to be made visually and the measurement is realized manually. The development of mathematical method for geometric analysis and pattern recognition, allows automatic microscopic image analysis with computer. Image processing program can be used for image texture and structure periodic analysis by the application of Fourier transform. Because the development of composite materials. Fourier analysis in frequency domain become important for measure the crystallography orientation. The periodic structure analysis and crystal orientation are the key to understand many material properties like mechanical strength. stress, heat conductivity, resistance, capacitance and other material electric and magnetic properties. In this paper will be shown the application of digital image processing in microscopic image characterization and analysis in microscopic image

Sudden Event Recognition: A Survey

Directory of Open Access Journals (Sweden)

Mohd Asyraf Zulkifley

2013-08-01

Full Text Available Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1 the importance of a sudden event over a general anomalous event; (2 frameworks used in sudden event recognition; (3 the requirements and comparative studies of a sudden event recognition system and (4 various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition.

Oxycodone Ingestion Patterns in Acute Fracture Pain With Digital Pills.

Science.gov (United States)

Chai, Peter R; Carreiro, Stephanie; Innes, Brendan J; Chapman, Brittany; Schreiber, Kristin L; Edwards, Robert R; Carrico, Adam W; Boyer, Edward W

2017-12-01

Opioid analgesics are commonly prescribed on an as-needed (PRN) basis for acute painful conditions. Uncertainty of how patients actually take PRN opioids, coupled with a desire to completely cover pain, leads to variable and overly generous opioid prescribing practices, resulting in a surplus of opioids. This opioid surplus becomes a source for diversion and nonmedical opioid use. Understanding patterns of actual opioid ingestion after acute painful conditions can help clinicians counsel patients on safe opioid use, and allow timely recognition and intervention when escalating opioid self-dosing occurs, to prevent tolerance and addiction. We used a novel oxycodone digital pill system (ingestible biosensor within a standard gelatin capsule combined with 5-mg oxycodone) that when ingested, is activated by the chloride ion gradient in the stomach thereby emitting a radiofrequency signal captured by a wearable reader. The reader relays ingestion data to a cloud-based server that displays ingestion events to the study team. We deployed the oxycodone digital pill among opioid-naive individuals discharged from the emergency department with acute fracture pain. Participants were trained on digital pill operation and discharged with twenty-one 5-mg oxycodone digital pills. They were instructed to take digital pills PRN for pain on discharge. We conducted a brief interview 7 days after study enrollment, at which point participants returned the digital pill system. We identified oxycodone ingestion events in real time by data from the digital pill system and performed pill counts at the return visit to validate digital pill reporting of medication ingestion. In this study, 26 individuals were approached; 16 enrolled with 15 completing the study. Participants ingested a median of 6 (3-9.5) oxycodone digital pills over the course of 7 days, with 82% of the oxycodone dose ingested in the first 3 days. In individuals who required operative repair, 86% (N = 6) continued to ingest

PERFORMANCE EVALUATION OF VARIANCES IN BACKPROPAGATION NEURAL NETWORK USED FOR HANDWRITTEN CHARACTER RECOGNITION

OpenAIRE

Vairaprakash Gurusamy *1 & K.Nandhini2

2017-01-01

A Neural Network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain.Back propagation was created by generalizing the Widrow-Hoff learning rule to multiple-layer networks and nonlinear differentiable transfer functions. The term back pro...

The Improvement of Behavior Recognition Accuracy of Micro Inertial Accelerometer by Secondary Recognition Algorithm

Directory of Open Access Journals (Sweden)

Yu Liu

2014-05-01

Full Text Available Behaviors of “still”, “walking”, “running”, “jumping”, “upstairs” and “downstairs” can be recognized by micro inertial accelerometer of low cost. By using the features as inputs to the well-trained BP artificial neural network which is selected as classifier, those behaviors can be recognized. But the experimental results show that the recognition accuracy is not satisfactory. This paper presents secondary recognition algorithm and combine it with BP artificial neural network to improving the recognition accuracy. The Algorithm is verified by the Android mobile platform, and the recognition accuracy can be improved more than 8 %. Through extensive testing statistic analysis, the recognition accuracy can reach 95 % through BP artificial neural network and the secondary recognition, which is a reasonable good result from practical point of view.

RGBD Video Based Human Hand Trajectory Tracking and Gesture Recognition System

Directory of Open Access Journals (Sweden)

Weihua Liu

2015-01-01

Full Text Available The task of human hand trajectory tracking and gesture trajectory recognition based on synchronized color and depth video is considered. Toward this end, in the facet of hand tracking, a joint observation model with the hand cues of skin saliency, motion and depth is integrated into particle filter in order to move particles to local peak in the likelihood. The proposed hand tracking method, namely, salient skin, motion, and depth based particle filter (SSMD-PF, is capable of improving the tracking accuracy considerably, in the context of the signer performing the gesture toward the camera device and in front of moving, cluttered backgrounds. In the facet of gesture recognition, a shape-order context descriptor on the basis of shape context is introduced, which can describe the gesture in spatiotemporal domain. The efficient shape-order context descriptor can reveal the shape relationship and embed gesture sequence order information into descriptor. Moreover, the shape-order context leads to a robust score for gesture invariant. Our approach is complemented with experimental results on the settings of the challenging hand-signed digits datasets and American sign language dataset, which corroborate the performance of the novel techniques.

Neutron-gamma discrimination employing pattern recognition of the signal from liquid scintillator

CERN Document Server

Kamada, K; Ogawa, S

1999-01-01

A pattern recognition method was applied to the neutron-gamma discrimination of the pulses from the liquid scintillator, NE-213. The circuit for the discrimination is composed of A/D converter, fast SCA, memory control circuit, two digital delay lines and two buffer memories. All components are packed on a small circuit board and are installed into a personal computer. Experiments using a weak sup 2 sup 5 sup 2 Cf n-gamma source were undertaken to test the feasibility of the circuit. The circuit is of very easy adjustment and, at the same time, of very economical price when compared with usual discrimination circuits, such as the TAC system.

Neutron-gamma discrimination employing pattern recognition of the signal from liquid scintillator

International Nuclear Information System (INIS)

Kamada, Kohji; Enokido, Uhji; Ogawa, Seiji

1999-01-01

A pattern recognition method was applied to the neutron-gamma discrimination of the pulses from the liquid scintillator, NE-213. The circuit for the discrimination is composed of A/D converter, fast SCA, memory control circuit, two digital delay lines and two buffer memories. All components are packed on a small circuit board and are installed into a personal computer. Experiments using a weak 252 Cf n-γ source were undertaken to test the feasibility of the circuit. The circuit is of very easy adjustment and, at the same time, of very economical price when compared with usual discrimination circuits, such as the TAC system

Digital Natives or Digital Tribes?

Science.gov (United States)

Watson, Ian Robert

2013-01-01

This research builds upon the discourse surrounding digital natives. A literature review into the digital native phenomena was undertaken and found that researchers are beginning to identify the digital native as not one cohesive group but of individuals influenced by other factors. Primary research by means of questionnaire survey of technologies…

reCAPTCHA: human-based character recognition via Web security measures.

Science.gov (United States)

von Ahn, Luis; Maurer, Benjamin; McMillen, Colin; Abraham, David; Blum, Manuel

2008-09-12

CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are widespread security measures on the World Wide Web that prevent automated programs from abusing online services. They do so by asking humans to perform a task that computers cannot yet perform, such as deciphering distorted characters. Our research explored whether such human effort can be channeled into a useful purpose: helping to digitize old printed material by asking users to decipher scanned words from books that computerized optical character recognition failed to recognize. We showed that this method can transcribe text with a word accuracy exceeding 99%, matching the guarantee of professional human transcribers. Our apparatus is deployed in more than 40,000 Web sites and has transcribed over 440 million words.

Kinect-based sign language recognition of static and dynamic hand movements

Science.gov (United States)

Dalawis, Rando C.; Olayao, Kenneth Deniel R.; Ramos, Evan Geoffrey I.; Samonte, Mary Jane C.

2017-02-01

A different approach of sign language recognition of static and dynamic hand movements was developed in this study using normalized correlation algorithm. The goal of this research was to translate fingerspelling sign language into text using MATLAB and Microsoft Kinect. Digital input image captured by Kinect devices are matched from template samples stored in a database. This Human Computer Interaction (HCI) prototype was developed to help people with communication disability to express their thoughts with ease. Frame segmentation and feature extraction was used to give meaning to the captured images. Sequential and random testing was used to test both static and dynamic fingerspelling gestures. The researchers explained some factors they encountered causing some misclassification of signs.

Identifikasi Huruf Kapital Tulisan Tangan Menggunakan Linear Discriminant Analysis dan Euclidean Distance

Directory of Open Access Journals (Sweden)

Septa Cahyani

2018-04-01

Full Text Available The human ability to recognize a variety of objects, however complex the object, is the special ability that humans possess. Any normal human will have no difficulty in recognizing handwriting objects between an author and another author. With the rapid development of digital technology, the human ability to recognize handwriting objects has been applied in a program known as Computer Vision. This study aims to create identification system different types of handwriting capital letters that have different sizes, thickness, shape, and tilt (distinctive features in handwriting using Linear Discriminant Analysis (LDA and Euclidean Distance methods. LDA is used to obtain characteristic characteristics of the image and provide the distance between the classes becomes larger, while the distance between training data in one class becomes smaller, so that the introduction time of digital image of handwritten capital letter using Euclidean Distance becomes faster computation time (by searching closest distance between training data and data testing. The results of testing the sample data showed that the image resolution of 50x50 pixels is the exact image resolution used for data as much as 1560 handwritten capital letter data compared to image resolution 25x25 pixels and 40x40 pixels. While the test data and training data testing using the method of 10-fold cross validation where 1404 for training data and 156 for data testing showed identification of digital image handwriting capital letter has an average effectiveness of the accuracy rate of 75.39% with the average time computing of 0.4199 seconds.

Digital platforms as enablers for digital transformation

DEFF Research Database (Denmark)

Hossain, Mokter; Lassen, Astrid Heidemann

transformation is crucial. This study aims at exploring how organizations are driven towards transformation in various ways to embrace digital platforms for ideas, technologies, and knowledge. It shows the opportunities and challenges digital platforms bring in organizations. It also highlights underlying......Digital platforms offer new ways for organizations to collaborate with the external environment for ideas, technologies, and knowledge. They provide new possibilities and competence but they also bring new challenges for organizations. Understanding the role of these platforms in digital...... mechanisms and potential outcomes of various digital platforms. The contribution of the submission is valuable for scholars to understand and further explore this area. It provides insight for practitioners to capture value through digital platforms and accelerate the pace of organizations’ digital...

The Legal Recognition of Sign Languages

Science.gov (United States)

De Meulder, Maartje

2015-01-01

This article provides an analytical overview of the different types of explicit legal recognition of sign languages. Five categories are distinguished: constitutional recognition, recognition by means of general language legislation, recognition by means of a sign language law or act, recognition by means of a sign language law or act including…

Basic Test Framework for the Evaluation of Text Line Segmentation and Text Parameter Extraction

Directory of Open Access Journals (Sweden)

Darko Brodić

2010-05-01

Full Text Available Text line segmentation is an essential stage in off-line optical character recognition (OCR systems. It is a key because inaccurately segmented text lines will lead to OCR failure. Text line segmentation of handwritten documents is a complex and diverse problem, complicated by the nature of handwriting. Hence, text line segmentation is a leading challenge in handwritten document image processing. Due to inconsistencies in measurement and evaluation of text segmentation algorithm quality, some basic set of measurement methods is required. Currently, there is no commonly accepted one and all algorithm evaluation is custom oriented. In this paper, a basic test framework for the evaluation of text feature extraction algorithms is proposed. This test framework consists of a few experiments primarily linked to text line segmentation, skew rate and reference text line evaluation. Although they are mutually independent, the results obtained are strongly cross linked. In the end, its suitability for different types of letters and languages as well as its adaptability are its main advantages. Thus, the paper presents an efficient evaluation method for text analysis algorithms.

Digital citizens Digital nations: the next agenda

NARCIS (Netherlands)

A.W. (Bert) Mulder; M.W. (Martijn) Hartog

2015-01-01

DIGITAL CITIZENS CREATE A DIGITAL NATION Citizens will play the lead role as they – in the next phase of the information society – collectively create a digital nation. Personal adoption of information and communication technology will create a digital infrastructure that supports individual and

Human activity recognition and prediction

CERN Document Server

2016-01-01

This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques. .

Playing tag with ANN: boosted top identification with pattern recognition

International Nuclear Information System (INIS)

Almeida, Leandro G.; Backović, Mihailo; Cliche, Mathieu; Lee, Seung J.; Perelstein, Maxim

2015-01-01

Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a “digital image" of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p T in the 1100–1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

Playing tag with ANN: boosted top identification with pattern recognition

Energy Technology Data Exchange (ETDEWEB)

Almeida, Leandro G. [Institut de Biologie de l’École Normale Supérieure (IBENS), Inserm 1024- CNRS 8197,46 rue d’Ulm, 75005 Paris (France); Backović, Mihailo [Center for Cosmology, Particle Physics and Phenomenology - CP3,Universite Catholique de Louvain,Louvain-la-neuve (Belgium); Cliche, Mathieu [Laboratory for Elementary Particle Physics, Cornell University,Ithaca, NY 14853 (United States); Lee, Seung J. [Department of Physics, Korea Advanced Institute of Science and Technology,335 Gwahak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of); School of Physics, Korea Institute for Advanced Study,Seoul 130-722 (Korea, Republic of); Perelstein, Maxim [Laboratory for Elementary Particle Physics, Cornell University,Ithaca, NY 14853 (United States)

2015-07-17

Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a “digital image' of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p{sub T} in the 1100–1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

Radar automatic target recognition (ATR) and non-cooperative target recognition (NCTR)

CERN Document Server

Blacknell, David

2013-01-01

The ability to detect and locate targets by day or night, over wide areas, regardless of weather conditions has long made radar a key sensor in many military and civil applications. However, the ability to automatically and reliably distinguish different targets represents a difficult challenge. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR) captures material presented in the NATO SET-172 lecture series to provide an overview of the state-of-the-art and continuing challenges of radar target recognition. Topics covered include the problem as applied to th

Recognition and Toleration

DEFF Research Database (Denmark)

Lægaard, Sune

2010-01-01

Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms or inter...

Innate pattern recognition and categorization in a jumping spider.

Directory of Open Access Journals (Sweden)

Yinnon Dolev

Full Text Available The East African jumping spider Evarcha culicivora feeds indirectly on vertebrate blood by preferentially preying upon blood-fed Anopheles mosquitoes, the vectors of human malaria1, using the distinct resting posture and engorged abdomen characteristic of these specific prey as key elements for their recognition. To understand perceptual categorization of objects by these spiders, we investigated their predatory behavior toward different digital stimuli--abstract 'stick figure' representations of Anopheles constructed solely by known key identification elements, disarranged versions of these, as well as non-prey items and detailed images of alternative prey. We hypothesized that the abstract images representing Anopheles would be perceived as potential prey, and would be preferred to those of non-preferred prey. Spiders perceived the abstract stick figures of Anopheles specifically as their preferred prey, attacking them significantly more often than non-preferred prey, even when the comprising elements of the Anopheles stick figures were disarranged and disconnected from each other. However, if the relative angles between the elements of the disconnected stick figures of Anopheles were altered, the otherwise identical set of elements was no longer perceived as prey. These data show that E. culicivora is capable of making discriminations based on abstract concepts, such as the hypothetical angle formed by discontinuous elements. It is this inter-element angle rather than resting posture that is important for correct identification of Anopheles. Our results provide a glimpse of the underlying processes of object recognition in animals with minute brains, and suggest that these spiders use a local processing approach for object recognition, rather than a holistic or global approach. This study provides an excellent basis for a comparative analysis on feature extraction and detection by animals as diverse as bees and mammals.

Innate Pattern Recognition and Categorization in a Jumping Spider

Science.gov (United States)

Dolev, Yinnon; Nelson, Ximena J.

2014-01-01

The East African jumping spider Evarcha culicivora feeds indirectly on vertebrate blood by preferentially preying upon blood-fed Anopheles mosquitoes, the vectors of human malaria1, using the distinct resting posture and engorged abdomen characteristic of these specific prey as key elements for their recognition. To understand perceptual categorization of objects by these spiders, we investigated their predatory behavior toward different digital stimuli - abstract ‘stick figure’ representations of Anopheles constructed solely by known key identification elements, disarranged versions of these, as well as non-prey items and detailed images of alternative prey. We hypothesized that the abstract images representing Anopheles would be perceived as potential prey, and would be preferred to those of non-preferred prey. Spiders perceived the abstract stick figures of Anopheles specifically as their preferred prey, attacking them significantly more often than non-preferred prey, even when the comprising elements of the Anopheles stick figures were disarranged and disconnected from each other. However, if the relative angles between the elements of the disconnected stick figures of Anopheles were altered, the otherwise identical set of elements was no longer perceived as prey. These data show that E. culicivora is capable of making discriminations based on abstract concepts, such as the hypothetical angle formed by discontinuous elements. It is this inter-element angle rather than resting posture that is important for correct identification of Anopheles. Our results provide a glimpse of the underlying processes of object recognition in animals with minute brains, and suggest that these spiders use a local processing approach for object recognition, rather than a holistic or global approach. This study provides an excellent basis for a comparative analysis on feature extraction and detection by animals as diverse as bees and mammals. PMID:24893306

Super-recognition in development: A case study of an adolescent with extraordinary face recognition skills.

Science.gov (United States)

Bennetts, Rachel J; Mole, Joseph; Bate, Sarah

2017-09-01

Face recognition abilities vary widely. While face recognition deficits have been reported in children, it is unclear whether superior face recognition skills can be encountered during development. This paper presents O.B., a 14-year-old female with extraordinary face recognition skills: a "super-recognizer" (SR). O.B. demonstrated exceptional face-processing skills across multiple tasks, with a level of performance that is comparable to adult SRs. Her superior abilities appear to be specific to face identity: She showed an exaggerated face inversion effect and her superior abilities did not extend to object processing or non-identity aspects of face recognition. Finally, an eye-movement task demonstrated that O.B. spent more time than controls examining the nose - a pattern previously reported in adult SRs. O.B. is therefore particularly skilled at extracting and using identity-specific facial cues, indicating that face and object recognition are dissociable during development, and that super recognition can be detected in adolescence.

A motivational determinant of facial emotion recognition: regulatory focus affects recognition of emotions in faces.

Science.gov (United States)

Sassenrath, Claudia; Sassenberg, Kai; Ray, Devin G; Scheiter, Katharina; Jarodzka, Halszka

2014-01-01

Two studies examined an unexplored motivational determinant of facial emotion recognition: observer regulatory focus. It was predicted that a promotion focus would enhance facial emotion recognition relative to a prevention focus because the attentional strategies associated with promotion focus enhance performance on well-learned or innate tasks - such as facial emotion recognition. In Study 1, a promotion or a prevention focus was experimentally induced and better facial emotion recognition was observed in a promotion focus compared to a prevention focus. In Study 2, individual differences in chronic regulatory focus were assessed and attention allocation was measured using eye tracking during the facial emotion recognition task. Results indicated that the positive relation between a promotion focus and facial emotion recognition is mediated by shorter fixation duration on the face which reflects a pattern of attention allocation matched to the eager strategy in a promotion focus (i.e., striving to make hits). A prevention focus did not have an impact neither on perceptual processing nor on facial emotion recognition. Taken together, these findings demonstrate important mechanisms and consequences of observer motivational orientation for facial emotion recognition.

Pattern recognition & machine learning

CERN Document Server

Anzai, Y

1992-01-01

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

Examples of challenges and opportunities in visual analysis in the digital humanities

Science.gov (United States)

Rushmeier, Holly; Pintus, Ruggero; Yang, Ying; Wong, Christiana; Li, David

2015-03-01

The massive digitization of books and manuscripts has converted millions of works that were once only physical into electronic documents. This conversion has made it possible for scholars to study large bodies of work, rather than just individual texts. This has offered new opportunities for scholarship in the humanities. Much previous work on digital collections has relied on optical character recognition and focused on the textual content of books. New work is emerging that is analyzing the visual layout and content of books and manuscripts. We present two different digital humanities projects in progress that present new opportunities for extracting data about the past, with new challenges for designing systems for scholars to interact with this data. The first project we consider is the layout and spectral content of thousands of pages from medieval manuscripts. We present the techniques used to study content variations in sets of similar manuscripts, and to study material variations that may indicate the location of manuscript production. The second project is the analysis of representations in the complete archive of Vogue magazine over 120 years. We present samples of applying computer vision techniques to understanding the changes in representation of women over time.

A Global Online Handwriting Recognition Approach Based on Frequent Patterns

Directory of Open Access Journals (Sweden)

C. Gmati

2018-06-01

Full Text Available In this article, the handwriting signals are represented based on geometric and spatio-temporal characteristics to increase the feature vectors relevance of each object. The main goal was to extract features in the form of a numeric vector based on the extraction of frequent patterns. We used two types of frequent motifs (closed frequent patterns and maximal frequent patterns that can represent handwritten characters pertinently. These common features patterns are generated from a raw data transformation method to achieve high relevance. A database of words consisting of two different letters was created. The proposed application gives promising results and highlights the advantages that frequent pattern extraction algorithms can achieve, as well as the central role played by the “minimum threshold” parameter in the overall description of the characters.

Social communication and emotion difficulties and second to fourth digit ratio in a large community-based sample.

Science.gov (United States)

Barona, Manuela; Kothari, Radha; Skuse, David; Micali, Nadia

2015-01-01

Recent research investigating the extreme male brain theory of autism spectrum disorders (ASD) has drawn attention to the possibility that autistic type social difficulties may be associated with high prenatal testosterone exposure. This study aims to investigate the association between social communication and emotion recognition difficulties and second to fourth digit ratio (2D:4D) and circulating maternal testosterone during pregnancy in a large community-based cohort: the Avon Longitudinal Study of Parents and Children (ALSPAC). A secondary aim is to investigate possible gender differences in the associations. Data on social communication (Social and Communication Disorders Checklist, N = 7165), emotion recognition (emotional triangles, N = 5844 and diagnostics analysis of non-verbal accuracy, N = 7488) and 2D:4D (second to fourth digit ratio, N = 7159) were collected in childhood and early adolescence from questionnaires and face-to-face assessments. Complete data was available on 3515 children. Maternal circulating testosterone during pregnancy was available in a subsample of 89 children. Males had lower 2D:4D ratios than females [t (3513) = -9.775, p emotion recognition, and the lowest 10 % of 2D:4D ratios. A significant association was found between maternal circulating testosterone and left hand 2D:4D [OR = 1.65, 95 % CI 1.1-2.4, p < 0.01]. Previous findings on the association between 2D:4D and social communication difficulties were not confirmed. A novel association between an extreme measure of 2D:4D in males suggests threshold effects and warrants replication.

Challenging ocular image recognition

Science.gov (United States)

Pauca, V. Paúl; Forkin, Michael; Xu, Xiao; Plemmons, Robert; Ross, Arun A.

2011-06-01

Ocular recognition is a new area of biometric investigation targeted at overcoming the limitations of iris recognition performance in the presence of non-ideal data. There are several advantages for increasing the area beyond the iris, yet there are also key issues that must be addressed such as size of the ocular region, factors affecting performance, and appropriate corpora to study these factors in isolation. In this paper, we explore and identify some of these issues with the goal of better defining parameters for ocular recognition. An empirical study is performed where iris recognition methods are contrasted with texture and point operators on existing iris and face datasets. The experimental results show a dramatic recognition performance gain when additional features are considered in the presence of poor quality iris data, offering strong evidence for extending interest beyond the iris. The experiments also highlight the need for the direct collection of additional ocular imagery.

Recall and Recognition of In-Game Advertising: The Impact of Game Control

Directory of Open Access Journals (Sweden)

Laura eHerrewijn

2014-01-01

Full Text Available Digital gaming has become one of the largest entertainment sectors worldwide, increasingly turning the medium into a promising vehicle for advertisers. As a result, the inclusion of advertising messages in digital games or in-game advertising (IGA is expected to grow steadily over the course of the following years. However, much work is still needed to maximize the effectiveness of IGA. The aim of the study was to contribute to IGA effectiveness research by analyzing the impact of two factors on the processing of IGA in terms of brand awareness. The primary objective was to investigate the effect of a person’s sense of involvement related to the control and movement mechanisms in a game (i.e. kinesthetic involvement. A within-subjects experiment was conducted in which control over a racing game was varied by manipulating game controller type, resulting in two experimental conditions (symbolic versus mimetic controller. Results show that the variation in game controller has a significant effect on the recall and recognition of the brands integrated into the game, and that this effect can be partially brought back to players’ perceived control over the game: when a game is easier to control, the control mechanisms require less conscious attention, freeing attentional resources that can be subsequently spent on other elements of the game such as IGA. A second factor that was taken into account in the study was brand prominence. The influence of both the size and spatial position of in-game advertisements was examined. Findings demonstrate that there are significant changes in effectiveness between different types of placements. Spatial position seems to be the most important placement characteristic, with central brand placements obtaining the highest recall and recognition scores. The effect of ad size is much smaller, with the effectiveness of the large placements not differing significantly from the effectiveness of their smaller

Recall and recognition of in-game advertising: the role of game control.

Science.gov (United States)

Herrewijn, Laura; Poels, Karolien

2013-01-01

Digital gaming has become one of the largest entertainment sectors worldwide, increasingly turning the medium into a promising vehicle for advertisers. As a result, the inclusion of advertising messages in digital games or in-game advertising (IGA) is expected to grow steadily over the course of the following years. However, much work is still needed to maximize the effectiveness of IGA. The aim of the study was to contribute to IGA effectiveness research by analyzing the impact of two factors on the processing of IGA in terms of brand awareness. The primary objective was to investigate the effect of a person's sense of involvement related to the control and movement mechanisms in a game (i.e., kinesthetic involvement). A within-subjects experiment was conducted in which control over a racing game was varied by manipulating game controller type, resulting in two experimental conditions (symbolic versus mimetic controller). Results show that the variation in game controller has a significant effect on the recall and recognition of the brands integrated into the game, and that this effect can be partially brought back to players' perceived control over the game: when a game is easier to control, the control mechanisms require less conscious attention, freeing attentional resources that can be subsequently spent on other elements of the game such as IGA. A second factor that was taken into account in the study was brand prominence. The influence of both the size and spatial position of in-game advertisements was examined. Findings demonstrate that there are significant changes in effectiveness between different types of placements. Spatial position seems to be the most important placement characteristic, with central brand placements obtaining the highest recall and recognition scores. The effect of ad size is much smaller, with the effectiveness of the large placements not differing significantly from the effectiveness of their smaller counterparts.

Digital forensics digital evidence in criminal investigations

CERN Document Server

Marshall, Angus McKenzie

2009-01-01

The vast majority of modern criminal investigations involve some element of digital evidence, from mobile phones, computers, CCTV and other devices. Digital Forensics: Digital Evidence in Criminal Investigations provides the reader with a better understanding of how digital evidence complements "traditional" scientific evidence and examines how it can be used more effectively and efficiently in a range of investigations. Taking a new approach to the topic, this book presents digital evidence as an adjunct to other types of evidence and discusses how it can be deployed effectively in s

Digital radiology and digitally formatted image management systems

International Nuclear Information System (INIS)

Cox, G.G.; Dwyer, S.J. III; Templeton, A.W.

1987-01-01

The number of diagnostic examinations performed with digitally formatted imaging equipment is increasing. Digital general-purpose and fluoroscopic radiology systems are being clinically evaluated. Digitizing conventional x-ray films, such as mammograms, frequently improves the diagnostic quality of the images. The digitizing process with laser has also afforded the opportunity to document required spatial resolution for digital imaging and network systems. The use of digitally formatted image instrumentation imposes new requirements on the acquisition, display and manipulation, transmission, hard copy image recording, and archiving of diagnostic data. Networking of digitally formatted image data offers many advantages for managing digital information. This paper identifies and describes digital radiographic systems. Parameters required for designing and implementing a digital image management system are outlined. Spatial and contrast resolution requirements are identified. The key parameters include the amount of image data generated each working day, the retrieval rate of the generated data, the display hardware and software needed for interactive diagnosis display stations, the requirements for analog hard copy generation, and on-line and long-term archiving requirements. These image management systems are often called PACS (Picture Archiving and Communication Systems)

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

Directory of Open Access Journals (Sweden)

V. K. Hohlov

2017-01-01

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

Genetic specificity of face recognition.

Science.gov (United States)

Shakeshaft, Nicholas G; Plomin, Robert

2015-10-13

Specific cognitive abilities in diverse domains are typically found to be highly heritable and substantially correlated with general cognitive ability (g), both phenotypically and genetically. Recent twin studies have found the ability to memorize and recognize faces to be an exception, being similarly heritable but phenotypically substantially uncorrelated both with g and with general object recognition. However, the genetic relationships between face recognition and other abilities (the extent to which they share a common genetic etiology) cannot be determined from phenotypic associations. In this, to our knowledge, first study of the genetic associations between face recognition and other domains, 2,000 18- and 19-year-old United Kingdom twins completed tests assessing their face recognition, object recognition, and general cognitive abilities. Results confirmed the substantial heritability of face recognition (61%), and multivariate genetic analyses found that most of this genetic influence is unique and not shared with other cognitive abilities.

Face Detection and Recognition

National Research Council Canada - National Science Library

Jain, Anil K

2004-01-01

This report describes research efforts towards developing algorithms for a robust face recognition system to overcome many of the limitations found in existing two-dimensional facial recognition systems...

Formal Verification of Digital Protection Logic and Automatic Testing Software

Energy Technology Data Exchange (ETDEWEB)

Cha, S. D.; Ha, J. S.; Seo, J. S. [KAIST, Daejeon (Korea, Republic of)

2008-06-15

- Technical aspect {center_dot} It is intended that digital I and C software have safety and reliability. Project results help the software to acquire license. Software verification technique, which results in this project, can be to use for digital NPP(Nuclear power plant) in the future. {center_dot} This research introduces many meaningful results of verification on digital protection logic and suggests I and C software testing strategy. These results apply to verify nuclear fusion device, accelerator, nuclear waste management and nuclear medical device that require dependable software and high-reliable controller. Moreover, These can be used for military, medical or aerospace-related software. - Economical and industrial aspect {center_dot} Since safety of digital I and C software is highly import, It is essential for the software to be verified. But verification and licence acquisition related to digital I and C software face high cost. This project gives economic profit to domestic economy by using introduced verification and testing technique instead of foreign technique. {center_dot} The operation rate of NPP will rise, when NPP safety critical software is verified with intellectual V and V tool. It is expected that these software substitute safety-critical software that wholly depend on foreign. Consequently, the result of this project has high commercial value and the recognition of the software development works will be able to be spread to the industrial circles. - Social and cultural aspect People expect that nuclear power generation contributes to relieving environmental problems because that does not emit more harmful air pollution source than other power generations. To give more trust and expectation about nuclear power generation to our society, we should make people to believe that NPP is highly safe system. In that point of view, we can present high-reliable I and C proofed by intellectual V and V technique as evidence

Forensic Face Recognition: A Survey

NARCIS (Netherlands)

Ali, Tauseef; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.; Quaglia, Adamo; Epifano, Calogera M.

2012-01-01

The improvements of automatic face recognition during the last 2 decades have disclosed new applications like border control and camera surveillance. A new application field is forensic face recognition. Traditionally, face recognition by human experts has been used in forensics, but now there is a

Voice Recognition in Face-Blind Patients

Science.gov (United States)

Liu, Ran R.; Pancaroglu, Raika; Hills, Charlotte S.; Duchaine, Brad; Barton, Jason J. S.

2016-01-01

Right or bilateral anterior temporal damage can impair face recognition, but whether this is an associative variant of prosopagnosia or part of a multimodal disorder of person recognition is an unsettled question, with implications for cognitive and neuroanatomic models of person recognition. We assessed voice perception and short-term recognition of recently heard voices in 10 subjects with impaired face recognition acquired after cerebral lesions. All 4 subjects with apperceptive prosopagnosia due to lesions limited to fusiform cortex had intact voice discrimination and recognition. One subject with bilateral fusiform and anterior temporal lesions had a combined apperceptive prosopagnosia and apperceptive phonagnosia, the first such described case. Deficits indicating a multimodal syndrome of person recognition were found only in 2 subjects with bilateral anterior temporal lesions. All 3 subjects with right anterior temporal lesions had normal voice perception and recognition, 2 of whom performed normally on perceptual discrimination of faces. This confirms that such lesions can cause a modality-specific associative prosopagnosia. PMID:25349193

A portable digital speech-rate converter for hearing impairment.

Science.gov (United States)

Nejime, Y; Aritsuka, T; Imamura, T; Ifukube, T; Matsushima, J

1996-06-01

A real-time hand-sized portable device that slows speech speed without changing the pitch is proposed for hearing impairment. By using this device, people can listen to fast speech at a comfortable speed. A combination of solid-state memory recording and real-time digital signal processing with a single chip processor enables this unique function. A simplified pitchsynchronous, time-scale-modification algorithm is proposed to minimize the complexity of the DSP operation. Unlike the traditional algorithm, this dynamic-processing algorithm reduces distortion even when the expansion rate is only just above 1. Seven out of 10 elderly hearing-impaired listeners showed improvement in a sentence recognition test when using speech-rate conversion with the largest expansion rate, although no improvement was observed in a word recognition test. Some subjects who showed large improvement had limited auditory temporal resolution, but the correlation was not significant. The results suggest that, unlike conventional hearing aids, this device can be used to overcome the deterioration of auditory ability by improving the transfer of information from short-term (echoic) memory into a more stable memory trace in the human auditory system.

Invariant Face recognition Using Infrared Images

International Nuclear Information System (INIS)

Zahran, E.G.

2012-01-01

Over the past few decades, face recognition has become a rapidly growing research topic due to the increasing demands in many applications of our daily life such as airport surveillance, personal identification in law enforcement, surveillance systems, information safety, securing financial transactions, and computer security. The objective of this thesis is to develop a face recognition system capable of recognizing persons with a high recognition capability, low processing time, and under different illumination conditions, and different facial expressions. The thesis presents a study for the performance of the face recognition system using two techniques; the Principal Component Analysis (PCA), and the Zernike Moments (ZM). The performance of the recognition system is evaluated according to several aspects including the recognition rate, and the processing time. Face recognition systems that use visual images are sensitive to variations in the lighting conditions and facial expressions. The performance of these systems may be degraded under poor illumination conditions or for subjects of various skin colors. Several solutions have been proposed to overcome these limitations. One of these solutions is to work in the Infrared (IR) spectrum. IR images have been suggested as an alternative source of information for detection and recognition of faces, when there is little or no control over lighting conditions. This arises from the fact that these images are formed due to thermal emissions from skin, which is an intrinsic property because these emissions depend on the distribution of blood vessels under the skin. On the other hand IR face recognition systems still have limitations with temperature variations and recognition of persons wearing eye glasses. In this thesis we will fuse IR images with visible images to enhance the performance of face recognition systems. Images are fused using the wavelet transform. Simulation results show that the fusion of visible and

[Application of image recognition technology in census of national traditional Chinese medicine resources].

Science.gov (United States)

Zhang, Xiao-Bo; Ge, Xiao-Guang; Jin, Yan; Shi, Ting-Ting; Wang, Hui; Li, Meng; Jing, Zhi-Xian; Guo, Lan-Ping; Huang, Lu-Qi

2017-11-01

With the development of computer and image processing technology, image recognition technology has been applied to the national medicine resources census work at all stages.Among them: ①In the preparatory work, in order to establish a unified library of traditional Chinese medicine resources, using text recognition technology based on paper materials, be the assistant in the digitalization of various categories related to Chinese medicine resources; to determine the representative area and plots of the survey from each census team, based on the satellite remote sensing image and vegetation map and other basic data, using remote sensing image classification and other technical methods to assist in determining the key investigation area. ②In the process of field investigation, to obtain the planting area of Chinese herbal medicine was accurately, we use the decision tree model, spectral feature and object-oriented method were used to assist the regional identification and area estimation of Chinese medicinal materials.③In the process of finishing in the industry, in order to be able to relatively accurately determine the type of Chinese medicine resources in the region, based on the individual photos of the plant, the specimens and the name of the use of image recognition techniques, to assist the statistical summary of the types of traditional Chinese medicine resources. ④In the application of the results of transformation, based on the pharmaceutical resources and individual samples of medicinal herbs, the development of Chinese medicine resources to identify APP and authentic herbs 3D display system, assisted the identification of Chinese medicine resources and herbs identification characteristics. The introduction of image recognition technology in the census of Chinese medicine resources, assisting census personnel to carry out related work, not only can reduce the workload of the artificial, improve work efficiency, but also improve the census results

End-Stop Exemplar Based Recognition

DEFF Research Database (Denmark)

Olsen, Søren I.

2003-01-01

An approach to exemplar based recognition of visual shapes is presented. The shape information is described by attributed interest points (keys) detected by an end-stop operator. The attributes describe the statistics of lines and edges local to the interest point, the position of neighboring int...... interest points, and (in the training phase) a list of recognition names. Recognition is made by a simple voting procedure. Preliminary experiments indicate that the recognition is robust to noise, small deformations, background clutter and partial occlusion....

Markov Models for Handwriting Recognition

CERN Document Server

Plotz, Thomas

2011-01-01

Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both hidden

Examining ERP correlates of recognition memory: Evidence of accurate source recognition without recollection

Science.gov (United States)

Addante, Richard, J.; Ranganath, Charan; Yonelinas, Andrew, P.

2012-01-01

Recollection is typically associated with high recognition confidence and accurate source memory. However, subjects sometimes make accurate source memory judgments even for items that are not confidently recognized, and it is not known whether these responses are based on recollection or some other memory process. In the current study, we measured event related potentials (ERPs) while subjects made item and source memory confidence judgments in order to determine whether recollection supported accurate source recognition responses for items that were not confidently recognized. In line with previous studies, we found that recognition memory was associated with two ERP effects: an early on-setting FN400 effect, and a later parietal old-new effect [Late Positive Component (LPC)], which have been associated with familiarity and recollection, respectively. The FN400 increased gradually with item recognition confidence, whereas the LPC was only observed for highly confident recognition responses. The LPC was also related to source accuracy, but only for items that had received a high confidence item recognition response; accurate source judgments to items that were less confidently recognized did not exhibit the typical ERP correlate of recollection or familiarity, but rather showed a late, broadly distributed negative ERP difference. The results indicate that accurate source judgments of episodic context can occur even when recollection fails. PMID:22548808

Evaluating music emotion recognition:Lessons from music genre recognition?

OpenAIRE

Sturm, Bob L.

2013-01-01

A fundamental problem with nearly all work in music genre recognition (MGR)is that evaluation lacks validity with respect to the principal goals of MGR. This problem also occurs in the evaluation of music emotion recognition (MER). Standard approaches to evaluation, though easy to implement, do not reliably differentiate between recognizing genre or emotion from music, or by virtue of confounding factors in signals (e.g., equalization). We demonstrate such problems for evaluating an MER syste...

Word Recognition in Auditory Cortex

Science.gov (United States)

DeWitt, Iain D. J.

2013-01-01

Although spoken word recognition is more fundamental to human communication than text recognition, knowledge of word-processing in auditory cortex is comparatively impoverished. This dissertation synthesizes current models of auditory cortex, models of cortical pattern recognition, models of single-word reading, results in phonetics and results in…

Digital citizenship and neoliberalization: governing digital citizens in Denmark

DEFF Research Database (Denmark)

Schou, Jannick; Hjelholt, Morten

2018-01-01

Digital citizenship is becoming increasingly normalized within advanced democratic states. As society and governmental institutions become reliant on digital technologies, citizens are expected to be and act digitally. This article examines the governance of digital citizens through a case study...... this case study, the article contributes to current critical perspectives on the digital citizen as a new political figure. It adds new insights into digital citizenship by connecting this figure to wider processes of neoliberalization and state restructuring, pushing for a more pronounced focus...... of digitalization efforts in Denmark. Drawing on multiple forms of data, the article showcases how digital citizens are governed through a combination of discursive, legal and institutional means. The article highlights the political, but also institutional work that goes into making citizens digital. Providing...

[Comparative studies of face recognition].

Science.gov (United States)

Kawai, Nobuyuki

2012-07-01

Every human being is proficient in face recognition. However, the reason for and the manner in which humans have attained such an ability remain unknown. These questions can be best answered-through comparative studies of face recognition in non-human animals. Studies in both primates and non-primates show that not only primates, but also non-primates possess the ability to extract information from their conspecifics and from human experimenters. Neural specialization for face recognition is shared with mammals in distant taxa, suggesting that face recognition evolved earlier than the emergence of mammals. A recent study indicated that a social insect, the golden paper wasp, can distinguish their conspecific faces, whereas a closely related species, which has a less complex social lifestyle with just one queen ruling a nest of underlings, did not show strong face recognition for their conspecifics. Social complexity and the need to differentiate between one another likely led humans to evolve their face recognition abilities.

Automated Meteor Detection by All-Sky Digital Camera Systems

Science.gov (United States)

Suk, Tomáš; Šimberová, Stanislava

2017-12-01

We have developed a set of methods to detect meteor light traces captured by all-sky CCD cameras. Operating at small automatic observatories (stations), these cameras create a network spread over a large territory. Image data coming from these stations are merged in one central node. Since a vast amount of data is collected by the stations in a single night, robotic storage and analysis are essential to processing. The proposed methodology is adapted to data from a network of automatic stations equipped with digital fish-eye cameras and includes data capturing, preparation, pre-processing, analysis, and finally recognition of objects in time sequences. In our experiments we utilized real observed data from two stations.

A novel word spotting method based on recurrent neural networks.

Science.gov (United States)

Frinken, Volkmar; Fischer, Andreas; Manmatha, R; Bunke, Horst

2012-02-01

Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperform not only a classical dynamic time warping-based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition.

Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.

Science.gov (United States)

Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

2017-12-01

The hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants' patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Digital radiography

International Nuclear Information System (INIS)

Brody, W.R.

1984-01-01

Digital Radiography begins with an orderly introduction to the fundamental concepts of digital imaging. The entire X-ray digital imagining system is described, from an overall characterization of image quality to specific components required for a digital radiographic system. Because subtraction is central to digital radiographic systems, the author details the use of various subtraction methods for image enhancement. Complex concepts are illustrated with numerous examples and presented in terms that can readily be understood by physicians without an advanced mathematics background. The second part of the book discusses implementations and applications of digital imagining systems based on area and scanned detector technologies. This section includes thorough coverage of digital fluoroscopy, scanned projection radiography, and film-based digital imaging systems, and features a state-of-the-art synopsis of the applications of digital subtraction angiography. The book concludes with a timely assessment of anticipated technological advances

A guide for digitising manuscript climate data

Directory of Open Access Journals (Sweden)

S. Brönnimann

2006-01-01

Full Text Available Hand-written or printed manuscript data are an important source for paleo-climatological studies, but bringing them into a suitable format can be a time consuming adventure with uncertain success. Before digitising such data (e.g., in the context a specific research project, it is worthwhile spending a few thoughts on the characteristics of the data, the scientific requirements with respect to quality and coverage, the metadata, and technical aspects such as reproduction techniques, digitising techniques, and quality control strategies. Here we briefly discuss the most important considerations according to our own experience and describe different methods for digitising numeric or text data (optical character recognition, speech recognition, and key entry. We present a tentative guide that is intended to help others compiling the necessary information and making the right decisions.

The use of the operand-recognition paradigm for the study of mental addition in older adults.

Science.gov (United States)

Thevenot, Catherine; Castel, Caroline; Danjon, Juliette; Fanget, Muriel; Fayol, Michel

2013-01-01

Determining how individuals solve arithmetic problems is crucial for our understanding of human cognitive architecture. Elderly adults are supposed to use memory retrieval more often than younger ones. However, they might backup their retrieval by reconstructive strategies. In order to investigate this issue, we used the operand-recognition paradigm, which capitalizes on the fact that algorithmic procedures degrade the memory traces of the operands. Twenty-three older adults (M = 70.4) and 23 younger adults (M = 20.0) solved easy, difficult, and medium-difficulty addition and comparison problems and were then presented with a recognition task of the operands. When one-digit numbers with sums larger than 10 were involved (medium-difficulty problem), it was more difficult for younger adults to recognize the operands after addition than comparison. In contrast, in older adults, recognition times of the operands were the same after addition and comparison. Older adults, in contrast with younger adults, are able to retrieve the results of addition problems of medium difficulty. Contrary to what was suggested, older participants do not seem to resort to backup strategies for such problems. Finally, older adults' reliance on the more efficient retrieval strategy allowed them to catch up to younger adults in terms of solution times.

Visual Recognition Memory across Contexts

Science.gov (United States)

Jones, Emily J. H.; Pascalis, Olivier; Eacott, Madeline J.; Herbert, Jane S.

2011-01-01

In two experiments, we investigated the development of representational flexibility in visual recognition memory during infancy using the Visual Paired Comparison (VPC) task. In Experiment 1, 6- and 9-month-old infants exhibited recognition when familiarization and test occurred in the same room, but showed no evidence of recognition when…

Deep neural networks for texture classification-A theoretical analysis.

Science.gov (United States)

Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

2018-01-01

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.