Sample records for handwritten digit recognition

  1. Handwritten digits recognition based on immune network

    NASA Astrophysics Data System (ADS)

    Li, Yangyang; Wu, Yunhui; Jiao, Lc; Wu, Jianshe

    2011-11-01

    With the development of society, handwritten digits recognition technique has been widely applied to production and daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN method for classification. Its characteristic is the novel network with parallel commutating and learning. The performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.

  2. An adaptive deep Q-learning strategy for handwritten digit recognition.

    PubMed

    Qiao, Junfei; Wang, Gongming; Li, Wenjing; Chen, Min

    2018-02-22

    Handwritten digits recognition is a challenging problem in recent years. Although many deep learning-based classification algorithms are studied for handwritten digits recognition, the recognition accuracy and running time still need to be further improved. In this paper, an adaptive deep Q-learning strategy is proposed to improve accuracy and shorten running time for handwritten digit recognition. The adaptive deep Q-learning strategy combines the feature-extracting capability of deep learning and the decision-making of reinforcement learning to form an adaptive Q-learning deep belief network (Q-ADBN). First, Q-ADBN extracts the features of original images using an adaptive deep auto-encoder (ADAE), and the extracted features are considered as the current states of Q-learning algorithm. Second, Q-ADBN receives Q-function (reward signal) during recognition of the current states, and the final handwritten digits recognition is implemented by maximizing the Q-function using Q-learning algorithm. Finally, experimental results from the well-known MNIST dataset show that the proposed Q-ADBN has a superiority to other similar methods in terms of accuracy and running time. Copyright © 2018 Elsevier Ltd. All rights reserved.

  3. Handwritten digits recognition using HMM and PSO based on storks

    NASA Astrophysics Data System (ADS)

    Yan, Liao; Jia, Zhenhong; Yang, Jie; Pang, Shaoning

    2010-07-01

    A new method for handwritten digits recognition based on hidden markov model (HMM) and particle swarm optimization (PSO) is proposed. This method defined 24 strokes with the sense of directional, to make up for the shortage that is sensitive in choice of stating point in traditional methods, but also reduce the ambiguity caused by shakes. Make use of excellent global convergence of PSO; improving the probability of finding the optimum and avoiding local infinitesimal obviously. Experimental results demonstrate that compared with the traditional methods, the proposed method can make most of the recognition rate of handwritten digits improved.

  4. Recognition of degraded handwritten digits using dynamic Bayesian networks

    NASA Astrophysics Data System (ADS)

    Likforman-Sulem, Laurence; Sigelle, Marc

    2007-01-01

    We investigate in this paper the application of dynamic Bayesian networks (DBNs) to the recognition of handwritten digits. The main idea is to couple two separate HMMs into various architectures. First, a vertical HMM and a horizontal HMM are built observing the evolving streams of image columns and image rows respectively. Then, two coupled architectures are proposed to model interactions between these two streams and to capture the 2D nature of character images. Experiments performed on the MNIST handwritten digit database show that coupled architectures yield better recognition performances than non-coupled ones. Additional experiments conducted on artificially degraded (broken) characters demonstrate that coupled architectures better cope with such degradation than non coupled ones and than discriminative methods such as SVMs.

  5. Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition

    NASA Astrophysics Data System (ADS)

    Popko, E. A.; Weinstein, I. A.

    2016-08-01

    Optical character recognition is one of the important issues in the field of pattern recognition. This paper presents a method for recognizing handwritten digits based on the modeling of convolutional neural network. The integrated fuzzy logic module based on a structural approach was developed. Used system architecture adjusted the output of the neural network to improve quality of symbol identification. It was shown that proposed algorithm was flexible and high recognition rate of 99.23% was achieved.

  6. A Dynamic Bayesian Network Based Structural Learning towards Automated Handwritten Digit Recognition

    NASA Astrophysics Data System (ADS)

    Pauplin, Olivier; Jiang, Jianmin

    Pattern recognition using Dynamic Bayesian Networks (DBNs) is currently a growing area of study. In this paper, we present DBN models trained for classification of handwritten digit characters. The structure of these models is partly inferred from the training data of each class of digit before performing parameter learning. Classification results are presented for the four described models.

  7. New efficient algorithm for recognizing handwritten Hindi digits

    NASA Astrophysics Data System (ADS)

    El-Sonbaty, Yasser; Ismail, Mohammed A.; Karoui, Kamal

    2001-12-01

    In this paper a new algorithm for recognizing handwritten Hindi digits is proposed. The proposed algorithm is based on using the topological characteristics combined with statistical properties of the given digits in order to extract a set of features that can be used in the process of digit classification. 10,000 handwritten digits are used in the experimental results. 1100 digits are used for training and another 5500 unseen digits are used for testing. The recognition rate has reached 97.56%, a substitution rate of 1.822%, and a rejection rate of 0.618%.

  8. Background feature descriptor for offline handwritten numeral recognition

    NASA Astrophysics Data System (ADS)

    Ming, Delie; Wang, Hao; Tian, Tian; Jie, Feiran; Lei, Bo

    2011-11-01

    This paper puts forward an offline handwritten numeral recognition method based on background structural descriptor (sixteen-value numerical background expression). Through encoding the background pixels in the image according to a certain rule, 16 different eigenvalues were generated, which reflected the background condition of every digit, then reflected the structural features of the digits. Through pattern language description of images by these features, automatic segmentation of overlapping digits and numeral recognition can be realized. This method is characterized by great deformation resistant ability, high recognition speed and easy realization. Finally, the experimental results and conclusions are presented. The experimental results of recognizing datasets from various practical application fields reflect that with this method, a good recognition effect can be achieved.

  9. Post processing for offline Chinese handwritten character string recognition

    NASA Astrophysics Data System (ADS)

    Wang, YanWei; Ding, XiaoQing; Liu, ChangSong

    2012-01-01

    Offline Chinese handwritten character string recognition is one of the most important research fields in pattern recognition. Due to the free writing style, large variability in character shapes and different geometric characteristics, Chinese handwritten character string recognition is a challenging problem to deal with. However, among the current methods over-segmentation and merging method which integrates geometric information, character recognition information and contextual information, shows a promising result. It is found experimentally that a large part of errors are segmentation error and mainly occur around non-Chinese characters. In a Chinese character string, there are not only wide characters namely Chinese characters, but also narrow characters like digits and letters of the alphabet. The segmentation error is mainly caused by uniform geometric model imposed on all segmented candidate characters. To solve this problem, post processing is employed to improve recognition accuracy of narrow characters. On one hand, multi-geometric models are established for wide characters and narrow characters respectively. Under multi-geometric models narrow characters are not prone to be merged. On the other hand, top rank recognition results of candidate paths are integrated to boost final recognition of narrow characters. The post processing method is investigated on two datasets, in total 1405 handwritten address strings. The wide character recognition accuracy has been improved lightly and narrow character recognition accuracy has been increased up by 10.41% and 10.03% respectively. It indicates that the post processing method is effective to improve recognition accuracy of narrow characters.

  10. Handwritten recognition of Tamil vowels using deep learning

    NASA Astrophysics Data System (ADS)

    Ram Prashanth, N.; Siddarth, B.; Ganesh, Anirudh; Naveen Kumar, Vaegae

    2017-11-01

    We come across a large volume of handwritten texts in our daily lives and handwritten character recognition has long been an important area of research in pattern recognition. The complexity of the task varies among different languages and it so happens largely due to the similarity between characters, distinct shapes and number of characters which are all language-specific properties. There have been numerous works on character recognition of English alphabets and with laudable success, but regional languages have not been dealt with very frequently and with similar accuracies. In this paper, we explored the performance of Deep Belief Networks in the classification of Handwritten Tamil vowels, and conclusively compared the results obtained. The proposed method has shown satisfactory recognition accuracy in light of difficulties faced with regional languages such as similarity between characters and minute nuances that differentiate them. We can further extend this to all the Tamil characters.

  11. Do handwritten words magnify lexical effects in visual word recognition?

    PubMed

    Perea, Manuel; Gil-López, Cristina; Beléndez, Victoria; Carreiras, Manuel

    2016-01-01

    An examination of how the word recognition system is able to process handwritten words is fundamental to formulate a comprehensive model of visual word recognition. Previous research has revealed that the magnitude of lexical effects (e.g., the word-frequency effect) is greater with handwritten words than with printed words. In the present lexical decision experiments, we examined whether the quality of handwritten words moderates the recruitment of top-down feedback, as reflected in word-frequency effects. Results showed a reading cost for difficult-to-read and easy-to-read handwritten words relative to printed words. But the critical finding was that difficult-to-read handwritten words, but not easy-to-read handwritten words, showed a greater word-frequency effect than printed words. Therefore, the inherent physical variability of handwritten words does not necessarily boost the magnitude of lexical effects.

  12. Handwritten Word Recognition Using Multi-view Analysis

    NASA Astrophysics Data System (ADS)

    de Oliveira, J. J.; de A. Freitas, C. O.; de Carvalho, J. M.; Sabourin, R.

    This paper brings a contribution to the problem of efficiently recognizing handwritten words from a limited size lexicon. For that, a multiple classifier system has been developed that analyzes the words from three different approximation levels, in order to get a computational approach inspired on the human reading process. For each approximation level a three-module architecture composed of a zoning mechanism (pseudo-segmenter), a feature extractor and a classifier is defined. The proposed application is the recognition of the Portuguese handwritten names of the months, for which a best recognition rate of 97.7% was obtained, using classifier combination.

  13. Sunspot drawings handwritten character recognition method based on deep learning

    NASA Astrophysics Data System (ADS)

    Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

    2016-05-01

    High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

  14. New approach for segmentation and recognition of handwritten numeral strings

    NASA Astrophysics Data System (ADS)

    Sadri, Javad; Suen, Ching Y.; Bui, Tien D.

    2004-12-01

    In this paper, we propose a new system for segmentation and recognition of unconstrained handwritten numeral strings. The system uses a combination of foreground and background features for segmentation of touching digits. The method introduces new algorithms for traversing the top/bottom-foreground-skeletons of the touched digits, and for finding feature points on these skeletons, and matching them to build all the segmentation paths. For the first time a genetic representation is used to show all the segmentation hypotheses. Our genetic algorithm tries to search and evolve the population of candidate segmentations and finds the one with the highest confidence for its segmentation and recognition. We have also used a new method for feature extraction which lowers the variations in the shapes of the digits, and then a MLP neural network is utilized to produce the labels and confidence values for those digits. The NIST SD19 and CENPARMI databases are used for evaluating the system. Our system can get a correct segmentation-recognition rate of 96.07% with rejection rate of 2.61% which compares favorably with those that exist in the literature.

  15. New approach for segmentation and recognition of handwritten numeral strings

    NASA Astrophysics Data System (ADS)

    Sadri, Javad; Suen, Ching Y.; Bui, Tien D.

    2005-01-01

    In this paper, we propose a new system for segmentation and recognition of unconstrained handwritten numeral strings. The system uses a combination of foreground and background features for segmentation of touching digits. The method introduces new algorithms for traversing the top/bottom-foreground-skeletons of the touched digits, and for finding feature points on these skeletons, and matching them to build all the segmentation paths. For the first time a genetic representation is used to show all the segmentation hypotheses. Our genetic algorithm tries to search and evolve the population of candidate segmentations and finds the one with the highest confidence for its segmentation and recognition. We have also used a new method for feature extraction which lowers the variations in the shapes of the digits, and then a MLP neural network is utilized to produce the labels and confidence values for those digits. The NIST SD19 and CENPARMI databases are used for evaluating the system. Our system can get a correct segmentation-recognition rate of 96.07% with rejection rate of 2.61% which compares favorably with those that exist in the literature.

  16. Online Farsi digit recognition using their upper half structure

    NASA Astrophysics Data System (ADS)

    Ghods, Vahid; Sohrabi, Mohammad Karim

    2015-03-01

    In this paper, we investigated the efficiency of upper half Farsi numerical digit structure. In other words, half of data (upper half of the digit shapes) was exploited for the recognition of Farsi numerical digits. This method can be used for both offline and online recognition. Half of data is more effective in speed process, data transfer and in this application accuracy. Hidden Markov model (HMM) was used to classify online Farsi digits. Evaluation was performed by TMU dataset. This dataset contains more than 1200 samples of online handwritten Farsi digits. The proposed method yielded more accuracy in recognition rate.

  17. Recognition of Similar Shaped Handwritten Marathi Characters Using Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Jane, Archana P.; Pund, Mukesh A.

    2012-03-01

    The growing need have handwritten Marathi character recognition in Indian offices such as passport, railways etc has made it vital area of a research. Similar shape characters are more prone to misclassification. In this paper a novel method is provided to recognize handwritten Marathi characters based on their features extraction and adaptive smoothing technique. Feature selections methods avoid unnecessary patterns in an image whereas adaptive smoothing technique form smooth shape of charecters.Combination of both these approaches leads to the better results. Previous study shows that, no one technique achieves 100% accuracy in handwritten character recognition area. This approach of combining both adaptive smoothing & feature extraction gives better results (approximately 75-100) and expected outcomes.

  18. Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

    PubMed

    Kulkarni, Shruti R; Rajendran, Bipin

    2018-07-01

    We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

  19. Online handwritten mathematical expression recognition

    NASA Astrophysics Data System (ADS)

    Büyükbayrak, Hakan; Yanikoglu, Berrin; Erçil, Aytül

    2007-01-01

    We describe a system for recognizing online, handwritten mathematical expressions. The system is designed with a user-interface for writing scientific articles, supporting the recognition of basic mathematical expressions as well as integrals, summations, matrices etc. A feed-forward neural network recognizes symbols which are assumed to be single-stroke and a recursive algorithm parses the expression by combining neural network output and the structure of the expression. Preliminary results show that writer-dependent recognition rates are very high (99.8%) while writer-independent symbol recognition rates are lower (75%). The interface associated with the proposed system integrates the built-in recognition capabilities of the Microsoft's Tablet PC API for recognizing textual input and supports conversion of hand-drawn figures into PNG format. This enables the user to enter text, mathematics and draw figures in a single interface. After recognition, all output is combined into one LATEX code and compiled into a PDF file.

  20. Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals.

    PubMed

    Bhattacharya, Ujjwal; Chaudhuri, B B

    2009-03-01

    This article primarily concerns the problem of isolated handwritten numeral recognition of major Indian scripts. The principal contributions presented here are (a) pioneering development of two databases for handwritten numerals of two most popular Indian scripts, (b) a multistage cascaded recognition scheme using wavelet based multiresolution representations and multilayer perceptron classifiers and (c) application of (b) for the recognition of mixed handwritten numerals of three Indian scripts Devanagari, Bangla and English. The present databases include respectively 22,556 and 23,392 handwritten isolated numeral samples of Devanagari and Bangla collected from real-life situations and these can be made available free of cost to researchers of other academic Institutions. In the proposed scheme, a numeral is subjected to three multilayer perceptron classifiers corresponding to three coarse-to-fine resolution levels in a cascaded manner. If rejection occurred even at the highest resolution, another multilayer perceptron is used as the final attempt to recognize the input numeral by combining the outputs of three classifiers of the previous stages. This scheme has been extended to the situation when the script of a document is not known a priori or the numerals written on a document belong to different scripts. Handwritten numerals in mixed scripts are frequently found in Indian postal mails and table-form documents.

  1. Comparison of crisp and fuzzy character networks in handwritten word recognition

    NASA Technical Reports Server (NTRS)

    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.

  2. Robust recognition of handwritten numerals based on dual cooperative network

    NASA Technical Reports Server (NTRS)

    Lee, Sukhan; Choi, Yeongwoo

    1992-01-01

    An approach to robust recognition of handwritten numerals using two operating parallel networks is presented. The first network uses inputs in Cartesian coordinates, and the second network uses the same inputs transformed into polar coordinates. How the proposed approach realizes the robustness to local and global variations of input numerals by handling inputs both in Cartesian coordinates and in its transformed Polar coordinates is described. The required network structures and its learning scheme are discussed. Experimental results show that by tracking only a small number of distinctive features for each teaching numeral in each coordinate, the proposed system can provide robust recognition of handwritten numerals.

  3. Maximum mutual information estimation of a simplified hidden MRF for offline handwritten Chinese character recognition

    NASA Astrophysics Data System (ADS)

    Xiong, Yan; Reichenbach, Stephen E.

    1999-01-01

    Understanding of hand-written Chinese characters is at such a primitive stage that models include some assumptions about hand-written Chinese characters that are simply false. So Maximum Likelihood Estimation (MLE) may not be an optimal method for hand-written Chinese characters recognition. This concern motivates the research effort to consider alternative criteria. Maximum Mutual Information Estimation (MMIE) is an alternative method for parameter estimation that does not derive its rationale from presumed model correctness, but instead examines the pattern-modeling problem in automatic recognition system from an information- theoretic point of view. The objective of MMIE is to find a set of parameters in such that the resultant model allows the system to derive from the observed data as much information as possible about the class. We consider MMIE for recognition of hand-written Chinese characters using on a simplified hidden Markov Random Field. MMIE provides improved performance improvement over MLE in this application.

  4. Transcript mapping for handwritten English documents

    NASA Astrophysics Data System (ADS)

    Jose, Damien; Bharadwaj, Anurag; Govindaraju, Venu

    2008-01-01

    Transcript mapping or text alignment with handwritten documents is the automatic alignment of words in a text file with word images in a handwritten document. Such a mapping has several applications in fields ranging from machine learning where large quantities of truth data are required for evaluating handwriting recognition algorithms, to data mining where word image indexes are used in ranked retrieval of scanned documents in a digital library. The alignment also aids "writer identity" verification algorithms. Interfaces which display scanned handwritten documents may use this alignment to highlight manuscript tokens when a person examines the corresponding transcript word. We propose an adaptation of the True DTW dynamic programming algorithm for English handwritten documents. The integration of the dissimilarity scores from a word-model word recognizer and Levenshtein distance between the recognized word and lexicon word, as a cost metric in the DTW algorithm leading to a fast and accurate alignment, is our primary contribution. Results provided, confirm the effectiveness of our approach.

  5. Evaluating structural pattern recognition for handwritten math via primitive label graphs

    NASA Astrophysics Data System (ADS)

    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.

  6. What differs in visual recognition of handwritten vs. printed letters? An fMRI study.

    PubMed

    Longcamp, Marieke; Hlushchuk, Yevhen; Hari, Riitta

    2011-08-01

    In models of letter recognition, handwritten letters are considered as a particular font exemplar, not qualitatively different in their processing from printed letters. Yet, some data suggest that recognizing handwritten letters might rely on distinct processes, possibly related to motor knowledge. We applied functional magnetic resonance imaging to compare the neural correlates of perceiving handwritten letters vs. standard printed letters. Statistical analysis circumscribed to frontal brain regions involved in hand-movement triggering and execution showed that processing of handwritten letters is supported by a stronger activation of the left primary motor cortex and the supplementary motor area. At the whole-brain level, additional differences between handwritten and printed letters were observed in the right superior frontal, middle occipital, and parahippocampal gyri, and in the left inferior precentral and the fusiform gyri. The results are suggested to indicate embodiment of the visual perception of handwritten letters. Copyright © 2010 Wiley-Liss, Inc.

  7. Performance evaluation of MLP and RBF feed forward neural network for the recognition of off-line handwritten characters

    NASA Astrophysics Data System (ADS)

    Rishi, Rahul; Choudhary, Amit; Singh, Ravinder; Dhaka, Vijaypal Singh; Ahlawat, Savita; Rao, Mukta

    2010-02-01

    In this paper we propose a system for classification problem of handwritten text. The system is composed of preprocessing module, supervised learning module and recognition module on a very broad level. The preprocessing module digitizes the documents and extracts features (tangent values) for each character. The radial basis function network is used in the learning and recognition modules. The objective is to analyze and improve the performance of Multi Layer Perceptron (MLP) using RBF transfer functions over Logarithmic Sigmoid Function. The results of 35 experiments indicate that the Feed Forward MLP performs accurately and exhaustively with RBF. With the change in weight update mechanism and feature-drawn preprocessing module, the proposed system is competent with good recognition show.

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

    PubMed

    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.

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

    PubMed Central

    Peissig, Peggy L; McCarty, Catherine A; Starren, Justin

    2011-01-01

    Background 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. Methods 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. Observations 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%. Discussion 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. PMID:21890871

  10. Offline handwritten word recognition using MQDF-HMMs

    NASA Astrophysics Data System (ADS)

    Ramachandrula, Sitaram; Hambarde, Mangesh; Patial, Ajay; Sahoo, Dushyant; Kochar, Shaivi

    2015-01-01

    We propose an improved HMM formulation for offline handwriting recognition (HWR). The main contribution of this work is using modified quadratic discriminant function (MQDF) [1] within HMM framework. In an MQDF-HMM the state observation likelihood is calculated by a weighted combination of MQDF likelihoods of individual Gaussians of GMM (Gaussian Mixture Model). The quadratic discriminant function (QDF) of a multivariate Gaussian can be rewritten by avoiding the inverse of covariance matrix by using the Eigen values and Eigen vectors of it. The MQDF is derived from QDF by substituting few of badly estimated lower-most Eigen values by an appropriate constant. The estimation errors of non-dominant Eigen vectors and Eigen values of covariance matrix for which the training data is insufficient can be controlled by this approach. MQDF has been successfully shown to improve the character recognition performance [1]. The usage of MQDF in HMM improves the computation, storage and modeling power of HMM when there is limited training data. We have got encouraging results on offline handwritten character (NIST database) and word recognition in English using MQDF HMMs.

  11. Unconstrained handwritten numeral recognition based on radial basis competitive and cooperative networks with spatio-temporal feature representation.

    PubMed

    Lee, S; Pan, J J

    1996-01-01

    This paper presents a new approach to representation and recognition of handwritten numerals. The approach first transforms a two-dimensional (2-D) spatial representation of a numeral into a three-dimensional (3-D) spatio-temporal representation by identifying the tracing sequence based on a set of heuristic rules acting as transformation operators. A multiresolution critical-point segmentation method is then proposed to extract local feature points, at varying degrees of scale and coarseness. A new neural network architecture, referred to as radial-basis competitive and cooperative network (RCCN), is presented especially for handwritten numeral recognition. RCCN is a globally competitive and locally cooperative network with the capability of self-organizing hidden units to progressively achieve desired network performance, and functions as a universal approximator of arbitrary input-output mappings. Three types of RCCNs are explored: input-space RCCN (IRCCN), output-space RCCN (ORCCN), and bidirectional RCCN (BRCCN). Experiments against handwritten zip code numerals acquired by the U.S. Postal Service indicated that the proposed method is robust in terms of variations, deformations, transformations, and corruption, achieving about 97% recognition rate.

  12. Recognition of Handwritten Arabic words using a neuro-fuzzy network

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Boukharouba, Abdelhak; Bennia, Abdelhak

    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 descentmore » 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.« less

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

    PubMed

    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.

  14. Optical character recognition of handwritten Arabic using hidden Markov models

    NASA Astrophysics Data System (ADS)

    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.

  15. Optical character recognition of handwritten Arabic using hidden Markov models

    DOE Office of Scientific and Technical Information (OSTI.GOV)

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

    2011-01-01

    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 ismore » 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.« less

  16. Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition.

    PubMed

    Bianne-Bernard, Anne-Laure; Menasri, Farès; Al-Hajj Mohamad, Rami; Mokbel, Chafic; Kermorvant, Christopher; Likforman-Sulem, Laurence

    2011-10-01

    This study aims at building an efficient word recognition system resulting from the combination of three handwriting recognizers. The main component of this combined system is an HMM-based recognizer which considers dynamic and contextual information for a better modeling of writing units. For modeling the contextual units, a state-tying process based on decision tree clustering is introduced. Decision trees are built according to a set of expert-based questions on how characters are written. Questions are divided into global questions, yielding larger clusters, and precise questions, yielding smaller ones. Such clustering enables us to reduce the total number of models and Gaussians densities by 10. We then apply this modeling to the recognition of handwritten words. Experiments are conducted on three publicly available databases based on Latin or Arabic languages: Rimes, IAM, and OpenHart. The results obtained show that contextual information embedded with dynamic modeling significantly improves recognition.

  17. Structural model constructing for optical handwritten character recognition

    NASA Astrophysics Data System (ADS)

    Khaustov, P. A.; Spitsyn, V. G.; Maksimova, E. I.

    2017-02-01

    The article is devoted to the development of the algorithms for optical handwritten character recognition based on the structural models constructing. The main advantage of these algorithms is the low requirement regarding the number of reference images. The one-pass approach to a thinning of the binary character representation has been proposed. This approach is based on the joint use of Zhang-Suen and Wu-Tsai algorithms. The effectiveness of the proposed approach is confirmed by the results of the experiments. The article includes the detailed description of the structural model constructing algorithm’s steps. The proposed algorithm has been implemented in character processing application and has been approved on MNIST handwriting characters database. Algorithms that could be used in case of limited reference images number were used for the comparison.

  18. Eye movements when reading sentences with handwritten words.

    PubMed

    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.

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

    PubMed

    Biswas, Mithun; Islam, Rafiqul; Shom, Gautam Kumar; Shopon, Md; Mohammed, Nabeel; Momen, Sifat; Abedin, Anowarul

    2017-06-01

    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.

  20. Handwritten character recognition using background analysis

    NASA Astrophysics Data System (ADS)

    Tascini, Guido; Puliti, Paolo; Zingaretti, Primo

    1993-04-01

    The paper describes a low-cost handwritten character recognizer. It is constituted by three modules: the `acquisition' module, the `binarization' module, and the `core' module. The core module can be logically partitioned into six steps: character dilation, character circumscription, region and `profile' analysis, `cut' analysis, decision tree descent, and result validation. Firstly, it reduces the resolution of the binarized regions and detects the minimum rectangle (MR) which encloses the character; the MR partitions the background into regions that surround the character or are enclosed by it, and allows it to define features as `profiles' and `cuts;' a `profile' is the set of vertical or horizontal minimum distances between a side of the MR and the character itself; a `cut' is a vertical or horizontal image segment delimited by the MR. Then, the core module classifies the character by descending along the decision tree on the basis of the analysis of regions around the character, in particular of the `profiles' and `cuts,' and without using context information. Finally, it recognizes the character or reactivates the core module by analyzing validation test results. The recognizer is largely insensible to character discontinuity and is able to detect Arabic numerals and English alphabet capital letters. The recognition rate of a 32 X 32 pixel character is of about 97% after the first iteration, and of over 98% after the second iteration.

  1. Application of the ANNA neural network chip to high-speed character recognition.

    PubMed

    Sackinger, E; Boser, B E; Bromley, J; Lecun, Y; Jackel, L D

    1992-01-01

    A neural network with 136000 connections for recognition of handwritten digits has been implemented using a mixed analog/digital neural network chip. The neural network chip is capable of processing 1000 characters/s. The recognition system has essentially the same rate (5%) as a simulation of the network with 32-b floating-point precision.

  2. Interpreting Chicken-Scratch: Lexical Access for Handwritten Words

    PubMed Central

    Barnhart, Anthony S.; Goldinger, Stephen D.

    2014-01-01

    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 recognition. The current study examined the effects of handwriting on a series of lexical variables thought to influence bottom-up and top-down processing, including word frequency, regularity, bidirectional consistency, and imageability. The results suggest that the natural physical ambiguity of handwritten stimuli forces a greater reliance on top-down processes, because almost all effects were magnified, relative to conditions with computer print. These findings suggest that processes of word perception naturally adapt to handwriting, compensating for physical ambiguity by increasing top-down feedback. PMID:20695708

  3. Interpreting Chicken-Scratch: Lexical Access for Handwritten Words

    ERIC Educational Resources Information Center

    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…

  4. Automatic extraction of numeric strings in unconstrained handwritten document images

    NASA Astrophysics Data System (ADS)

    Haji, M. Mehdi; Bui, Tien D.; Suen, Ching Y.

    2011-01-01

    Numeric strings such as identification numbers carry vital pieces of information in documents. In this paper, we present a novel algorithm for automatic extraction of numeric strings in unconstrained handwritten document images. The algorithm has two main phases: pruning and verification. In the pruning phase, the algorithm first performs a new segment-merge procedure on each text line, and then using a new regularity measure, it prunes all sequences of characters that are unlikely to be numeric strings. The segment-merge procedure is composed of two modules: a new explicit character segmentation algorithm which is based on analysis of skeletal graphs and a merging algorithm which is based on graph partitioning. All the candidate sequences that pass the pruning phase are sent to a recognition-based verification phase for the final decision. The recognition is based on a coarse-to-fine approach using probabilistic RBF networks. We developed our algorithm for the processing of real-world documents where letters and digits may be connected or broken in a document. The effectiveness of the proposed approach is shown by extensive experiments done on a real-world database of 607 documents which contains handwritten, machine-printed and mixed documents with different types of layouts and levels of noise.

  5. Construction of language models for an handwritten mail reading system

    NASA Astrophysics Data System (ADS)

    Morillot, Olivier; Likforman-Sulem, Laurence; Grosicki, Emmanuèle

    2012-01-01

    This paper presents a system for the recognition of unconstrained handwritten mails. The main part of this system is an HMM recognizer which uses trigraphs to model contextual information. This recognition system does not require any segmentation into words or characters and directly works at line level. To take into account linguistic information and enhance performance, a language model is introduced. This language model is based on bigrams and built from training document transcriptions only. Different experiments with various vocabulary sizes and language models have been conducted. Word Error Rate and Perplexity values are compared to show the interest of specific language models, fit to handwritten mail recognition task.

  6. Neural Networks for Handwritten English Alphabet Recognition

    NASA Astrophysics Data System (ADS)

    Perwej, Yusuf; Chaturvedi, Ashish

    2011-04-01

    This paper demonstrates the use of neural networks for developing a system that can recognize hand-written English alphabets. In this system, each English alphabet is represented by binary values that are used as input to a simple feature extraction system, whose output is fed to our neural network system.

  7. Adaptive Learning and Pruning Using Periodic Packet for Fast Invariance Extraction and Recognition

    NASA Astrophysics Data System (ADS)

    Chang, Sheng-Jiang; Zhang, Bian-Li; Lin, Lie; Xiong, Tao; Shen, Jin-Yuan

    2005-02-01

    A new learning scheme using a periodic packet as the neuronal activation function is proposed for invariance extraction and recognition of handwritten digits. Simulation results show that the proposed network can extract the invariant feature effectively and improve both the convergence and the recognition rate.

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

    NASA Astrophysics Data System (ADS)

    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.

  9. Recognition of handwritten similar Chinese characters by self-growing probabilistic decision-based neural network.

    PubMed

    Fu, H C; Xu, Y Y; Chang, H Y

    1999-12-01

    Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters x200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance. As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.

  10. Identifying images of handwritten digits using deep learning in H2O

    NASA Astrophysics Data System (ADS)

    Sadhasivam, Jayakumar; Charanya, R.; Kumar, S. Harish; Srinivasan, A.

    2017-11-01

    Automatic digit recognition is of popular interest today. Deep learning techniques make it possible for object recognition in image data. Perceiving the digit has turned into a fundamental part as far as certifiable applications. Since, digits are composed in various styles in this way to distinguish the digit it is important to perceive and arrange it with the assistance of machine learning methods. This exploration depends on supervised learning vector quantization neural system arranged under counterfeit artificial neural network. The pictures of digits are perceived, prepared and tried. After the system is made digits are prepared utilizing preparing dataset vectors and testing is connected to the pictures of digits which are separated to each other by fragmenting the picture and resizing the digit picture as needs be for better precision.

  11. Recognition of Telugu characters using neural networks.

    PubMed

    Sukhaswami, M B; Seetharamulu, P; Pujari, A K

    1995-09-01

    The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different "hands" in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.

  12. Handwritten mathematical symbols dataset.

    PubMed

    Chajri, Yassine; Bouikhalene, Belaid

    2016-06-01

    Due to the technological advances in recent years, paper scientific documents are used less and less. Thus, the trend in the scientific community to use digital documents has increased considerably. Among these documents, there are scientific documents and more specifically mathematics documents. In this context, we present our own dataset of handwritten mathematical symbols composed of 10,379 images. This dataset gathers Arabic characters, Latin characters, Arabic numerals, Latin numerals, arithmetic operators, set-symbols, comparison symbols, delimiters, etc.

  13. Handwritten mathematical symbols dataset

    PubMed Central

    Chajri, Yassine; Bouikhalene, Belaid

    2016-01-01

    Due to the technological advances in recent years, paper scientific documents are used less and less. Thus, the trend in the scientific community to use digital documents has increased considerably. Among these documents, there are scientific documents and more specifically mathematics documents. In this context, we present our own dataset of handwritten mathematical symbols composed of 10,379 images. This dataset gathers Arabic characters, Latin characters, Arabic numerals, Latin numerals, arithmetic operators, set-symbols, comparison symbols, delimiters, etc. PMID:27006975

  14. Font generation of personal handwritten Chinese characters

    NASA Astrophysics Data System (ADS)

    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.

  15. Slant correction for handwritten English documents

    NASA Astrophysics Data System (ADS)

    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.

  16. A distinguishing method of printed and handwritten legal amount on Chinese bank check

    NASA Astrophysics Data System (ADS)

    Zhu, Ningbo; Lou, Zhen; Yang, Jingyu

    2003-09-01

    While carrying out Optical Chinese Character Recognition, distinguishing the font between printed and handwritten characters at the early phase is necessary, because there is so much difference between the methods on recognizing these two types of characters. In this paper, we proposed a good method on how to banish seals and its relative standards that can judge whether they should be banished. Meanwhile, an approach on clearing up scattered noise shivers after image segmentation is presented. Four sets of classifying features that show discrimination between printed and handwritten characters are well adopted. The proposed approach was applied to an automatic check processing system and tested on about 9031 checks. The recognition rate is more than 99.5%.

  17. Arabic handwritten: pre-processing and segmentation

    NASA Astrophysics Data System (ADS)

    Maliki, Makki; Jassim, Sabah; Al-Jawad, Naseer; Sellahewa, Harin

    2012-06-01

    This paper is concerned with pre-processing and segmentation tasks that influence the performance of Optical Character Recognition (OCR) systems and handwritten/printed text recognition. In Arabic, these tasks are adversely effected by the fact that many words are made up of sub-words, with many sub-words there associated one or more diacritics that are not connected to the sub-word's body; there could be multiple instances of sub-words overlap. To overcome these problems we investigate and develop segmentation techniques that first segment a document into sub-words, link the diacritics with their sub-words, and removes possible overlapping between words and sub-words. We shall also investigate two approaches for pre-processing tasks to estimate sub-words baseline, and to determine parameters that yield appropriate slope correction, slant removal. We shall investigate the use of linear regression on sub-words pixels to determine their central x and y coordinates, as well as their high density part. We also develop a new incremental rotation procedure to be performed on sub-words that determines the best rotation angle needed to realign baselines. We shall demonstrate the benefits of these proposals by conducting extensive experiments on publicly available databases and in-house created databases. These algorithms help improve character segmentation accuracy by transforming handwritten Arabic text into a form that could benefit from analysis of printed text.

  18. A perceptive method for handwritten text segmentation

    NASA Astrophysics Data System (ADS)

    Lemaitre, Aurélie; Camillerapp, Jean; Coüasnon, Bertrand

    2011-01-01

    This paper presents a new method to address the problem of handwritten text segmentation into text lines and words. Thus, we propose a method based on the cooperation among points of view that enables the localization of the text lines in a low resolution image, and then to associate the pixels at a higher level of resolution. Thanks to the combination of levels of vision, we can detect overlapping characters and re-segment the connected components during the analysis. Then, we propose a segmentation of lines into words based on the cooperation among digital data and symbolic knowledge. The digital data are obtained from distances inside a Delaunay graph, which gives a precise distance between connected components, at the pixel level. We introduce structural rules in order to take into account some generic knowledge about the organization of a text page. This cooperation among information gives a bigger power of expression and ensures the global coherence of the recognition. We validate this work using the metrics and the database proposed for the segmentation contest of ICDAR 2009. Thus, we show that our method obtains very interesting results, compared to the other methods of the literature. More precisely, we are able to deal with slope and curvature, overlapping text lines and varied kinds of writings, which are the main difficulties met by the other methods.

  19. Limited receptive area neural classifier for recognition of swallowing sounds using continuous wavelet transform.

    PubMed

    Makeyev, Oleksandr; Sazonov, Edward; Schuckers, Stephanie; Lopez-Meyer, Paulo; Melanson, Ed; Neuman, Michael

    2007-01-01

    In this paper we propose a sound recognition technique based on the limited receptive area (LIRA) neural classifier and continuous wavelet transform (CWT). LIRA neural classifier was developed as a multipurpose image recognition system. Previous tests of LIRA demonstrated good results in different image recognition tasks including: handwritten digit recognition, face recognition, metal surface texture recognition, and micro work piece shape recognition. We propose a sound recognition technique where scalograms of sound instances serve as inputs of the LIRA neural classifier. The methodology was tested in recognition of swallowing sounds. Swallowing sound recognition may be employed in systems for automated swallowing assessment and diagnosis of swallowing disorders. The experimental results suggest high efficiency and reliability of the proposed approach.

  20. Determining the Value of Handwritten Comments within Work Orders

    ERIC Educational Resources Information Center

    Thombs, Daniel

    2010-01-01

    In the workplace many work orders are handwritten on paper rather than recorded in a digital format. Despite being archived, these documents are neither referenced nor analyzed after their creation. Tacit knowledge gathered though employee documentation is generally considered beneficial, but only if it can be easily gathered and processed. …

  1. Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition.

    PubMed

    Wang, Runchun; Thakur, Chetan Singh; Cohen, Gregory; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, Andre

    2017-06-01

    We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously presented an FPGA implementation of the NEF that successfully performs nonlinear mathematical computations. That work was developed based on a compact digital neural core, which consists of 64 neurons that are instantiated by a single physical neuron using a time-multiplexing approach. We have now scaled this approach up to build a pattern recognition system by combining identical neural cores together. As a proof of concept, we have developed a handwritten digit recognition system using the MNIST database and achieved a recognition rate of 96.55%. The system is implemented on a state-of-the-art FPGA and can process 5.12 million digits per second. The architecture and hardware optimisations presented offer high-speed and resource-efficient means for performing high-speed, neuromorphic, and massively parallel pattern recognition and classification tasks.

  2. Kannada character recognition system using neural network

    NASA Astrophysics Data System (ADS)

    Kumar, Suresh D. S.; Kamalapuram, Srinivasa K.; Kumar, Ajay B. R.

    2013-03-01

    Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. As there is no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India. In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten Kannada character is resized into 20x30 Pixel. The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different Kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems.

  3. Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

    PubMed Central

    Yang, Xinyi

    2016-01-01

    In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods. PMID:27610128

  4. Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification.

    PubMed

    Pang, Shan; Yang, Xinyi

    2016-01-01

    In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

  5. Iterative cross section sequence graph for handwritten character segmentation.

    PubMed

    Dawoud, Amer

    2007-08-01

    The iterative cross section sequence graph (ICSSG) is an algorithm for handwritten character segmentation. It expands the cross section sequence graph concept by applying it iteratively at equally spaced thresholds. The iterative thresholding reduces the effect of information loss associated with image binarization. ICSSG preserves the characters' skeletal structure by preventing the interference of pixels that causes flooding of adjacent characters' segments. Improving the structural quality of the characters' skeleton facilitates better feature extraction and classification, which improves the overall performance of optical character recognition (OCR). Experimental results showed significant improvements in OCR recognition rates compared to other well-established segmentation algorithms.

  6. On the Optimum Architecture of the Biologically Inspired Hierarchical Temporal Memory Model Applied to the Hand-Written Digit Recognition

    NASA Astrophysics Data System (ADS)

    Štolc, Svorad; Bajla, Ivan

    2010-01-01

    In the paper we describe basic functions of the Hierarchical Temporal Memory (HTM) network based on a novel biologically inspired model of the large-scale structure of the mammalian neocortex. The focus of this paper is in a systematic exploration of possibilities how to optimize important controlling parameters of the HTM model applied to the classification of hand-written digits from the USPS database. The statistical properties of this database are analyzed using the permutation test which employs a randomization distribution of the training and testing data. Based on a notion of the homogeneous usage of input image pixels, a methodology of the HTM parameter optimization is proposed. In order to study effects of two substantial parameters of the architecture: the patch size and the overlap in more details, we have restricted ourselves to the single-level HTM networks. A novel method for construction of the training sequences by ordering series of the static images is developed. A novel method for estimation of the parameter maxDist based on the box counting method is proposed. The parameter sigma of the inference Gaussian is optimized on the basis of the maximization of the belief distribution entropy. Both optimization algorithms can be equally applied to the multi-level HTM networks as well. The influences of the parameters transitionMemory and requestedGroupCount on the HTM network performance have been explored. Altogether, we have investigated 2736 different HTM network configurations. The obtained classification accuracy results have been benchmarked with the published results of several conventional classifiers.

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

    NASA Astrophysics Data System (ADS)

    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.

  8. Digital signal processing algorithms for automatic voice recognition

    NASA Technical Reports Server (NTRS)

    Botros, Nazeih M.

    1987-01-01

    The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms.

  9. The Characteristics of Binary Spike-Time-Dependent Plasticity in HfO2-Based RRAM and Applications for Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Zhou, Zheng; Liu, Chen; Shen, Wensheng; Dong, Zhen; Chen, Zhe; Huang, Peng; Liu, Lifeng; Liu, Xiaoyan; Kang, Jinfeng

    2017-04-01

    A binary spike-time-dependent plasticity (STDP) protocol based on one resistive-switching random access memory (RRAM) device was proposed and experimentally demonstrated in the fabricated RRAM array. Based on the STDP protocol, a novel unsupervised online pattern recognition system including RRAM synapses and CMOS neurons is developed. Our simulations show that the system can efficiently compete the handwritten digits recognition task, which indicates the feasibility of using the RRAM-based binary STDP protocol in neuromorphic computing systems to obtain good performance.

  10. Permutation coding technique for image recognition systems.

    PubMed

    Kussul, Ernst M; Baidyk, Tatiana N; Wunsch, Donald C; Makeyev, Oleksandr; Martín, Anabel

    2006-11-01

    A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1%.

  11. Handwritten-word spotting using biologically inspired features.

    PubMed

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

    2008-11-01

    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 whole-word recognition method which is used to incrementally elicit word labels in a live, web-based annotation system, named Monk. Since human labor should be minimized given the massive amount of image data, it becomes important to rely on robust perceptual mechanisms in the machine. Recent computational models of the neuro-physiology of vision are applied to isolated word classification. A primate cortex-like mechanism allows to classify text-images that have a low frequency of occurrence. Typically these images are the most difficult to retrieve and often contain named entities and are regarded as the most important to people. Usually standard pattern-recognition technology cannot deal with these text-images if there are not enough labeled instances. The results of this retrieval system are compared to normalized word-image matching and appear to be very promising.

  12. Handwritten text line segmentation by spectral clustering

    NASA Astrophysics Data System (ADS)

    Han, Xuecheng; Yao, Hui; Zhong, Guoqiang

    2017-02-01

    Since handwritten text lines are generally skewed and not obviously separated, text line segmentation of handwritten document images is still a challenging problem. In this paper, we propose a novel text line segmentation algorithm based on the spectral clustering. Given a handwritten document image, we convert it to a binary image first, and then compute the adjacent matrix of the pixel points. We apply spectral clustering on this similarity metric and use the orthogonal kmeans clustering algorithm to group the text lines. Experiments on Chinese handwritten documents database (HIT-MW) demonstrate the effectiveness of the proposed method.

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

    NASA Astrophysics Data System (ADS)

    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.

  14. Automatic forensic face recognition from digital images.

    PubMed

    Peacock, C; Goode, A; Brett, A

    2004-01-01

    Digital image evidence is now widely available from criminal investigations and surveillance operations, often captured by security and surveillance CCTV. This has resulted in a growing demand from law enforcement agencies for automatic person-recognition based on image data. In forensic science, a fundamental requirement for such automatic face recognition is to evaluate the weight that can justifiably be attached to this recognition evidence in a scientific framework. This paper describes a pilot study carried out by the Forensic Science Service (UK) which explores the use of digital facial images in forensic investigation. For the purpose of the experiment a specific software package was chosen (Image Metrics Optasia). The paper does not describe the techniques used by the software to reach its decision of probabilistic matches to facial images, but accepts the output of the software as though it were a 'black box'. In this way, the paper lays a foundation for how face recognition systems can be compared in a forensic framework. The aim of the paper is to explore how reliably and under what conditions digital facial images can be presented in evidence.

  15. Hemispheric Differences in Processing Handwritten Cursive

    ERIC Educational Resources Information Center

    Hellige, Joseph B.; Adamson, Maheen M.

    2007-01-01

    Hemispheric asymmetry was examined for native English speakers identifying consonant-vowel-consonant (CVC) non-words presented in standard printed form, in standard handwritten cursive form or in handwritten cursive with the letters separated by small gaps. For all three conditions, fewer errors occurred when stimuli were presented to the right…

  16. Assessment of legibility and completeness of handwritten and electronic prescriptions.

    PubMed

    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 < 0.001) was observed between handwritten and e-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.

  17. Assessment of legibility and completeness of handwritten and electronic prescriptions

    PubMed Central

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

    2014-01-01

    Objectives To assess the legibility and completeness of handwritten prescriptions and compare with electronic prescription system for medication errors. Design Prospective study. Setting King Khalid University Hospital (KKUH), Riyadh, Saudi Arabia. Subjects and methods 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. Main outcome measures Legibility and completeness of prescriptions. Results 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 < 0.001) was observed between handwritten and e-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. Conclusions 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. PMID:25561864

  18. Boosting bonsai trees for handwritten/printed text discrimination

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    Sadeghi, Zahra; Testolin, Alberto

    2017-08-01

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

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

    NASA Technical Reports Server (NTRS)

    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.

  1. Character context: a shape descriptor for Arabic handwriting recognition

    NASA Astrophysics Data System (ADS)

    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.

  2. ASM Based Synthesis of Handwritten Arabic Text Pages

    PubMed Central

    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. PMID:26295059

  3. ASM Based Synthesis of Handwritten Arabic Text Pages.

    PubMed

    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.

  4. The recognition of graphical patterns invariant to geometrical transformation of the models

    NASA Astrophysics Data System (ADS)

    Ileană, Ioan; Rotar, Corina; Muntean, Maria; Ceuca, Emilian

    2010-11-01

    In case that a pattern recognition system is used for images recognition (in robot vision, handwritten recognition etc.), the system must have the capacity to identify an object indifferently of its size or position in the image. The problem of the invariance of recognition can be approached in some fundamental modes. One may apply the similarity criterion used in associative recall. The original pattern is replaced by a mathematical transform that assures some invariance (e.g. the value of two-dimensional Fourier transformation is translation invariant, the value of Mellin transformation is scale invariant). In a different approach the original pattern is represented through a set of features, each of them being coded indifferently of the position, orientation or position of the pattern. Generally speaking, it is easy to obtain invariance in relation with one transformation group, but is difficult to obtain simultaneous invariance at rotation, translation and scale. In this paper we analyze some methods to achieve invariant recognition of images, particularly for digit images. A great number of experiments are due and the conclusions are underplayed in the paper.

  5. Text-image alignment for historical handwritten documents

    NASA Astrophysics Data System (ADS)

    Zinger, S.; Nerbonne, J.; Schomaker, L.

    2009-01-01

    We describe our work on text-image alignment in context of building a historical document retrieval system. We aim at aligning images of words in handwritten lines with their text transcriptions. The images of handwritten lines are automatically segmented from the scanned pages of historical documents and then manually transcribed. To train automatic routines to detect words in an image of handwritten text, we need a training set - images of words with their transcriptions. We present our results on aligning words from the images of handwritten lines and their corresponding text transcriptions. Alignment based on the longest spaces between portions of handwriting is a baseline. We then show that relative lengths, i.e. proportions of words in their lines, can be used to improve the alignment results considerably. To take into account the relative word length, we define the expressions for the cost function that has to be minimized for aligning text words with their images. We apply right to left alignment as well as alignment based on exhaustive search. The quality assessment of these alignments shows correct results for 69% of words from 100 lines, or 90% of partially correct and correct alignments combined.

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

    PubMed Central

    McDonnell, Mark D.; Tissera, Migel D.; Vladusich, Tony; van Schaik, André; Tapson, Jonathan

    2015-01-01

    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. PMID:26262687

  7. Combination of dynamic Bayesian network classifiers for the recognition of degraded characters

    NASA Astrophysics Data System (ADS)

    Likforman-Sulem, Laurence; Sigelle, Marc

    2009-01-01

    We investigate in this paper the combination of DBN (Dynamic Bayesian Network) classifiers, either independent or coupled, for the recognition of degraded characters. The independent classifiers are a vertical HMM and a horizontal HMM whose observable outputs are the image columns and the image rows respectively. The coupled classifiers, presented in a previous study, associate the vertical and horizontal observation streams into single DBNs. The scores of the independent and coupled classifiers are then combined linearly at the decision level. We compare the different classifiers -independent, coupled or linearly combined- on two tasks: the recognition of artificially degraded handwritten digits and the recognition of real degraded old printed characters. Our results show that coupled DBNs perform better on degraded characters than the linear combination of independent HMM scores. Our results also show that the best classifier is obtained by linearly combining the scores of the best coupled DBN and the best independent HMM.

  8. Automatic Mexican sign language and digits recognition using normalized central moments

    NASA Astrophysics Data System (ADS)

    Solís, Francisco; Martínez, David; Espinosa, Oscar; Toxqui, Carina

    2016-09-01

    This work presents a framework for automatic Mexican sign language and digits recognition based on computer vision system using normalized central moments and artificial neural networks. Images are captured by digital IP camera, four LED reflectors and a green background in order to reduce computational costs and prevent the use of special gloves. 42 normalized central moments are computed per frame and used in a Multi-Layer Perceptron to recognize each database. Four versions per sign and digit were used in training phase. 93% and 95% of recognition rates were achieved for Mexican sign language and digits respectively.

  9. Semi-automatic ground truth generation using unsupervised clustering and limited manual labeling: Application to handwritten character recognition

    PubMed Central

    Vajda, Szilárd; Rangoni, Yves; Cecotti, Hubert

    2015-01-01

    For training supervised classifiers to recognize different patterns, large data collections with accurate labels are necessary. In this paper, we propose a generic, semi-automatic labeling technique for large handwritten character collections. In order to speed up the creation of a large scale ground truth, the method combines unsupervised clustering and minimal expert knowledge. To exploit the potential discriminant complementarities across features, each character is projected into five different feature spaces. After clustering the images in each feature space, the human expert labels the cluster centers. Each data point inherits the label of its cluster’s center. A majority (or unanimity) vote decides the label of each character image. The amount of human involvement (labeling) is strictly controlled by the number of clusters – produced by the chosen clustering approach. To test the efficiency of the proposed approach, we have compared, and evaluated three state-of-the art clustering methods (k-means, self-organizing maps, and growing neural gas) on the MNIST digit data set, and a Lampung Indonesian character data set, respectively. Considering a k-nn classifier, we show that labeling manually only 1.3% (MNIST), and 3.2% (Lampung) of the training data, provides the same range of performance than a completely labeled data set would. PMID:25870463

  10. Optical character recognition with feature extraction and associative memory matrix

    NASA Astrophysics Data System (ADS)

    Sasaki, Osami; Shibahara, Akihito; Suzuki, Takamasa

    1998-06-01

    A method is proposed in which handwritten characters are recognized using feature extraction and an associative memory matrix. In feature extraction, simple processes such as shifting and superimposing patterns are executed. A memory matrix is generated with singular value decomposition and by modifying small singular values. The method is optically implemented with two liquid crystal displays. Experimental results for the recognition of 25 handwritten alphabet characters clearly shows the effectiveness of the method.

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

    NASA Astrophysics Data System (ADS)

    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%.

  12. The Need for Careful Data Collection for Pattern Recognition in Digital Pathology.

    PubMed

    Marée, Raphaël

    2017-01-01

    Effective pattern recognition requires carefully designed ground-truth datasets. In this technical note, we first summarize potential data collection issues in digital pathology and then propose guidelines to build more realistic ground-truth datasets and to control their quality. We hope our comments will foster the effective application of pattern recognition approaches in digital pathology.

  13. A Novel Handwritten Letter Recognizer Using Enhanced Evolutionary Neural Network

    NASA Astrophysics Data System (ADS)

    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.

  14. Speech Recognition for A Digital Video Library.

    ERIC Educational Resources Information Center

    Witbrock, Michael J.; Hauptmann, Alexander G.

    1998-01-01

    Production of the meta-data supporting the Informedia Digital Video Library interface is automated using techniques derived from artificial intelligence research. Speech recognition and natural-language processing, information retrieval, and image analysis are applied to produce an interface that helps users locate information and navigate more…

  15. Quantify spatial relations to discover handwritten graphical symbols

    NASA Astrophysics Data System (ADS)

    Li, Jinpeng; Mouchère, Harold; Viard-Gaudin, Christian

    2012-01-01

    To model a handwritten graphical language, spatial relations describe how the strokes are positioned in the 2-dimensional space. Most of existing handwriting recognition systems make use of some predefined spatial relations. However, considering a complex graphical language, it is hard to express manually all the spatial relations. Another possibility would be to use a clustering technique to discover the spatial relations. In this paper, we discuss how to create a relational graph between strokes (nodes) labeled with graphemes in a graphical language. Then we vectorize spatial relations (edges) for clustering and quantization. As the targeted application, we extract the repetitive sub-graphs (graphical symbols) composed of graphemes and learned spatial relations. On two handwriting databases, a simple mathematical expression database and a complex flowchart database, the unsupervised spatial relations outperform the predefined spatial relations. In addition, we visualize the frequent patterns on two text-lines containing Chinese characters.

  16. Interactive-predictive detection of handwritten text blocks

    NASA Astrophysics Data System (ADS)

    Ramos Terrades, O.; Serrano, N.; Gordó, A.; Valveny, E.; Juan, A.

    2010-01-01

    A method for text block detection is introduced for old handwritten documents. The proposed method takes advantage of sequential book structure, taking into account layout information from pages previously transcribed. This glance at the past is used to predict the position of text blocks in the current page with the help of conventional layout analysis methods. The method is integrated into the GIDOC prototype: a first attempt to provide integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. Results are given in a transcription task on a 764-page Spanish manuscript from 1891.

  17. Implementation of age and gender recognition system for intelligent digital signage

    NASA Astrophysics Data System (ADS)

    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.

  18. Spotting words in handwritten Arabic documents

    NASA Astrophysics Data System (ADS)

    Srihari, Sargur; Srinivasan, Harish; Babu, Pavithra; Bhole, Chetan

    2006-01-01

    The design and performance of a system for spotting handwritten Arabic words in scanned document images is presented. Three main components of the system are a word segmenter, a shape based matcher for words and a search interface. The user types in a query in English within a search window, the system finds the equivalent Arabic word, e.g., by dictionary look-up, locates word images in an indexed (segmented) set of documents. A two-step approach is employed in performing the search: (1) prototype selection: the query is used to obtain a set of handwritten samples of that word from a known set of writers (these are the prototypes), and (2) word matching: the prototypes are used to spot each occurrence of those words in the indexed document database. A ranking is performed on the entire set of test word images-- where the ranking criterion is a similarity score between each prototype word and the candidate words based on global word shape features. A database of 20,000 word images contained in 100 scanned handwritten Arabic documents written by 10 different writers was used to study retrieval performance. Using five writers for providing prototypes and the other five for testing, using manually segmented documents, 55% precision is obtained at 50% recall. Performance increases as more writers are used for training.

  19. An online handwriting recognition system for Turkish

    NASA Astrophysics Data System (ADS)

    Vural, Esra; Erdogan, Hakan; Oflazer, Kemal; Yanikoglu, Berrin A.

    2004-12-01

    Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

  20. An online handwriting recognition system for Turkish

    NASA Astrophysics Data System (ADS)

    Vural, Esra; Erdogan, Hakan; Oflazer, Kemal; Yanikoglu, Berrin A.

    2005-01-01

    Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

  1. Recognition of handprinted characters for automated cartography A progress report

    NASA Technical Reports Server (NTRS)

    Lybanon, M.; Brown, R. M.; Gronmeyer, L. K.

    1980-01-01

    A research program for developing handwritten character recognition techniques is reported. The generation of cartographic/hydrographic manuscripts is overviewed. The performance of hardware/software systems is discussed, along with future research problem areas and planned approaches.

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

    PubMed

    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.

  3. Local Subspace Classifier with Transform-Invariance for Image Classification

    NASA Astrophysics Data System (ADS)

    Hotta, Seiji

    A family of linear subspace classifiers called local subspace classifier (LSC) outperforms the k-nearest neighbor rule (kNN) and conventional subspace classifiers in handwritten digit classification. However, LSC suffers very high sensitivity to image transformations because it uses projection and the Euclidean distances for classification. In this paper, I present a combination of a local subspace classifier (LSC) and a tangent distance (TD) for improving accuracy of handwritten digit recognition. In this classification rule, we can deal with transform-invariance easily because we are able to use tangent vectors for approximation of transformations. However, we cannot use tangent vectors in other type of images such as color images. Hence, kernel LSC (KLSC) is proposed for incorporating transform-invariance into LSC via kernel mapping. The performance of the proposed methods is verified with the experiments on handwritten digit and color image classification.

  4. Handwritten document age classification based on handwriting styles

    NASA Astrophysics Data System (ADS)

    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.

  5. Simulation Detection in Handwritten Documents by Forensic Document Examiners.

    PubMed

    Kam, Moshe; Abichandani, Pramod; Hewett, Tom

    2015-07-01

    This study documents the results of a controlled experiment designed to quantify the abilities of forensic document examiners (FDEs) and laypersons to detect simulations in handwritten documents. Nineteen professional FDEs and 26 laypersons (typical of a jury pool) were asked to inspect test packages that contained six (6) known handwritten documents written by the same person and two (2) questioned handwritten documents. Each questioned document was either written by the person who wrote the known documents, or written by a different person who tried to simulate the writing of the person who wrote the known document. The error rates of the FDEs were smaller than those of the laypersons when detecting simulations in the questioned documents. Among other findings, the FDEs never labeled a questioned document that was written by the same person who wrote the known documents as "simulation." There was a significant statistical difference between the responses of the FDEs and layperson for documents without simulations. © 2015 American Academy of Forensic Sciences.

  6. Teachers' Perceptions of Digital Badges as Recognition of Professional Development

    ERIC Educational Resources Information Center

    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…

  7. Orthographic and phonological neighborhood effects in handwritten word perception

    PubMed Central

    Goldinger, Stephen D.

    2017-01-01

    In printed-word perception, the orthographic neighborhood effect (i.e., faster recognition of words with more neighbors) has considerable theoretical importance, because it implicates great interactivity in lexical access. Mulatti, Reynolds, and Besner Journal of Experimental Psychology: Human Perception and Performance, 32, 799–810 (2006) questioned the validity of orthographic neighborhood effects, suggesting that they reflect a confound with phonological neighborhood density. They reported that, when phonological density is controlled, orthographic neighborhood effects vanish. Conversely, phonological neighborhood effects were still evident even when controlling for orthographic neighborhood density. The present study was a replication and extension of Mulatti et al. (2006), with words presented in four different formats (computer-generated print and cursive, and handwritten print and cursive). The results from Mulatti et al. (2006) were replicated with computer-generated stimuli, but were reversed with natural stimuli. These results suggest that, when ambiguity is introduced at the level of individual letters, top-down influences from lexical neighbors are increased. PMID:26306881

  8. Signature Verification Based on Handwritten Text Recognition

    NASA Astrophysics Data System (ADS)

    Viriri, Serestina; Tapamo, Jules-R.

    Signatures continue to be an important biometric trait because it remains widely used primarily for authenticating the identity of human beings. This paper presents an efficient text-based directional signature recognition algorithm which verifies signatures, even when they are composed of special unconstrained cursive characters which are superimposed and embellished. This algorithm extends the character-based signature verification technique. The experiments carried out on the GPDS signature database and an additional database created from signatures captured using the ePadInk tablet, show that the approach is effective and efficient, with a positive verification rate of 94.95%.

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

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Dise, J; McDonald, D; Ashenafi, M

    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 withmore » 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

  10. Rotation Reveals the Importance of Configural Cues in Handwritten Word Perception

    PubMed Central

    Barnhart, Anthony S.; Goldinger, Stephen D.

    2013-01-01

    A dramatic perceptual asymmetry occurs when handwritten words are rotated 90° in either direction. Those rotated in a direction consistent with their natural tilt (typically clockwise) become much more difficult to recognize, relative to those rotated in the opposite direction. In Experiment 1, we compared computer-printed and handwritten words, all equated for degrees of leftward and rightward tilt, and verified the phenomenon: The effect of rotation was far larger for cursive words, especially when rotated in a tilt-consistent direction. In Experiment 2, we replicated this pattern with all items presented in visual noise. In both experiments, word frequency effects were larger for computer-printed words and did not interact with rotation. The results suggest that handwritten word perception requires greater configural processing, relative to computer print, because handwritten letters are variable and ambiguous. When words are rotated, configural processing suffers, particularly when rotation exaggerates natural tilt. Our account is similar to theories of the “Thatcher Illusion,” wherein face inversion disrupts holistic processing. Together, the findings suggest that configural, word-level processing automatically increases when people read handwriting, as letter-level processing becomes less reliable. PMID:23589201

  11. Unsupervised Word Spotting in Historical Handwritten Document Images using Document-oriented Local Features.

    PubMed

    Zagoris, Konstantinos; Pratikakis, Ioannis; Gatos, Basilis

    2017-05-03

    Word spotting strategies employed in historical handwritten documents face many challenges due to variation in the writing style and intense degradation. In this paper, a new method that permits effective word spotting in handwritten documents is presented that it relies upon document-oriented local features which take into account information around representative keypoints as well a matching process that incorporates spatial context in a local proximity search without using any training data. Experimental results on four historical handwritten datasets for two different scenarios (segmentation-based and segmentation-free) using standard evaluation measures show the improved performance achieved by the proposed methodology.

  12. Target recognition and phase acquisition by using incoherent digital holographic imaging

    NASA Astrophysics Data System (ADS)

    Lee, Munseob; Lee, Byung-Tak

    2017-05-01

    In this study, we proposed the Incoherent Digital Holographic Imaging (IDHI) for recognition and phase information of dedicated target. Although recent development of a number of target recognition techniques such as LIDAR, there have limited success in target discrimination, in part due to low-resolution, low scanning speed, and computation power. In the paper, the proposed system consists of the incoherent light source, such as LED, Michelson interferometer, and digital CCD for acquisition of four phase shifting image. First of all, to compare with relative coherence, we used a source as laser and LED, respectively. Through numerical reconstruction by using the four phase shifting method and Fresnel diffraction method, we recovered the intensity and phase image of USAF resolution target apart from about 1.0m distance. In this experiment, we show 1.2 times improvement in resolution compared to conventional imaging. Finally, to confirm the recognition result of camouflaged targets with the same color from background, we carry out to test holographic imaging in incoherent light. In this result, we showed the possibility of a target detection and recognition that used three dimensional shape and size signatures, numerical distance from phase information of obtained holographic image.

  13. Kernel-aligned multi-view canonical correlation analysis for image recognition

    NASA Astrophysics Data System (ADS)

    Su, Shuzhi; Ge, Hongwei; Yuan, Yun-Hao

    2016-09-01

    Existing kernel-based correlation analysis methods mainly adopt a single kernel in each view. However, only a single kernel is usually insufficient to characterize nonlinear distribution information of a view. To solve the problem, we transform each original feature vector into a 2-dimensional feature matrix by means of kernel alignment, and then propose a novel kernel-aligned multi-view canonical correlation analysis (KAMCCA) method on the basis of the feature matrices. Our proposed method can simultaneously employ multiple kernels to better capture the nonlinear distribution information of each view, so that correlation features learned by KAMCCA can have well discriminating power in real-world image recognition. Extensive experiments are designed on five real-world image datasets, including NIR face images, thermal face images, visible face images, handwritten digit images, and object images. Promising experimental results on the datasets have manifested the effectiveness of our proposed method.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    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.

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

    PubMed Central

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

    2016-01-01

    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. PMID:26978368

  17. Dual function seal: visualized digital signature for electronic medical record systems.

    PubMed

    Yu, Yao-Chang; Hou, Ting-Wei; Chiang, Tzu-Chiang

    2012-10-01

    Digital signature is an important cryptography technology to be used to provide integrity and non-repudiation in electronic medical record systems (EMRS) and it is required by law. However, digital signatures normally appear in forms unrecognizable to medical staff, this may reduce the trust from medical staff that is used to the handwritten signatures or seals. Therefore, in this paper we propose a dual function seal to extend user trust from a traditional seal to a digital signature. The proposed dual function seal is a prototype that combines the traditional seal and digital seal. With this prototype, medical personnel are not just can put a seal on paper but also generate a visualized digital signature for electronic medical records. Medical Personnel can then look at the visualized digital signature and directly know which medical personnel generated it, just like with a traditional seal. Discrete wavelet transform (DWT) is used as an image processing method to generate a visualized digital signature, and the peak signal to noise ratio (PSNR) is calculated to verify that distortions of all converted images are beyond human recognition, and the results of our converted images are from 70 dB to 80 dB. The signature recoverability is also tested in this proposed paper to ensure that the visualized digital signature is verifiable. A simulated EMRS is implemented to show how the visualized digital signature can be integrity into EMRS.

  18. A Parallel Neuromorphic Text Recognition System and Its Implementation on a Heterogeneous High-Performance Computing Cluster

    DTIC Science & Technology

    2013-01-01

    M. Ahmadi, and M. Shridhar, “ Handwritten Numeral Recognition with Multiple Features and Multistage Classifiers,” Proc. IEEE Int’l Symp. Circuits...ARTICLE (Post Print) 3. DATES COVERED (From - To) SEP 2011 – SEP 2013 4. TITLE AND SUBTITLE A PARALLEL NEUROMORPHIC TEXT RECOGNITION SYSTEM AND ITS...research in computational intelligence has entered a new era. In this paper, we present an HPC-based context-aware intelligent text recognition

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

    PubMed

    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.

  20. A comparison study between MLP and convolutional neural network models for character recognition

    NASA Astrophysics Data System (ADS)

    Ben Driss, S.; Soua, M.; Kachouri, R.; Akil, M.

    2017-05-01

    Optical Character Recognition (OCR) systems have been designed to operate on text contained in scanned documents and images. They include text detection and character recognition in which characters are described then classified. In the classification step, characters are identified according to their features or template descriptions. Then, a given classifier is employed to identify characters. In this context, we have proposed the unified character descriptor (UCD) to represent characters based on their features. Then, matching was employed to ensure the classification. This recognition scheme performs a good OCR Accuracy on homogeneous scanned documents, however it cannot discriminate characters with high font variation and distortion.3 To improve recognition, classifiers based on neural networks can be used. The multilayer perceptron (MLP) ensures high recognition accuracy when performing a robust training. Moreover, the convolutional neural network (CNN), is gaining nowadays a lot of popularity for its high performance. Furthermore, both CNN and MLP may suffer from the large amount of computation in the training phase. In this paper, we establish a comparison between MLP and CNN. We provide MLP with the UCD descriptor and the appropriate network configuration. For CNN, we employ the convolutional network designed for handwritten and machine-printed character recognition (Lenet-5) and we adapt it to support 62 classes, including both digits and characters. In addition, GPU parallelization is studied to speed up both of MLP and CNN classifiers. Based on our experimentations, we demonstrate that the used real-time CNN is 2x more relevant than MLP when classifying characters.

  1. Rapid detection of malignant bio-species using digital holographic pattern recognition and nano-photonics

    NASA Astrophysics Data System (ADS)

    Sarkisov, Sergey S.; Kukhtareva, Tatiana; Kukhtarev, Nickolai V.; Curley, Michael J.; Edwards, Vernessa; Creer, Marylyn

    2013-03-01

    There is a great need for rapid detection of bio-hazardous species particularly in applications to food safety and biodefense. It has been recently demonstrated that the colonies of various bio-species could be rapidly detected using culture-specific and reproducible patterns generated by scattered non-coherent light. However, the method heavily relies on a digital pattern recognition algorithm, which is rather complex, requires substantial computational power and is prone to ambiguities due to shift, scale, or orientation mismatch between the analyzed pattern and the reference from the library. The improvement could be made, if, in addition to the intensity of the scattered optical wave, its phase would be also simultaneously recorded and used for the digital holographic pattern recognition. In this feasibility study the research team recorded digital Gabor-type (in-line) holograms of colonies of micro-organisms, such as Salmonella with a laser diode as a low-coherence light source and a lensless high-resolution (2.0x2.0 micron pixel pitch) digital image sensor. The colonies were grown in conventional Petri dishes using standard methods. The digitally recorded holograms were used for computational reconstruction of the amplitude and phase information of the optical wave diffracted on the colonies. Besides, the pattern recognition of the colony fragments using the cross-correlation between the digital hologram was also implemented. The colonies of mold fungi Altenaria sp, Rhizophus, sp, and Aspergillus sp have been also generating nano-colloidal silver during their growth in specially prepared matrices. The silver-specific plasmonic optical extinction peak at 410-nm was also used for rapid detection and growth monitoring of the fungi colonies.

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

    NASA Astrophysics Data System (ADS)

    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

  3. Modeling the Lexical Morphology of Western Handwritten Signatures

    PubMed Central

    Diaz-Cabrera, Moises; Ferrer, Miguel A.; Morales, Aythami

    2015-01-01

    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. PMID:25860942

  4. Correlation of patient entry rates and physician documentation errors in dictated and handwritten emergency treatment records.

    PubMed

    Dawdy, M R; Munter, D W; Gilmore, R A

    1997-03-01

    This study was designed to examine the relationship between patient entry rates (a measure of physician work load) and documentation errors/omissions in both handwritten and dictated emergency treatment records. The study was carried out in two phases. Phase I examined handwritten records and Phase II examined dictated and transcribed records. A total of 838 charts for three common chief complaints (chest pain, abdominal pain, asthma/chronic obstructive pulmonary disease) were retrospectively reviewed and scored for the presence or absence of 11 predetermined criteria. Patient entry rates were determined by reviewing the emergency department patient registration logs. The data were analyzed using simple correlation and linear regression analysis. A positive correlation was found between patient entry rates and documentation errors in handwritten charts. No such correlation was found in the dictated charts. We conclude that work load may negatively affect documentation accuracy when charts are handwritten. However, the use of dictation services may minimize or eliminate this effect.

  5. Diffuse Interface Methods for Multiclass Segmentation of High-Dimensional Data

    DTIC Science & Technology

    2014-03-04

    handwritten digits , 1998. http://yann.lecun.com/exdb/mnist/. [19] S. Nene, S. Nayar, H. Murase, Columbia Object Image Library (COIL-100), Technical Report... recognition on smartphones using a multiclass hardware-friendly support vector machine, in: Ambient Assisted Living and Home Care, Springer, 2012, pp. 216–223.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    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.

  8. Handwritten Newspapers on the Iowa Frontier, 1844-54.

    ERIC Educational Resources Information Center

    Atwood, Roy Alden

    Journalism on the agricultural frontier of the Old Northwest territory of the United States was shaped by a variety of cultural forces and environmental factors and took on diverse forms. Bridging the gap between the two cultural forms of written correspondence and printed news was a third form: the handwritten newspaper. Between 1844 and 1854…

  9. Script-independent text line segmentation in freestyle handwritten documents.

    PubMed

    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.

  10. Spatial Analysis of Handwritten Texts as a Marker of Cognitive Control.

    PubMed

    Crespo, Y; Soriano, M F; Iglesias-Parro, S; Aznarte, J I; Ibáñez-Molina, A J

    2017-12-01

    We explore the idea that cognitive demands of the handwriting would influence the degree of automaticity of the handwriting process, which in turn would affect the geometric parameters of texts. We compared the heterogeneity of handwritten texts in tasks with different cognitive demands; the heterogeneity of texts was analyzed with lacunarity, a measure of geometrical invariance. In Experiment 1, we asked participants to perform two tasks that varied in cognitive demands: transcription and exposition about an autobiographical episode. Lacunarity was significantly lower in transcription. In Experiment 2, we compared a veridical and a fictitious version of a personal event. Lacunarity was lower in veridical texts. We contend that differences in lacunarity of handwritten texts reveal the degree of automaticity in handwriting.

  11. Facial Recognition in a Discus Fish (Cichlidae): Experimental Approach Using Digital Models.

    PubMed

    Satoh, Shun; Tanaka, Hirokazu; Kohda, Masanori

    2016-01-01

    A number of mammals and birds are known to be capable of visually discriminating between familiar and unfamiliar individuals, depending on facial patterns in some species. Many fish also visually recognize other conspecifics individually, and previous studies report that facial color patterns can be an initial signal for individual recognition. For example, a cichlid fish and a damselfish will use individual-specific color patterns that develop only in the facial area. However, it remains to be determined whether the facial area is an especially favorable site for visual signals in fish, and if so why? The monogamous discus fish, Symphysopdon aequifasciatus (Cichlidae), is capable of visually distinguishing its pair-partner from other conspecifics. Discus fish have individual-specific coloration patterns on entire body including the facial area, frontal head, trunk and vertical fins. If the facial area is an inherently important site for the visual cues, this species will use facial patterns for individual recognition, but otherwise they will use patterns on other body parts as well. We used modified digital models to examine whether discus fish use only facial coloration for individual recognition. Digital models of four different combinations of familiar and unfamiliar fish faces and bodies were displayed in frontal and lateral views. Focal fish frequently performed partner-specific displays towards partner-face models, and did aggressive displays towards models of non-partner's faces. We conclude that to identify individuals this fish does not depend on frontal color patterns but does on lateral facial color patterns, although they have unique color patterns on the other parts of body. We discuss the significance of facial coloration for individual recognition in fish compared with birds and mammals.

  12. Enhancement Of Reading Accuracy By Multiple Data Integration

    NASA Astrophysics Data System (ADS)

    Lee, Kangsuk

    1989-07-01

    In this paper, a multiple sensor integration technique with neural network learning algorithms is presented which can enhance the reading accuracy of the hand-written numerals. Many document reading applications involve hand-written numerals in a predetermined location on a form, and in many cases, critical data is redundantly described. The amount of a personal check is one such case which is written redundantly in numerals and in alphabetical form. Information from two optical character recognition modules, one specialized for digits and one for words, is combined to yield an enhanced recognition of the amount. The combination can be accomplished by a decision tree with "if-then" rules, but by simply fusing two or more sets of sensor data in a single expanded neural net, the same functionality can be expected with a much reduced system cost. Experimental results of fusing two neural nets to enhance overall recognition performance using a controlled data set are presented.

  13. Word Spotting and Recognition with Embedded Attributes.

    PubMed

    Almazán, Jon; Gordo, Albert; Fornés, Alicia; Valveny, Ernest

    2014-12-01

    This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.

  14. Group discriminatory power of handwritten characters

    NASA Astrophysics Data System (ADS)

    Tomai, Catalin I.; Kshirsagar, Devika M.; Srihari, Sargur N.

    2003-12-01

    Using handwritten characters we address two questions (i) what is the group identification performance of different alphabets (upper and lower case) and (ii) what are the best characters for the verification task (same writer/different writer discrimination) knowing demographic information about the writer such as ethnicity, age or sex. The Bhattacharya distance is used to rank different characters by their group discriminatory power and the k-nn classifier to measure the individual performance of characters for group identification. Given the tasks of identifying the correct gender/age/ethnicity or handedness, the accumulated performance of characters varies between 65% and 85%.

  15. Marker Registration Technique for Handwritten Text Marker in Augmented Reality Applications

    NASA Astrophysics Data System (ADS)

    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.

  16. Analysis of line structure in handwritten documents using the Hough transform

    NASA Astrophysics Data System (ADS)

    Ball, Gregory R.; Kasiviswanathan, Harish; Srihari, Sargur N.; Narayanan, Aswin

    2010-01-01

    In the analysis of handwriting in documents a central task is that of determining line structure of the text, e.g., number of text lines, location of their starting and end-points, line-width, etc. While simple methods can handle ideal images, real world documents have complexities such as overlapping line structure, variable line spacing, line skew, document skew, noisy or degraded images etc. This paper explores the application of the Hough transform method to handwritten documents with the goal of automatically determining global document line structure in a top-down manner which can then be used in conjunction with a bottom-up method such as connected component analysis. The performance is significantly better than other top-down methods, such as the projection profile method. In addition, we evaluate the performance of skew analysis by the Hough transform on handwritten documents.

  17. Signature Verification Using N-tuple Learning Machine.

    PubMed

    Maneechot, Thanin; Kitjaidure, Yuttana

    2005-01-01

    This research presents new algorithm for signature verification using N-tuple learning machine. The features are taken from handwritten signature on Digital Tablet (On-line). This research develops recognition algorithm using four features extraction, namely horizontal and vertical pen tip position(x-y position), pen tip pressure, and pen altitude angles. Verification uses N-tuple technique with Gaussian thresholding.

  18. Facial Recognition in a Discus Fish (Cichlidae): Experimental Approach Using Digital Models

    PubMed Central

    Satoh, Shun; Tanaka, Hirokazu; Kohda, Masanori

    2016-01-01

    A number of mammals and birds are known to be capable of visually discriminating between familiar and unfamiliar individuals, depending on facial patterns in some species. Many fish also visually recognize other conspecifics individually, and previous studies report that facial color patterns can be an initial signal for individual recognition. For example, a cichlid fish and a damselfish will use individual-specific color patterns that develop only in the facial area. However, it remains to be determined whether the facial area is an especially favorable site for visual signals in fish, and if so why? The monogamous discus fish, Symphysopdon aequifasciatus (Cichlidae), is capable of visually distinguishing its pair-partner from other conspecifics. Discus fish have individual-specific coloration patterns on entire body including the facial area, frontal head, trunk and vertical fins. If the facial area is an inherently important site for the visual cues, this species will use facial patterns for individual recognition, but otherwise they will use patterns on other body parts as well. We used modified digital models to examine whether discus fish use only facial coloration for individual recognition. Digital models of four different combinations of familiar and unfamiliar fish faces and bodies were displayed in frontal and lateral views. Focal fish frequently performed partner-specific displays towards partner-face models, and did aggressive displays towards models of non-partner’s faces. We conclude that to identify individuals this fish does not depend on frontal color patterns but does on lateral facial color patterns, although they have unique color patterns on the other parts of body. We discuss the significance of facial coloration for individual recognition in fish compared with birds and mammals. PMID:27191162

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

    PubMed

    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.

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

    PubMed

    Crukley, Jeffery; Scollie, Susan D

    2014-03-01

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

  1. Completion of hand-written surgical consent forms is frequently suboptimal and could be improved by using electronically generated, procedure-specific forms.

    PubMed

    St John, E R; Scott, A J; Irvine, T E; Pakzad, F; Leff, D R; Layer, G T

    2017-08-01

    Completion of hand-written consent forms for surgical procedures may suffer from missing or inaccurate information, poor legibility and high variability. We audited the completion of hand-written consent forms and trialled a web-based application to generate modifiable, procedure-specific consent forms. The investigation comprised two phases at separate UK hospitals. In phase one, the completion of individual responses in hand-written consent forms for a variety of procedures were prospectively audited. Responses were categorised into three domains (patient details, procedure details and patient sign-off) that were considered "failed" if a contained element was not correct and legible. Phase two was confined to a breast surgical unit where hand-written consent forms were assessed as for phase one and interrogated for missing complications by two independent experts. An electronic consent platform was introduced and electronically-produced consent forms assessed. In phase one, 99 hand-written consent forms were assessed and the domain failure rates were: patient details 10%; procedure details 30%; and patient sign-off 27%. Laparoscopic cholecystectomy was the most common procedure (7/99) but there was significant variability in the documentation of complications: 12 in total, a median of 6 and a range of 2-9. In phase two, 44% (27/61) of hand-written forms were missing essential complications. There were no domain failures amongst 29 electronically-produced consent forms and no variability in the documentation of potential complications. Completion of hand-written consent forms suffers from wide variation and is frequently suboptimal. Electronically-produced, procedure-specific consent forms can improve the quality and consistency of consent documentation. Copyright © 2015 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.

  2. Comparison of speech recognition with adaptive digital and FM remote microphone hearing assistance technology by listeners who use hearing aids.

    PubMed

    Thibodeau, Linda

    2014-06-01

    The purpose of this study was to compare the benefits of 3 types of remote microphone hearing assistance technology (HAT), adaptive digital broadband, adaptive frequency modulation (FM), and fixed FM, through objective and subjective measures of speech recognition in clinical and real-world settings. Participants included 11 adults, ages 16 to 78 years, with primarily moderate-to-severe bilateral hearing impairment (HI), who wore binaural behind-the-ear hearing aids; and 15 adults, ages 18 to 30 years, with normal hearing. Sentence recognition in quiet and in noise and subjective ratings were obtained in 3 conditions of wireless signal processing. Performance by the listeners with HI when using the adaptive digital technology was significantly better than that obtained with the FM technology, with the greatest benefits at the highest noise levels. The majority of listeners also preferred the digital technology when listening in a real-world noisy environment. The wireless technology allowed persons with HI to surpass persons with normal hearing in speech recognition in noise, with the greatest benefit occurring with adaptive digital technology. The use of adaptive digital technology combined with speechreading cues would allow persons with HI to engage in communication in environments that would have otherwise not been possible with traditional wireless technology.

  3. Artificial neural networks for document analysis and recognition.

    PubMed

    Marinai, Simone; Gori, Marco; Soda, Giovanni; Society, Computer

    2005-01-01

    Artificial neural networks have been extensively applied to document analysis and recognition. Most efforts have been devoted to the recognition of isolated handwritten and printed characters with widely recognized successful results. However, many other document processing tasks, like preprocessing, layout analysis, character segmentation, word recognition, and signature verification, have been effectively faced with very promising results. This paper surveys the most significant problems in the area of offline document image processing, where connectionist-based approaches have been applied. Similarities and differences between approaches belonging to different categories are discussed. A particular emphasis is given on the crucial role of prior knowledge for the conception of both appropriate architectures and learning algorithms. Finally, the paper provides a critical analysis on the reviewed approaches and depicts the most promising research guidelines in the field. In particular, a second generation of connectionist-based models are foreseen which are based on appropriate graphical representations of the learning environment.

  4. A paper form processing system with an error correcting function for reading handwritten Kanji strings

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Katsumi Marukawa; Kazuki Nakashima; Masashi Koga

    1994-12-31

    This paper presents a paper form processing system with an error correcting function for reading handwritten kanji strings. In the paper form processing system, names and addresses are important key data, and especially this paper takes up an error correcting method for name and address recognition. The method automatically corrects errors of the kanji OCR (Optical Character Reader) with the help of word dictionaries and other knowledge. Moreover, it allows names and addresses to be written in any style. The method consists of word matching {open_quotes}furigana{close_quotes} verification for name strings, and address approval for address strings. For word matching, kanjimore » name candidates are extracted by automaton-type word matching. In {open_quotes}furigana{close_quotes} verification, kana candidate characters recognized by the kana OCR are compared with kana`s searched from the name dictionary based on kanji name candidates, given by the word matching. The correct name is selected from the results of word matching and furigana verification. Also, the address approval efficiently searches for the right address based on a bottom-up procedure which follows hierarchical relations from a lower placename to a upper one by using the positional condition among the placenames. We ascertained that the error correcting method substantially improves the recognition rate and processing speed in experiments on 5,032 forms.« less

  5. Optical and digital pattern recognition; Proceedings of the Meeting, Los Angeles, CA, Jan. 13-15, 1987

    NASA Technical Reports Server (NTRS)

    Liu, Hua-Kuang (Editor); Schenker, Paul (Editor)

    1987-01-01

    The papers presented in this volume provide an overview of current research in both optical and digital pattern recognition, with a theme of identifying overlapping research problems and methodologies. Topics discussed include image analysis and low-level vision, optical system design, object analysis and recognition, real-time hybrid architectures and algorithms, high-level image understanding, and optical matched filter design. Papers are presented on synthetic estimation filters for a control system; white-light correlator character recognition; optical AI architectures for intelligent sensors; interpreting aerial photographs by segmentation and search; and optical information processing using a new photopolymer.

  6. Text-line extraction in handwritten Chinese documents based on an energy minimization framework.

    PubMed

    Koo, Hyung Il; Cho, Nam Ik

    2012-03-01

    Text-line extraction in unconstrained handwritten documents remains a challenging problem due to nonuniform character scale, spatially varying text orientation, and the interference between text lines. In order to address these problems, we propose a new cost function that considers the interactions between text lines and the curvilinearity of each text line. Precisely, we achieve this goal by introducing normalized measures for them, which are based on an estimated line spacing. We also present an optimization method that exploits the properties of our cost function. Experimental results on a database consisting of 853 handwritten Chinese document images have shown that our method achieves a detection rate of 99.52% and an error rate of 0.32%, which outperforms conventional methods.

  7. Textual blocks rectification method based on fast Hough transform analysis in identity documents recognition

    NASA Astrophysics Data System (ADS)

    Bezmaternykh, P. V.; Nikolaev, D. P.; Arlazarov, V. L.

    2018-04-01

    Textual blocks rectification or slant correction is an important stage of document image processing in OCR systems. This paper considers existing methods and introduces an approach for the construction of such algorithms based on Fast Hough Transform analysis. A quality measurement technique is proposed and obtained results are shown for both printed and handwritten textual blocks processing as a part of an industrial system of identity documents recognition on mobile devices.

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

    PubMed

    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.

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

    PubMed Central

    Bird, Steven B; Lane, David R

    2006-01-01

    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 completely, compared to

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

    NASA Technical Reports Server (NTRS)

    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.

  11. Aspects of quality insurance in digitizing historical climate data in Germany

    NASA Astrophysics Data System (ADS)

    Mächel, H.; Behrends, J.; Kapala, A.

    2010-09-01

    This contribution presents some of the problems and offers solutions regarding the digitization of historical meteorological data, and explains the need for verification and quality control. For the assessment of changes in climate extremes, long-term and complete observational records with a high temporal resolution are needed. However, in most countries, including Germany, such climate data are rare. Therefore, in 2005, the German Weather Service launched a project to inventory and digitize historical daily climatic records in cooperation with the Meteorological Institute of the University of Bonn. Experience with Optical Character Recognition (OCR) show that it is only of very limited use, as even printed tables (e.g. yearbooks) are not sufficiently recognized (10-20% error). In hand-written records, the recognition rate is about 50%. By comparing daily and monthly values, it is possible to auto-detect errors, but they can not be automatically corrected, since there is often more than one error per month. These erroneous data must then be controlled manually on an individual basis, which is significantly more error-prone than direct manual input. Therefore, both precipitation and climate station data are digitized manually. The time required to digitize one year of precipitation data (including the recording of daily precipitation amount and type, snow amount and type, and weather events such as thunder storms, fog, etc.) is equivalent to about five hours for one year of data. This involves manually typing, reformatting and quality control of the digitized data, as well as creating a digital photograph. For climate stations with three observations per day, the working time is 30-50 hours for one year of data, depending on the number of parameters and the condition of the documents. Several other problems occur when creating the digital records from historical observational data, some of which are listed below. Older records often used varying units and different

  12. Shape analysis modeling for character recognition

    NASA Astrophysics Data System (ADS)

    Khan, Nadeem A. M.; Hegt, Hans A.

    1998-10-01

    Optimal shape modeling of character-classes is crucial for achieving high performance on recognition of mixed-font, hand-written or (and) poor quality text. A novel scheme is presented in this regard focusing on constructing such structural models that can be hierarchically examined. These models utilize a certain `well-thought' set of shape primitives. They are simplified enough to ignore the inter- class variations in font-type or writing style yet retaining enough details for discrimination between the samples of the similar classes. Thus the number of models per class required can be kept minimal without sacrificing the recognition accuracy. In this connection a flexible multi- stage matching scheme exploiting the proposed modeling is also described. This leads to a system which is robust against various distortions and degradation including those related to cases of touching and broken characters. Finally, we present some examples and test results as a proof-of- concept demonstrating the validity and the robustness of the approach.

  13. Word spotting for handwritten documents using Chamfer Distance and Dynamic Time Warping

    NASA Astrophysics Data System (ADS)

    Saabni, Raid M.; El-Sana, Jihad A.

    2011-01-01

    A large amount of handwritten historical documents are located in libraries around the world. The desire to access, search, and explore these documents paves the way for a new age of knowledge sharing and promotes collaboration and understanding between human societies. Currently, the indexes for these documents are generated manually, which is very tedious and time consuming. Results produced by state of the art techniques, for converting complete images of handwritten documents into textual representations, are not yet sufficient. Therefore, word-spotting methods have been developed to archive and index images of handwritten documents in order to enable efficient searching within documents. In this paper, we present a new matching algorithm to be used in word-spotting tasks for historical Arabic documents. We present a novel algorithm based on the Chamfer Distance to compute the similarity between shapes of word-parts. Matching results are used to cluster images of Arabic word-parts into different classes using the Nearest Neighbor rule. To compute the distance between two word-part images, the algorithm subdivides each image into equal-sized slices (windows). A modified version of the Chamfer Distance, incorporating geometric gradient features and distance transform data, is used as a similarity distance between the different slices. Finally, the Dynamic Time Warping (DTW) algorithm is used to measure the distance between two images of word-parts. By using the DTW we enabled our system to cluster similar word-parts, even though they are transformed non-linearly due to the nature of handwriting. We tested our implementation of the presented methods using various documents in different writing styles, taken from Juma'a Al Majid Center - Dubai, and obtained encouraging results.

  14. Dealing with contaminated datasets: An approach to classifier training

    NASA Astrophysics Data System (ADS)

    Homenda, Wladyslaw; Jastrzebska, Agnieszka; Rybnik, Mariusz

    2016-06-01

    The paper presents a novel approach to classification reinforced with rejection mechanism. The method is based on a two-tier set of classifiers. First layer classifies elements, second layer separates native elements from foreign ones in each distinguished class. The key novelty presented here is rejection mechanism training scheme according to the philosophy "one-against-all-other-classes". Proposed method was tested in an empirical study of handwritten digits recognition.

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

    ERIC Educational Resources Information Center

    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…

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

    PubMed

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

    2018-05-01

    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.

  17. Learning in Stochastic Bit Stream Neural Networks.

    PubMed

    van Daalen, Max; Shawe-Taylor, John; Zhao, Jieyu

    1996-08-01

    This paper presents learning techniques for a novel feedforward stochastic neural network. The model uses stochastic weights and the "bit stream" data representation. It has a clean analysable functionality and is very attractive with its great potential to be implemented in hardware using standard digital VLSI technology. The design allows simulation at three different levels and learning techniques are described for each level. The lowest level corresponds to on-chip learning. Simulation results on three benchmark MONK's problems and handwritten digit recognition with a clean set of 500 16 x 16 pixel digits demonstrate that the new model is powerful enough for the real world applications. Copyright 1996 Elsevier Science Ltd

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

    NASA Astrophysics Data System (ADS)

    Yoshikawa, Takatoshi; Okamoto, Masayosi; Horii, Hiroshi

    1993-04-01

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

  19. Line Segmentation in Handwritten Assamese and Meetei Mayek Script Using Seam Carving Based Algorithm

    NASA Astrophysics Data System (ADS)

    Kumar, Chandan Jyoti; Kalita, Sanjib Kr.

    Line segmentation is a key stage in an Optical Character Recognition system. This paper primarily concerns the problem of text line extraction on color and grayscale manuscript pages of two major North-east Indian regional Scripts, Assamese and Meetei Mayek. Line segmentation of handwritten text in Assamese and Meetei Mayek scripts is an uphill task primarily because of the structural features of both the scripts and varied writing styles. Line segmentation of a document image is been achieved by using the Seam carving technique, in this paper. Researchers from various regions used this approach for content aware resizing of an image. However currently many researchers are implementing Seam Carving for line segmentation phase of OCR. Although it is a language independent technique, mostly experiments are done over Arabic, Greek, German and Chinese scripts. Two types of seams are generated, medial seams approximate the orientation of each text line, and separating seams separated one line of text from another. Experiments are performed extensively over various types of documents and detailed analysis of the evaluations reflects that the algorithm performs well for even documents with multiple scripts. In this paper, we present a comparative study of accuracy of this method over different types of data.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

  2. Progressive sparse representation-based classification using local discrete cosine transform evaluation for image recognition

    NASA Astrophysics Data System (ADS)

    Song, Xiaoning; Feng, Zhen-Hua; Hu, Guosheng; Yang, Xibei; Yang, Jingyu; Qi, Yunsong

    2015-09-01

    This paper proposes a progressive sparse representation-based classification algorithm using local discrete cosine transform (DCT) evaluation to perform face recognition. Specifically, the sum of the contributions of all training samples of each subject is first taken as the contribution of this subject, then the redundant subject with the smallest contribution to the test sample is iteratively eliminated. Second, the progressive method aims at representing the test sample as a linear combination of all the remaining training samples, by which the representation capability of each training sample is exploited to determine the optimal "nearest neighbors" for the test sample. Third, the transformed DCT evaluation is constructed to measure the similarity between the test sample and each local training sample using cosine distance metrics in the DCT domain. The final goal of the proposed method is to determine an optimal weighted sum of nearest neighbors that are obtained under the local correlative degree evaluation, which is approximately equal to the test sample, and we can use this weighted linear combination to perform robust classification. Experimental results conducted on the ORL database of faces (created by the Olivetti Research Laboratory in Cambridge), the FERET face database (managed by the Defense Advanced Research Projects Agency and the National Institute of Standards and Technology), AR face database (created by Aleix Martinez and Robert Benavente in the Computer Vision Center at U.A.B), and USPS handwritten digit database (gathered at the Center of Excellence in Document Analysis and Recognition at SUNY Buffalo) demonstrate the effectiveness of the proposed method.

  3. Numerical linear algebra in data mining

    NASA Astrophysics Data System (ADS)

    Eldén, Lars

    Ideas and algorithms from numerical linear algebra are important in several areas of data mining. We give an overview of linear algebra methods in text mining (information retrieval), pattern recognition (classification of handwritten digits), and PageRank computations for web search engines. The emphasis is on rank reduction as a method of extracting information from a data matrix, low-rank approximation of matrices using the singular value decomposition and clustering, and on eigenvalue methods for network analysis.

  4. A comparison of the surface contaminants of handwritten recycled and printed electronic parenteral nutrition prescriptions and their transfer to bag surfaces during delivery to hospital wards.

    PubMed

    Austin, Peter David; Hand, Kieran Sean; Elia, Marinos

    2014-02-01

    Handwritten recycled paper prescription for parenteral nutrition (PN) may become a concentrated source of viable contaminants, including pathogens. This study examined the effect of using fresh printouts of electronic prescriptions on these contaminants. Cellulose sponge stick swabs with neutralizing buffer were used to sample the surfaces of PN prescriptions (n = 32 handwritten recycled; n = 32 printed electronic) on arrival to the pharmacy or following printing and PN prescriptions and bags packaged together during delivery (n = 38 handwritten recycled; n = 34 printed electronic) on arrival to hospital wards. Different media plates and standard microbiological procedures identified the type and number of contaminants. Staphylococcus aureus, fungi, and mold were infrequent contaminants. nonspecific aerobes more frequently contaminated handwritten recycled than printed electronic prescriptions (into pharmacy, 94% vs 44%, fisher exact test P .001; onto wards, 76% vs 50%, p = .028), with greater numbers of colony-forming units (CFU) (into pharmacy, median 130 [interquartile range (IQR), 65260] VS 0 [075], Mann-Whitney U test, P .001; onto wards, median 120 [15320] vs 10 [040], P = .001). packaging with handwritten recycled prescriptions led to more frequent nonspecific aerobic bag surface contamination (63% vs 41%, fisher exact test P = .097), with greater numbers of CFU (median 40 [IQR, 080] VS 0 [040], Mann-Whitney U test, P = .036). The use of printed electronic PN prescriptions can reduce microbial loads for contamination of surfaces that compromises aseptic techniques.

  5. A hybrid intelligence approach to artifact recognition in digital publishing

    NASA Astrophysics Data System (ADS)

    Vega-Riveros, J. Fernando; Santos Villalobos, Hector J.

    2006-02-01

    The system presented integrates rule-based and case-based reasoning for artifact recognition in Digital Publishing. In Variable Data Printing (VDP) human proofing could result prohibitive since a job could contain millions of different instances that may contain two types of artifacts: 1) evident defects, like a text overflow or overlapping 2) style-dependent artifacts, subtle defects that show as inconsistencies with regard to the original job design. We designed a Knowledge-Based Artifact Recognition tool for document segmentation, layout understanding, artifact detection, and document design quality assessment. Document evaluation is constrained by reference to one instance of the VDP job proofed by a human expert against the remaining instances. Fundamental rules of document design are used in the rule-based component for document segmentation and layout understanding. Ambiguities in the design principles not covered by the rule-based system are analyzed by case-based reasoning, using the Nearest Neighbor Algorithm, where features from previous jobs are used to detect artifacts and inconsistencies within the document layout. We used a subset of XSL-FO and assembled a set of 44 document samples. The system detected all the job layout changes, while obtaining an overall average accuracy of 84.56%, with the highest accuracy of 92.82%, for overlapping and the lowest, 66.7%, for the lack-of-white-space.

  6. Ancient administrative handwritten documents: X-ray analysis and imaging

    PubMed Central

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

    2015-01-01

    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. PMID:25723946

  7. Ancient administrative handwritten documents: X-ray analysis and imaging.

    PubMed

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

    2015-03-01

    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.

  8. Recognition of Handwriting from Electromyography

    PubMed Central

    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

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

    PubMed

    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.

  10. Fuzzy Clustering of Multiple Instance Data

    DTIC Science & Technology

    2015-11-30

    depth is not. To illustrate this data, in figure 1 we display the GPR signatures of the same mine buried at 3 in deep in two geographically different...target signature depends on the soil properties of the site. The same mine type is buried at 3in deep in both sites. Since its formal introduction...drug design [15], and the problem of handwritten digit recognition [16]. To the best of our knowledge, Diet - terich, et. al [1] were the first to

  11. The proximate unit in Chinese handwritten character production

    PubMed Central

    Chen, Jenn-Yeu; Cherng, Rong-Ju

    2013-01-01

    In spoken word production, a proximate unit is the first phonological unit at the sublexical level that is selectable for production (O'Seaghdha et al., 2010). The present study investigated whether the proximate unit in Chinese handwritten character 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 (Experiment 1: 687 vs. 684 ms, Experiment 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 (Experiment 3: 685 vs. 704, Experiment 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., ± in , , , ; called a logographeme). Response times in the homogeneous blocks were significantly faster than those in the heterogeneous blocks (Experiment 5: 576 vs. 625, Experiment 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. PMID:23950752

  12. Learning optimal features for visual pattern recognition

    NASA Astrophysics Data System (ADS)

    Labusch, Kai; Siewert, Udo; Martinetz, Thomas; Barth, Erhardt

    2007-02-01

    The optimal coding hypothesis proposes that the human visual system has adapted to the statistical properties of the environment by the use of relatively simple optimality criteria. We here (i) discuss how the properties of different models of image coding, i.e. sparseness, decorrelation, and statistical independence are related to each other (ii) propose to evaluate the different models by verifiable performance measures (iii) analyse the classification performance on images of handwritten digits (MNIST data base). We first employ the SPARSENET algorithm (Olshausen, 1998) to derive a local filter basis (on 13 × 13 pixels windows). We then filter the images in the database (28 × 28 pixels images of digits) and reduce the dimensionality of the resulting feature space by selecting the locally maximal filter responses. We then train a support vector machine on a training set to classify the digits and report results obtained on a separate test set. Currently, the best state-of-the-art result on the MNIST data base has an error rate of 0,4%. This result, however, has been obtained by using explicit knowledge that is specific to the data (elastic distortion model for digits). We here obtain an error rate of 0,55% which is second best but does not use explicit data specific knowledge. In particular it outperforms by far all methods that do not use data-specific knowledge.

  13. Experiments on Urdu Text Recognition

    NASA Astrophysics Data System (ADS)

    Mukhtar, Omar; Setlur, Srirangaraj; Govindaraju, Venu

    Urdu is a language spoken in the Indian subcontinent by an estimated 130-270 million speakers. At the spoken level, Urdu and Hindi are considered dialects of a single language because of shared vocabulary and the similarity in grammar. At the written level, however, Urdu is much closer to Arabic because it is written in Nastaliq, the calligraphic style of the Persian-Arabic script. Therefore, a speaker of Hindi can understand spoken Urdu but may not be able to read written Urdu because Hindi is written in Devanagari script, whereas an Arabic writer can read the written words but may not understand the spoken Urdu. In this chapter we present an overview of written Urdu. Prior research in handwritten Urdu OCR is very limited. We present (perhaps) the first system for recognizing handwritten Urdu words. On a data set of about 1300 handwritten words, we achieved an accuracy of 70% for the top choice, and 82% for the top three choices.

  14. Unsupervised categorization method of graphemes on handwritten manuscripts: application to style recognition

    NASA Astrophysics Data System (ADS)

    Daher, H.; Gaceb, D.; Eglin, V.; Bres, S.; Vincent, N.

    2012-01-01

    We present in this paper a feature selection and weighting method for medieval handwriting images that relies on codebooks of shapes of small strokes of characters (graphemes that are issued from the decomposition of manuscripts). These codebooks are important to simplify the automation of the analysis, the manuscripts transcription and the recognition of styles or writers. Our approach provides a precise features weighting by genetic algorithms and a highperformance methodology for the categorization of the shapes of graphemes by using graph coloring into codebooks which are applied in turn on CBIR (Content Based Image Retrieval) in a mixed handwriting database containing different pages from different writers, periods of the history and quality. We show how the coupling of these two mechanisms 'features weighting - graphemes classification' can offer a better separation of the forms to be categorized by exploiting their grapho-morphological, their density and their significant orientations particularities.

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

    ERIC Educational Resources Information Center

    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)

  16. Automated recognition and extraction of tabular fields for the indexing of census records

    NASA Astrophysics Data System (ADS)

    Clawson, Robert; Bauer, Kevin; Chidester, Glen; Pohontsch, Milan; Kennard, Douglas; Ryu, Jongha; Barrett, William

    2013-01-01

    We describe a system for indexing of census records in tabular documents with the goal of recognizing the content of each cell, including both headers and handwritten entries. Each document is automatically rectified, registered and scaled to a known template following which lines and fields are detected and delimited as cells in a tabular form. Whole-word or whole-phrase recognition of noisy machine-printed text is performed using a glyph library, providing greatly increased efficiency and accuracy (approaching 100%), while avoiding the problems inherent with traditional OCR approaches. Constrained handwriting recognition results for a single author reach as high as 98% and 94.5% for the Gender field and Birthplace respectively. Multi-author accuracy (currently 82%) can be improved through an increased training set. Active integration of user feedback in the system will accelerate the indexing of records while providing a tightly coupled learning mechanism for system improvement.

  17. Unsupervised learning of digit recognition using spike-timing-dependent plasticity

    PubMed Central

    Diehl, Peter U.; Cook, Matthew

    2015-01-01

    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 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. PMID:26941637

  18. Concurrent evolution of feature extractors and modular artificial neural networks

    NASA Astrophysics Data System (ADS)

    Hannak, Victor; Savakis, Andreas; Yang, Shanchieh Jay; Anderson, Peter

    2009-05-01

    This paper presents a new approach for the design of feature-extracting recognition networks that do not require expert knowledge in the application domain. Feature-Extracting Recognition Networks (FERNs) are composed of interconnected functional nodes (feurons), which serve as feature extractors, and are followed by a subnetwork of traditional neural nodes (neurons) that act as classifiers. A concurrent evolutionary process (CEP) is used to search the space of feature extractors and neural networks in order to obtain an optimal recognition network that simultaneously performs feature extraction and recognition. By constraining the hill-climbing search functionality of the CEP on specific parts of the solution space, i.e., individually limiting the evolution of feature extractors and neural networks, it was demonstrated that concurrent evolution is a necessary component of the system. Application of this approach to a handwritten digit recognition task illustrates that the proposed methodology is capable of producing recognition networks that perform in-line with other methods without the need for expert knowledge in image processing.

  19. Computer-implemented land use classification with pattern recognition software and ERTS digital data. [Mississippi coastal plains

    NASA Technical Reports Server (NTRS)

    Joyce, A. T.

    1974-01-01

    Significant progress has been made in the classification of surface conditions (land uses) with computer-implemented techniques based on the use of ERTS digital data and pattern recognition software. The supervised technique presently used at the NASA Earth Resources Laboratory is based on maximum likelihood ratioing with a digital table look-up approach to classification. After classification, colors are assigned to the various surface conditions (land uses) classified, and the color-coded classification is film recorded on either positive or negative 9 1/2 in. film at the scale desired. Prints of the film strips are then mosaicked and photographed to produce a land use map in the format desired. Computer extraction of statistical information is performed to show the extent of each surface condition (land use) within any given land unit that can be identified in the image. Evaluations of the product indicate that classification accuracy is well within the limits for use by land resource managers and administrators. Classifications performed with digital data acquired during different seasons indicate that the combination of two or more classifications offer even better accuracy.

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

    NASA Astrophysics Data System (ADS)

    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.

  1. From Hahnemann's hand to your computer screen: building a digital homeopathy collection

    PubMed Central

    Mix, Lisa A; Cameron, Kathleen

    2011-01-01

    The University of California, San Francisco (UCSF), Library holds the unique manuscript of the sixth edition of Samuel Hahnemann's Organon der Heilkunst, the primary text of homeopathy. The manuscript volume is Hahnemann's own copy of the fifth edition of the Organon with his notes for the sixth edition, handwritten throughout the volume. There is a high level of interest in the Organon manuscript, particularly among homeopaths. This led to the decision to present a digital surrogate on the web to make it accessible to a wider audience. Digitizing Hahnemann's manuscript and determining the best method of presentation on the web posed several challenges. Lessons learned in the course of this project will inform future digital projects. This article discusses the historical significance of the sixth edition of Hahnemann's Organon, its context in UCSF's homeopathy collections, and the specifics of developing the online homeopathy collection. PMID:21243055

  2. Development of a Digitalized Child's Checkups Information System.

    PubMed

    Ito, Yoshiya; Takimoto, Hidemi

    2017-01-01

    In Japan, health checkups for children take place from infancy through high school and play an important role in the maintenance and control of childhood/adolescent health. The anthropometric data obtained during these checkups are kept in health centers and schools and are also recorded in a mother's maternal and child health handbook, as well as on school health cards. These data are meaningful if they are utilized well and in an appropriate manner. They are particularly useful for the prevention of obesity-related conditions in adulthood, such as metabolic syndrome and diabetes mellitus. For this purpose, we have tried to establish a scanning system with an optical character recognition (OCR) function, which links data obtained during health checkups in infancy with that obtained in schools. In this system, handwritten characters on the records are scanned and processed using OCR. However, because many of the scanned characters are not read properly, we must wait for the improvement in the performance of the OCR function. In addition, we have developed Microsoft Excel spreadsheets, on which obesity-related indices, such as body mass index and relative body weight, are calculated. These sheets also provide functions that tabulate the frequencies of obesity in specific groups. Actively using these data and digitalized systems will not only contribute towards resolving physical health problems in children, but also decrease the risk of developing lifestyle-related diseases in adulthood.

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

    PubMed

    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

  4. Fast Multiclass Segmentation using Diffuse Interface Methods on Graphs

    DTIC Science & Technology

    2013-02-01

    000 28 × 28 images of handwritten digits 0 through 9. Examples of entries can be found in Figure 6. The task is to classify each of the images into the...database of handwritten digits .” [Online]. Available: http://yann.lecun.com/exdb/mnist/ [36] J. Lellmann, J. H. Kappes, J. Yuan, F. Becker, and C...corresponding digit . The images include digits from 0 to 9; thus, this is a 10 class segmentation problem. To construct the weight matrix, we used N

  5. The use of discrete-event simulation modeling to compare handwritten and electronic prescribing systems.

    PubMed

    Ghany, Ahmad; Vassanji, Karim; Kuziemsky, Craig; Keshavjee, Karim

    2013-01-01

    Electronic prescribing (e-prescribing) is expected to bring many benefits to Canadian healthcare, such as a reduction in errors and adverse drug reactions. As there currently is no functioning e-prescribing system in Canada that is completely electronic, we are unable to evaluate the performance of a live system. An alternative approach is to use simulation modeling for evaluation. We developed two discrete-event simulation models, one of the current handwritten prescribing system and one of a proposed e-prescribing system, to compare the performance of these two systems. We were able to compare the number of processes in each model, workflow efficiency, and the distribution of patients or prescriptions. Although we were able to compare these models to each other, using discrete-event simulation software was challenging. We were limited in the number of variables we could measure. We discovered non-linear processes and feedback loops in both models that could not be adequately represented using discrete-event simulation software. Finally, interactions between entities in both models could not be modeled using this type of software. We have come to the conclusion that a more appropriate approach to modeling both the handwritten and electronic prescribing systems would be to use a complex adaptive systems approach using agent-based modeling or systems-based modeling.

  6. Facial Recognition in a Group-Living Cichlid Fish.

    PubMed

    Kohda, Masanori; Jordan, Lyndon Alexander; Hotta, Takashi; Kosaka, Naoya; Karino, Kenji; Tanaka, Hirokazu; Taniyama, Masami; Takeyama, Tomohiro

    2015-01-01

    The theoretical underpinnings of the mechanisms of sociality, e.g. territoriality, hierarchy, and reciprocity, are based on assumptions of individual recognition. While behavioural evidence suggests individual recognition is widespread, the cues that animals use to recognise individuals are established in only a handful of systems. Here, we use digital models to demonstrate that facial features are the visual cue used for individual recognition in the social fish Neolamprologus pulcher. Focal fish were exposed to digital images showing four different combinations of familiar and unfamiliar face and body colorations. Focal fish attended to digital models with unfamiliar faces longer and from a further distance to the model than to models with familiar faces. These results strongly suggest that fish can distinguish individuals accurately using facial colour patterns. Our observations also suggest that fish are able to rapidly (≤ 0.5 sec) discriminate between familiar and unfamiliar individuals, a speed of recognition comparable to primates including humans.

  7. For Those of Us at the Borders: Recognition and Evaluation of Faculty Work in the Academic Field of Film and Digital Media

    ERIC Educational Resources Information Center

    Collins, E. Anthony

    2011-01-01

    Artistic, scholarly, and professional works by individual faculty members in the field of film and digital media are not being adequately recognized or rewarded as scholarship activity during performance evaluation in institutions of higher learning. Conventional systems for the recognition and evaluation of work prioritize scientism and compel…

  8. Introduction of statistical information in a syntactic analyzer for document image recognition

    NASA Astrophysics Data System (ADS)

    Maroneze, André O.; Coüasnon, Bertrand; Lemaitre, Aurélie

    2011-01-01

    This paper presents an improvement to document layout analysis systems, offering a possible solution to Sayre's paradox (which states that an element "must be recognized before it can be segmented; and it must be segmented before it can be recognized"). This improvement, based on stochastic parsing, allows integration of statistical information, obtained from recognizers, during syntactic layout analysis. We present how this fusion of numeric and symbolic information in a feedback loop can be applied to syntactic methods to improve document description expressiveness. To limit combinatorial explosion during exploration of solutions, we devised an operator that allows optional activation of the stochastic parsing mechanism. Our evaluation on 1250 handwritten business letters shows this method allows the improvement of global recognition scores.

  9. Combining approaches to on-line handwriting information retrieval

    NASA Astrophysics Data System (ADS)

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

    2010-01-01

    In this work, we propose to combine two quite different approaches for retrieving handwritten documents. Our hypothesis is that different retrieval algorithms should retrieve different sets of documents for the same query. Therefore, significant improvements in retrieval performances can be expected. The first approach is based on information retrieval techniques carried out on the noisy texts obtained through handwriting recognition, while the second approach is recognition-free using a word spotting algorithm. Results shows that for texts having a word error rate (WER) lower than 23%, the performances obtained with the combined system are close to the performances obtained on clean digital texts. In addition, for poorly recognized texts (WER > 52%), an improvement of nearly 17% can be observed with respect to the best available baseline method.

  10. Character recognition using a neural network model with fuzzy representation

    NASA Technical Reports Server (NTRS)

    Tavakoli, Nassrin; Seniw, David

    1992-01-01

    The degree to which digital images are recognized correctly by computerized algorithms is highly dependent upon the representation and the classification processes. Fuzzy techniques play an important role in both processes. In this paper, the role of fuzzy representation and classification on the recognition of digital characters is investigated. An experimental Neural Network model with application to character recognition was developed. Through a set of experiments, the effect of fuzzy representation on the recognition accuracy of this model is presented.

  11. Eye movement analysis for activity recognition using electrooculography.

    PubMed

    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.

  12. Identification of handwriting by using the genetic algorithm (GA) and support vector machine (SVM)

    NASA Astrophysics Data System (ADS)

    Zhang, Qigui; Deng, Kai

    2016-12-01

    As portable digital camera and a camera phone comes more and more popular, and equally pressing is meeting the requirements of people to shoot at any time, to identify and storage handwritten character. In this paper, genetic algorithm(GA) and support vector machine(SVM)are used for identification of handwriting. Compare with parameters-optimized method, this technique overcomes two defects: first, it's easy to trap in the local optimum; second, finding the best parameters in the larger range will affects the efficiency of classification and prediction. As the experimental results suggest, GA-SVM has a higher recognition rate.

  13. Age and gender-invariant features of handwritten signatures for verification systems

    NASA Astrophysics Data System (ADS)

    AbdAli, Sura; Putz-Leszczynska, Joanna

    2014-11-01

    Handwritten signature is one of the most natural biometrics, the study of human physiological and behavioral patterns. Behavioral biometrics includes signatures that may be different due to its owner gender or age because of intrinsic or extrinsic factors. This paper presents the results of the author's research on age and gender influence on verification factors. The experiments in this research were conducted using a database that contains signatures and their associated metadata. The used algorithm is based on the universal forgery feature idea, where the global classifier is able to classify a signature as a genuine one or, as a forgery, without the actual knowledge of the signature template and its owner. Additionally, the reduction of the dimensionality with the MRMR method is discussed.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2004-12-01

    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.

  16. Hue-saturation-density (HSD) model for stain recognition in digital images from transmitted light microscopy.

    PubMed

    van Der Laak, J A; Pahlplatz, M M; Hanselaar, A G; de Wilde, P C

    2000-04-01

    Transmitted light microscopy is used in pathology to examine stained tissues. Digital image analysis is gaining importance as a means to quantify alterations in tissues. A prerequisite for accurate and reproducible quantification is the possibility to recognise stains in a standardised manner, independently of variations in the staining density. The usefulness of three colour models was studied using data from computer simulations and experimental data from an immuno-doublestained tissue section. Direct use of the three intensities obtained by a colour camera results in the red-green-blue (RGB) model. By decoupling the intensity from the RGB data, the hue-saturation-intensity (HSI) model is obtained. However, the major part of the variation in perceived intensities in transmitted light microscopy is caused by variations in staining density. Therefore, the hue-saturation-density (HSD) transform was defined as the RGB to HSI transform, applied to optical density values rather than intensities for the individual RGB channels. In the RGB model, the mixture of chromatic and intensity information hampers standardisation of stain recognition. In the HSI model, mixtures of stains that could be distinguished from other stains in the RGB model could not be separated. The HSD model enabled all possible distinctions in a two-dimensional, standardised data space. In the RGB model, standardised recognition is only possible by using complex and time-consuming algorithms. The HSI model is not suitable for stain recognition in transmitted light microscopy. The newly derived HSD model was found superior to the existing models for this purpose. Copyright 2000 Wiley-Liss, Inc.

  17. THE RELIABILITY OF HAND-WRITTEN AND COMPUTERISED RECORDS OF BIRTH DATA COLLECTED AT BARAGWANATH HOSPITAL IN SOWETO

    PubMed Central

    Ellison, GTH; Richter, LM; de Wet, T; Harris, HE; Griesel, RD; McIntyre, JA

    2007-01-01

    This study examined the reliability of hand-written and computerised records of birth data collected during the Birth to Ten study at Baragwanath Hospital in Soweto. The reliability of record-keeping in hand-written obstetric and neonatal files was assessed by comparing duplicate records of six different variables abstracted from six different sections in these files. The reliability of computerised record-keeping was assessed by comparing the original hand-written record of each variable with records contained in the hospital’s computerised database. These data sets displayed similar levels of reliability which suggests that similar errors occurred when data were transcribed from one section of the files to the next, and from these files to the computerised database. In both sets of records reliability was highest for the categorical variable infant sex, and for those continuous variables (such as maternal age and gravidity) recorded with unambiguous units. Reliability was lower for continuous variables that could be recorded with different levels of precision (such as birth weight), those that were occasionally measured more than once, and those that could be measured using more than one measurement technique (such as gestational age). Reducing the number of times records are transcribed, categorising continuous variables, and standardising the techniques used for measuring and recording variables would improve the reliability of both hand-written and computerised data sets. OPSOMMING In hierdie studie is die betroubaarheid van handgeskrewe en gerekenariseerde rekords van ge boortedata ondersoek, wat versamel is gedurende die ‘Birth to Ten’ -studie aan die Baragwanath hospitaal in Soweto. Die betroubaarheid van handgeskrewe verloskundige en pasgeboortelike rekords is beoordeel deur duplikaatrekords op ses verskillende verander likes te vergelyk, wat onttrek is uit ses verskillende dele van die betrokke lêers. Die gerekenariseerde rekords se betroubaarheid

  18. Supporting Learning with Weblogs in Science Education: A Comparison of Blogging and Hand-Written Reflective Writing with and without Prompts

    ERIC Educational Resources Information Center

    Petko, Dominik; Egger, Nives; Graber, Marc

    2014-01-01

    The goal of this study was to compare how weblogs and traditional handwritten reflective learning protocols compare regarding the use of cognitive and metacognitive strategies for knowledge acquisition as well as learning gains in secondary school students. The study used a quasi-experimental control group design with repeated measurements…

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

    PubMed

    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.

  20. Usage of the back-propagation method for alphabet recognition

    NASA Astrophysics Data System (ADS)

    Shaila Sree, R. N.; Eswaran, Kumar; Sundararajan, N.

    1999-03-01

    Artificial Neural Networks play a pivotal role in the branch of Artificial Intelligence. They can be trained efficiently for a variety of tasks using different methods, of which the Back Propagation method is one among them. The paper studies the choosing of various design parameters of a neural network for the Back Propagation method. The study shows that when these parameters are properly assigned, the training task of the net is greatly simplified. The character recognition problem has been chosen as a test case for this study. A sample space of different handwritten characters of the English alphabet was gathered. A Neural net is finally designed taking many the design aspects into consideration and trained for different styles of writing. Experimental results are reported and discussed. It has been found that an appropriate choice of the design parameters of the neural net for the Back Propagation method reduces the training time and improves the performance of the net.

  1. Rapid Naming Speed and Chinese Character Recognition

    ERIC Educational Resources Information Center

    Liao, Chen-Huei; Georgiou, George K.; Parrila, Rauno

    2008-01-01

    We examined the relationship between rapid naming speed (RAN) and Chinese character recognition accuracy and fluency. Sixty-three grade 2 and 54 grade 4 Taiwanese children were administered four RAN tasks (colors, digits, Zhu-Yin-Fu-Hao, characters), and two character recognition tasks. RAN tasks accounted for more reading variance in grade 4 than…

  2. Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers

    PubMed Central

    Ospina, Raydonal; Frery, Alejandro C.

    2016-01-01

    We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results are better than state-of-the-art online techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups. PMID:27907014

  3. Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers.

    PubMed

    Rosso, Osvaldo A; Ospina, Raydonal; Frery, Alejandro C

    2016-01-01

    We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results are better than state-of-the-art online techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.

  4. Multi-font printed Mongolian document recognition system

    NASA Astrophysics Data System (ADS)

    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.

  5. Deformation-Aware Log-Linear Models

    NASA Astrophysics Data System (ADS)

    Gass, Tobias; Deselaers, Thomas; Ney, Hermann

    In this paper, we present a novel deformation-aware discriminative model for handwritten digit recognition. Unlike previous approaches our model directly considers image deformations and allows discriminative training of all parameters, including those accounting for non-linear transformations of the image. This is achieved by extending a log-linear framework to incorporate a latent deformation variable. The resulting model has an order of magnitude less parameters than competing approaches to handling image deformations. We tune and evaluate our approach on the USPS task and show its generalization capabilities by applying the tuned model to the MNIST task. We gain interesting insights and achieve highly competitive results on both tasks.

  6. Terrain type recognition using ERTS-1 MSS images

    NASA Technical Reports Server (NTRS)

    Gramenopoulos, N.

    1973-01-01

    For the automatic recognition of earth resources from ERTS-1 digital tapes, both multispectral and spatial pattern recognition techniques are important. Recognition of terrain types is based on spatial signatures that become evident by processing small portions of an image through selected algorithms. An investigation of spatial signatures that are applicable to ERTS-1 MSS images is described. Artifacts in the spatial signatures seem to be related to the multispectral scanner. A method for suppressing such artifacts is presented. Finally, results of terrain type recognition for one ERTS-1 image are presented.

  7. Military applications of automatic speech recognition and future requirements

    NASA Technical Reports Server (NTRS)

    Beek, Bruno; Cupples, Edward J.

    1977-01-01

    An updated summary of the state-of-the-art of automatic speech recognition and its relevance to military applications is provided. A number of potential systems for military applications are under development. These include: (1) digital narrowband communication systems; (2) automatic speech verification; (3) on-line cartographic processing unit; (4) word recognition for militarized tactical data system; and (5) voice recognition and synthesis for aircraft cockpit.

  8. Analysis and Recognition of Curve Type as The Basis of Object Recognition in Image

    NASA Astrophysics Data System (ADS)

    Nugraha, Nurma; Madenda, Sarifuddin; Indarti, Dina; Dewi Agushinta, R.; Ernastuti

    2016-06-01

    An object in an image when analyzed further will show the characteristics that distinguish one object with another object in an image. Characteristics that are used in object recognition in an image can be a color, shape, pattern, texture and spatial information that can be used to represent objects in the digital image. The method has recently been developed for image feature extraction on objects that share characteristics curve analysis (simple curve) and use the search feature of chain code object. This study will develop an algorithm analysis and the recognition of the type of curve as the basis for object recognition in images, with proposing addition of complex curve characteristics with maximum four branches that will be used for the process of object recognition in images. Definition of complex curve is the curve that has a point of intersection. By using some of the image of the edge detection, the algorithm was able to do the analysis and recognition of complex curve shape well.

  9. Mexican sign language recognition using normalized moments and artificial neural networks

    NASA Astrophysics Data System (ADS)

    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.

  10. Fuzzy Logic-Based Audio Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Malcangi, M.

    2008-11-01

    Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

  11. Degraded character recognition based on gradient pattern

    NASA Astrophysics Data System (ADS)

    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.

  12. Word recognition materials for native speakers of Taiwan Mandarin.

    PubMed

    Nissen, Shawn L; Harris, Richard W; Dukes, Alycia

    2008-06-01

    To select, digitally record, evaluate, and psychometrically equate word recognition materials that can be used to measure the speech perception abilities of native speakers of Taiwan Mandarin in quiet. Frequently used bisyllabic words produced by male and female talkers of Taiwan Mandarin were digitally recorded and subsequently evaluated using 20 native listeners with normal hearing at 10 intensity levels (-5 to 40 dB HL) in increments of 5 dB. Using logistic regression, 200 words with the steepest psychometric slopes were divided into 4 lists and 8 half-lists that were relatively equivalent in psychometric function slope. To increase auditory homogeneity of the lists, the intensity of words in each list was digitally adjusted so that the threshold of each list was equal to the midpoint between the mean thresholds of the male and female half-lists. Digital recordings of the word recognition lists and the associated clinical instructions are available on CD upon request.

  13. A Limited-Vocabulary, Multi-Speaker Automatic Isolated Word Recognition System.

    ERIC Educational Resources Information Center

    Paul, James E., Jr.

    Techniques for automatic recognition of isolated words are investigated, and a computer simulation of a word recognition system is effected. Considered in detail are data acquisition and digitizing, word detection, amplitude and time normalization, short-time spectral estimation including spectral windowing, spectral envelope approximation,…

  14. Context-dependent similarity effects in letter recognition.

    PubMed

    Kinoshita, Sachiko; Robidoux, Serje; Guilbert, Daniel; Norris, Dennis

    2015-10-01

    In visual word recognition tasks, digit primes that are visually similar to letter string targets (e.g., 4/A, 8/B) are known to facilitate letter identification relative to visually dissimilar digits (e.g., 6/A, 7/B); in contrast, with letter primes, visual similarity effects have been elusive. In the present study we show that the visual similarity effect with letter primes can be made to come and go, depending on whether it is necessary to discriminate between visually similar letters. The results support a Bayesian view which regards letter recognition not as a passive activation process driven by the fixed stimulus properties, but as a dynamic evidence accumulation process for a decision that is guided by the task context.

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

    NASA Astrophysics Data System (ADS)

    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

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

    PubMed

    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.

  17. Recognition and Localization of Speech by Adult Cochlear Implant Recipients Wearing a Digital Hearing Aid in the Nonimplanted Ear (Bimodal Hearing)

    PubMed Central

    Potts, Lisa G.; Skinner, Margaret W.; Litovsky, Ruth A.; Strube, Michael J; Kuk, Francis

    2010-01-01

    Background The use of bilateral amplification is now common clinical practice for hearing aid users but not for cochlear implant recipients. In the past, most cochlear implant recipients were implanted in one ear and wore only a monaural cochlear implant processor. There has been recent interest in benefits arising from bilateral stimulation that may be present for cochlear implant recipients. One option for bilateral stimulation is the use of a cochlear implant in one ear and a hearing aid in the opposite nonimplanted ear (bimodal hearing). Purpose This study evaluated the effect of wearing a cochlear implant in one ear and a digital hearing aid in the opposite ear on speech recognition and localization. Research Design A repeated-measures correlational study was completed. Study Sample Nineteen adult Cochlear Nucleus 24 implant recipients participated in the study. Intervention The participants were fit with a Widex Senso Vita 38 hearing aid to achieve maximum audibility and comfort within their dynamic range. Data Collection and Analysis Soundfield thresholds, loudness growth, speech recognition, localization, and subjective questionnaires were obtained six–eight weeks after the hearing aid fitting. Testing was completed in three conditions: hearing aid only, cochlear implant only, and cochlear implant and hearing aid (bimodal). All tests were repeated four weeks after the first test session. Repeated-measures analysis of variance was used to analyze the data. Significant effects were further examined using pairwise comparison of means or in the case of continuous moderators, regression analyses. The speech-recognition and localization tasks were unique, in that a speech stimulus presented from a variety of roaming azimuths (140 degree loudspeaker array) was used. Results Performance in the bimodal condition was significantly better for speech recognition and localization compared to the cochlear implant–only and hearing aid–only conditions. Performance

  18. Recognition and localization of speech by adult cochlear implant recipients wearing a digital hearing aid in the nonimplanted ear (bimodal hearing).

    PubMed

    Potts, Lisa G; Skinner, Margaret W; Litovsky, Ruth A; Strube, Michael J; Kuk, Francis

    2009-06-01

    The use of bilateral amplification is now common clinical practice for hearing aid users but not for cochlear implant recipients. In the past, most cochlear implant recipients were implanted in one ear and wore only a monaural cochlear implant processor. There has been recent interest in benefits arising from bilateral stimulation that may be present for cochlear implant recipients. One option for bilateral stimulation is the use of a cochlear implant in one ear and a hearing aid in the opposite nonimplanted ear (bimodal hearing). This study evaluated the effect of wearing a cochlear implant in one ear and a digital hearing aid in the opposite ear on speech recognition and localization. A repeated-measures correlational study was completed. Nineteen adult Cochlear Nucleus 24 implant recipients participated in the study. The participants were fit with a Widex Senso Vita 38 hearing aid to achieve maximum audibility and comfort within their dynamic range. Soundfield thresholds, loudness growth, speech recognition, localization, and subjective questionnaires were obtained six-eight weeks after the hearing aid fitting. Testing was completed in three conditions: hearing aid only, cochlear implant only, and cochlear implant and hearing aid (bimodal). All tests were repeated four weeks after the first test session. Repeated-measures analysis of variance was used to analyze the data. Significant effects were further examined using pairwise comparison of means or in the case of continuous moderators, regression analyses. The speech-recognition and localization tasks were unique, in that a speech stimulus presented from a variety of roaming azimuths (140 degree loudspeaker array) was used. Performance in the bimodal condition was significantly better for speech recognition and localization compared to the cochlear implant-only and hearing aid-only conditions. Performance was also different between these conditions when the location (i.e., side of the loudspeaker array that

  19. Variational dynamic background model for keyword spotting in handwritten documents

    NASA Astrophysics Data System (ADS)

    Kumar, Gaurav; Wshah, Safwan; Govindaraju, Venu

    2013-12-01

    We propose a bayesian framework for keyword spotting in handwritten documents. This work is an extension to our previous work where we proposed dynamic background model, DBM for keyword spotting that takes into account the local character level scores and global word level scores to learn a logistic regression classifier to separate keywords from non-keywords. In this work, we add a bayesian layer on top of the DBM called the variational dynamic background model, VDBM. The logistic regression classifier uses the sigmoid function to separate keywords from non-keywords. The sigmoid function being neither convex nor concave, exact inference of VDBM becomes intractable. An expectation maximization step is proposed to do approximate inference. The advantage of VDBM over the DBM is multi-fold. Firstly, being bayesian, it prevents over-fitting of data. Secondly, it provides better modeling of data and an improved prediction of unseen data. VDBM is evaluated on the IAM dataset and the results prove that it outperforms our prior work and other state of the art line based word spotting system.

  20. [Patient safety: a comparison between handwritten and computerized voluntary incident reporting].

    PubMed

    Capucho, Helaine Carneiro; Arnas, Emilly Rasquini; Cassiani, Silvia Helena De Bortoli

    2013-03-01

    This study's objective was to compare two types of voluntary incident reporting methods that affect patient safety, handwritten (HR) and computerized (CR), in relation to the number of reports, type of incident reported the individual submitting the report, and quality of reports. This was a descriptive, retrospective and cross-sectional study. CR were more frequent than HR (61.2% vs. 38.6%) among the 1,089 reports analyzed and were submitted every day of the month, while HR were submitted only on weekdays. The highest number of reports referred to medication, followed by problems related to medical-hospital material and the professional who most frequently submitted reports were nurses in both cases. Overall CR presented higher quality than HR (86.1% vs. 61.7%); 36.8% of HR were illegible, a problem that was eliminated in CR. Therefore, the use of computerized incident reporting in hospitals favors qualified voluntary reports, increasing patient safety.

  1. Indoor navigation by image recognition

    NASA Astrophysics Data System (ADS)

    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.

  2. Recognition and inference of crevice processing on digitized paintings

    NASA Astrophysics Data System (ADS)

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

  4. Keywords image retrieval in historical handwritten Arabic documents

    NASA Astrophysics Data System (ADS)

    Saabni, Raid; El-Sana, Jihad

    2013-01-01

    A system is presented for spotting and searching keywords in handwritten Arabic documents. A slightly modified dynamic time warping algorithm is used to measure similarities between words. Two sets of features are generated from the outer contour of the words/word-parts. The first set is based on the angles between nodes on the contour and the second set is based on the shape context features taken from the outer contour. To recognize a given word, the segmentation-free approach is partially adopted, i.e., continuous word parts are used as the basic alphabet, instead of individual characters or complete words. Additional strokes, such as dots and detached short segments, are classified and used in a postprocessing step to determine the final comparison decision. The search for a keyword is performed by the search for its word parts given in the correct order. The performance of the presented system was very encouraging in terms of efficiency and match rates. To evaluate the presented system its performance is compared to three different systems. Unfortunately, there are no publicly available standard datasets with ground truth for testing Arabic key word searching systems. Therefore, a private set of images partially taken from Juma'a Al-Majid Center in Dubai for evaluation is used, while using a slightly modified version of the IFN/ENIT database for training.

  5. Thermal-Polarimetric and Visible Data Collection for Face Recognition

    DTIC Science & Technology

    2016-09-01

    pixels • Spectral range: 7.5–13 μm • Analog image output: NTSC analog video • Digital image output: Firewire radiometric, 14-bit digital video to...PC The analog video was not used for this study. The radiometric, 14-bit digital data provided temperature measurement information for comparison...distribution unlimited. 18 9. References 1. Choi J, Hu S, Young SS, Davis LS. Thermal to visible face recognition. Proc. SPIE 8371, Sensing

  6. Object recognition of ladar with support vector machine

    NASA Astrophysics Data System (ADS)

    Sun, Jian-Feng; Li, Qi; Wang, Qi

    2005-01-01

    Intensity, range and Doppler images can be obtained by using laser radar. Laser radar can detect much more object information than other detecting sensor, such as passive infrared imaging and synthetic aperture radar (SAR), so it is well suited as the sensor of object recognition. Traditional method of laser radar object recognition is extracting target features, which can be influenced by noise. In this paper, a laser radar recognition method-Support Vector Machine is introduced. Support Vector Machine (SVM) is a new hotspot of recognition research after neural network. It has well performance on digital written and face recognition. Two series experiments about SVM designed for preprocessing and non-preprocessing samples are performed by real laser radar images, and the experiments results are compared.

  7. A Taxonomy of 3D Occluded Objects Recognition Techniques

    NASA Astrophysics Data System (ADS)

    Soleimanizadeh, Shiva; Mohamad, Dzulkifli; Saba, Tanzila; Al-ghamdi, Jarallah Saleh

    2016-03-01

    The overall performances of object recognition techniques under different condition (e.g., occlusion, viewpoint, and illumination) have been improved significantly in recent years. New applications and hardware are shifted towards digital photography, and digital media. This faces an increase in Internet usage requiring object recognition for certain applications; particularly occulded objects. However occlusion is still an issue unhandled, interlacing the relations between extracted feature points through image, research is going on to develop efficient techniques and easy to use algorithms that would help users to source images; this need to overcome problems and issues regarding occlusion. The aim of this research is to review recognition occluded objects algorithms and figure out their pros and cons to solve the occlusion problem features, which are extracted from occluded object to distinguish objects from other co-existing objects by determining the new techniques, which could differentiate the occluded fragment and sections inside an image.

  8. Handwritten word preprocessing for database adaptation

    NASA Astrophysics Data System (ADS)

    Oprean, Cristina; Likforman-Sulem, Laurence; Mokbel, Chafic

    2013-01-01

    Handwriting recognition systems are typically trained using publicly available databases, where data have been collected in controlled conditions (image resolution, paper background, noise level,...). Since this is not often the case in real-world scenarios, classification performance can be affected when novel data is presented to the word recognition system. To overcome this problem, we present in this paper a new approach called database adaptation. It consists of processing one set (training or test) in order to adapt it to the other set (test or training, respectively). Specifically, two kinds of preprocessing, namely stroke thickness normalization and pixel intensity normalization are considered. The advantage of such approach is that we can re-use the existing recognition system trained on controlled data. We conduct several experiments with the Rimes 2011 word database and with a real-world database. We adapt either the test set or the training set. Results show that training set adaptation achieves better results than test set adaptation, at the cost of a second training stage on the adapted data. Accuracy of data set adaptation is increased by 2% to 3% in absolute value over no adaptation.

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

    PubMed

    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

  10. Exhibits Recognition System for Combining Online Services and Offline Services

    NASA Astrophysics Data System (ADS)

    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.

  11. Development of an Autonomous Face Recognition Machine.

    DTIC Science & Technology

    1986-12-08

    This approach, like Baron’s, would be a very time consuming task. The problem of locating a face in Bromley’s work was the least complex of the three...top level design and the development and design decisions that were made in developing the Autonomous Face Recognition Machine (AFRM). The chapter is...images within a digital image. The second sectio examines the algorithm used in performing face recognition. The decision to divide the development

  12. Automatic Target Recognition Based on Cross-Plot

    PubMed Central

    Wong, Kelvin Kian Loong; Abbott, Derek

    2011-01-01

    Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository. PMID:21980508

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

    PubMed

    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.

  14. Automatic violence detection in digital movies

    NASA Astrophysics Data System (ADS)

    Fischer, Stephan

    1996-11-01

    Research on computer-based recognition of violence is scant. We are working on the automatic recognition of violence in digital movies, a first step towards the goal of a computer- assisted system capable of protecting children against TV programs containing a great deal of violence. In the video domain a collision detection and a model-mapping to locate human figures are run, while the creation and comparison of fingerprints to find certain events are run int he audio domain. This article centers on the recognition of fist- fights in the video domain and on the recognition of shots, explosions and cries in the audio domain.

  15. Palmprint Recognition Across Different Devices.

    PubMed

    Jia, Wei; Hu, Rong-Xiang; Gui, Jie; Zhao, Yang; Ren, Xiao-Ming

    2012-01-01

    In this paper, the problem of Palmprint Recognition Across Different Devices (PRADD) is investigated, which has not been well studied so far. Since there is no publicly available PRADD image database, we created a non-contact PRADD image database containing 12,000 grayscale captured from 100 subjects using three devices, i.e., one digital camera and two smart-phones. Due to the non-contact image acquisition used, rotation and scale changes between different images captured from a same palm are inevitable. We propose a robust method to calculate the palm width, which can be effectively used for scale normalization of palmprints. On this PRADD image database, we evaluate the recognition performance of three different methods, i.e., subspace learning method, correlation method, and orientation coding based method, respectively. Experiments results show that orientation coding based methods achieved promising recognition performance for PRADD.

  16. [New discovery of the handwritten draft of Eucharius Rösslin's midwifery textbook Pregnant Women and Midwives Rosengarten and Ps.-Ortlof's Small Book for Women].

    PubMed

    Kruse, B J

    1994-01-01

    The author of the famous midwifery text book Der schwangeren Frauen und Hebammen Rosengarten has until now thought to have been Eucharius Rösslin the Elder, in whose name the first printed edition of the work appeared in 1513. According to him, he compiled the text from various sources in the years 1508-1512 at the suggestion of the Duchess Catherine of Brunswick-Luneburg. In the SB und UB Hamburg there is a handwritten preliminary draft of Rosengarten (Cod. med. 801, p. 9-130), dated by the scribe in the year 1494 (this is borne out by watermark analysis). It reproduces the text of Rosengarten without the privilegium, the dedication and the rhyming 'admonition' of the pregnant women and the midwives, as well as the glossary and the illustrative woodcuts almost identically. The printed version of Rosengarten was also expanded by Eucharius Rösslin the Elder with passages among others from Ps.-Ortolfs Frauenbüchlein. The author of this paper was also able to trace a handwritten preliminary draft of Frauenbüchlein, until now unknown, in manuscript 2967 of the Austrian National Library in Vienna. The remark Hic liber pertinet ad Constantinum Roeslin written in the manuscript by a previous owner, and a treatise on syphilis in the hand Eucharius Rösslin the Younger, would indicate that Cod. med. 801 was once in the possession of the Rösslin family. Since Eucharius Rösslin the Elder was born around 1470, and since errors and omissions in Cod. med. 801 indicate that it is a copy of an older text, we are confronted with the question of whether or not the handwritten edition of Rosengarten originates from him or from some other author.

  17. Palmprint Recognition across Different Devices

    PubMed Central

    Jia, Wei; Hu, Rong-Xiang; Gui, Jie; Zhao, Yang; Ren, Xiao-Ming

    2012-01-01

    In this paper, the problem of Palmprint Recognition Across Different Devices (PRADD) is investigated, which has not been well studied so far. Since there is no publicly available PRADD image database, we created a non-contact PRADD image database containing 12,000 grayscale captured from 100 subjects using three devices, i.e., one digital camera and two smart-phones. Due to the non-contact image acquisition used, rotation and scale changes between different images captured from a same palm are inevitable. We propose a robust method to calculate the palm width, which can be effectively used for scale normalization of palmprints. On this PRADD image database, we evaluate the recognition performance of three different methods, i.e., subspace learning method, correlation method, and orientation coding based method, respectively. Experiments results show that orientation coding based methods achieved promising recognition performance for PRADD. PMID:22969380

  18. Compact hybrid optoelectrical unit for image processing and recognition

    NASA Astrophysics Data System (ADS)

    Cheng, Gang; Jin, Guofan; Wu, Minxian; Liu, Haisong; He, Qingsheng; Yuan, ShiFu

    1998-07-01

    In this paper a compact opto-electric unit (CHOEU) for digital image processing and recognition is proposed. The central part of CHOEU is an incoherent optical correlator, which is realized with a SHARP QA-1200 8.4 inch active matrix TFT liquid crystal display panel which is used as two real-time spatial light modulators for both the input image and reference template. CHOEU can do two main processing works. One is digital filtering; the other is object matching. Using CHOEU an edge-detection operator is realized to extract the edges from the input images. Then the reprocessed images are sent into the object recognition unit for identifying the important targets. A novel template- matching method is proposed for gray-tome image recognition. A positive and negative cycle-encoding method is introduced to realize the absolute difference measurement pixel- matching on a correlator structure simply. The system has god fault-tolerance ability for rotation distortion, Gaussian noise disturbance or information losing. The experiments are given at the end of this paper.

  19. Newly Digitized Historical Climate Data of the German Bight and the Southern Baltic Sea Coasts

    NASA Astrophysics Data System (ADS)

    Röhrbein, Dörte; Tinz, Birger; von Storch, Hans

    2015-04-01

    The detection of historical climate information plays an important role with regard to the discussion on climate change, particularly on storminess. The German Meteorological Service houses huge archives of historical handwritten journals of weather observations. A considerable number of original observation sheets from stations along the coast of the German Bight and the southern Baltic Sea exists which has been until recently almost unnoticed. These stations are called signal stations and are positioned close to the shore. However, for this region meteorological observation data of 128 stations exist from 1877 to 1999 and are partly digitized. In this study we show an analysis of firstly newly digitized wind and surface air pressure data of 15 stations from 1877 to 1939 and we also present a case study of the storm surge at the coast of the southern Baltic Sea in December 1913. The data are quality controlled by formal, climatological, temporal and consistency checks. It is shown that these historical climate data are usable in consistency and quality for further investigations on climate change, e.g. as input for regional and global reanalysis.

  20. License Plate Recognition System for Indian Vehicles

    NASA Astrophysics Data System (ADS)

    Sanap, P. R.; Narote, S. P.

    2010-11-01

    We consider the task of recognition of Indian vehicle number plates (also called license plates or registration plates in other countries). A system for Indian number plate recognition must cope with wide variations in the appearance of the plates. Each state uses its own range of designs with font variations between the designs. Also, vehicle owners may place the plates inside glass covered frames or use plates made of nonstandard materials. These issues compound the complexity of automatic number plate recognition, making existing approaches inadequate. We have developed a system that incorporates a novel combination of image processing and artificial neural network technologies to successfully locate and read Indian vehicle number plates in digital images. Commercial application of the system is envisaged.

  1. Defining event reconstruction of digital crime scenes.

    PubMed

    Carrier, Brian D; Spafford, Eugene H

    2004-11-01

    Event reconstruction plays a critical role in solving physical crimes by explaining why a piece of physical evidence has certain characteristics. With digital crimes, the current focus has been on the recognition and identification of digital evidence using an object's characteristics, but not on the identification of the events that caused the characteristics. This paper examines digital event reconstruction and proposes a process model and procedure that can be used for a digital crime scene. The model has been designed so that it can apply to physical crime scenes, can support the unique aspects of a digital crime scene, and can be implemented in software to automate part of the process. We also examine the differences between physical event reconstruction and digital event reconstruction.

  2. Effectiveness of feature and classifier algorithms in character recognition systems

    NASA Astrophysics Data System (ADS)

    Wilson, Charles L.

    1993-04-01

    At the first Census Optical Character Recognition Systems Conference, NIST generated accuracy data for more than character recognition systems. Most systems were tested on the recognition of isolated digits and upper and lower case alphabetic characters. The recognition experiments were performed on sample sizes of 58,000 digits, and 12,000 upper and lower case alphabetic characters. The algorithms used by the 26 conference participants included rule-based methods, image-based methods, statistical methods, and neural networks. The neural network methods included Multi-Layer Perceptron's, Learned Vector Quantitization, Neocognitrons, and cascaded neural networks. In this paper 11 different systems are compared using correlations between the answers of different systems, comparing the decrease in error rate as a function of confidence of recognition, and comparing the writer dependence of recognition. This comparison shows that methods that used different algorithms for feature extraction and recognition performed with very high levels of correlation. This is true for neural network systems, hybrid systems, and statistically based systems, and leads to the conclusion that neural networks have not yet demonstrated a clear superiority to more conventional statistical methods. Comparison of these results with the models of Vapnick (for estimation problems), MacKay (for Bayesian statistical models), Moody (for effective parameterization), and Boltzmann models (for information content) demonstrate that as the limits of training data variance are approached, all classifier systems have similar statistical properties. The limiting condition can only be approached for sufficiently rich feature sets because the accuracy limit is controlled by the available information content of the training set, which must pass through the feature extraction process prior to classification.

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

    NASA Astrophysics Data System (ADS)

    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.

  4. Incoherent optical generalized Hough transform: pattern recognition and feature extraction applications

    NASA Astrophysics Data System (ADS)

    Fernández, Ariel; Ferrari, José A.

    2017-05-01

    Pattern recognition and feature extraction are image processing applications of great interest in defect inspection and robot vision among others. In comparison to purely digital methods, the attractiveness of optical processors for pattern recognition lies in their highly parallel operation and real-time processing capability. This work presents an optical implementation of the generalized Hough transform (GHT), a well-established technique for recognition of geometrical features in binary images. Detection of a geometric feature under the GHT is accomplished by mapping the original image to an accumulator space; the large computational requirements for this mapping make the optical implementation an attractive alternative to digital-only methods. We explore an optical setup where the transformation is obtained, and the size and orientation parameters can be controlled, allowing for dynamic scale and orientation-variant pattern recognition. A compact system for the above purposes results from the use of an electrically tunable lens for scale control and a pupil mask implemented on a high-contrast spatial light modulator for orientation/shape variation of the template. Real-time can also be achieved. In addition, by thresholding of the GHT and optically inverse transforming, the previously detected features of interest can be extracted.

  5. Recognition of digital characteristics based new improved genetic algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Meng; Xu, Guoqiang; Lin, Zihao

    2017-08-01

    In the field of digital signal processing, Estimating the characteristics of signal modulation parameters is an significant research direction. The paper determines the set of eigenvalue which can show the difference of the digital signal modulation based on the deep research of the new improved genetic algorithm. Firstly take them as the best gene pool; secondly, The best gene pool will be changed in the genetic evolvement by selecting, overlapping and eliminating each other; Finally, Adapting the strategy of futher enhance competition and punishment to more optimizer the gene pool and ensure each generation are of high quality gene. The simulation results show that this method not only has the global convergence, stability and faster convergence speed.

  6. Studies in automatic speech recognition and its application in aerospace

    NASA Astrophysics Data System (ADS)

    Taylor, Michael Robinson

    Human communication is characterized in terms of the spectral and temporal dimensions of speech waveforms. Electronic speech recognition strategies based on Dynamic Time Warping and Markov Model algorithms are described and typical digit recognition error rates are tabulated. The application of Direct Voice Input (DVI) as an interface between man and machine is explored within the context of civil and military aerospace programmes. Sources of physical and emotional stress affecting speech production within military high performance aircraft are identified. Experimental results are reported which quantify fundamental frequency and coarse temporal dimensions of male speech as a function of the vibration, linear acceleration and noise levels typical of aerospace environments; preliminary indications of acoustic phonetic variability reported by other researchers are summarized. Connected whole-word pattern recognition error rates are presented for digits spoken under controlled Gz sinusoidal whole-body vibration. Correlations are made between significant increases in recognition error rate and resonance of the abdomen-thorax and head subsystems of the body. The phenomenon of vibrato style speech produced under low frequency whole-body Gz vibration is also examined. Interactive DVI system architectures and avionic data bus integration concepts are outlined together with design procedures for the efficient development of pilot-vehicle command and control protocols.

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

    PubMed

    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.

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

    PubMed Central

    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. PMID:24115919

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

    PubMed

    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.

  10. Chemical recognition of gases and gas mixtures with terahertz waves.

    PubMed

    Jacobsen, R H; Mittleman, D M; Nuss, M C

    1996-12-15

    A time-domain chemical-recognition system for classifying gases and analyzing gas mixtures is presented. We analyze the free induction decay exhibited by gases excited by far-infrared (terahertz) pulses in the time domain, using digital signal-processing techniques. A simple geometric picture is used for the classif ication of the waveforms measured for unknown gas species. We demonstrate how the recognition system can be used to determine the partial pressures of an ammonia-water gas mixture.

  11. Chemical recognition of gases and gas mixtures with terahertz waves

    NASA Astrophysics Data System (ADS)

    Jacobsen, R. H.; Mittleman, D. M.; Nuss, M. C.

    1996-12-01

    A time-domain chemical-recognition system for classifying gases and analyzing gas mixtures is presented. We analyze the free induction decay exhibited by gases excited by far-infrared (terahertz) pulses in the time domain, using digital signal-processing techniques. A simple geometric picture is used for the classification of the waveforms measured for unknown gas species. We demonstrate how the recognition system can be used to determine the partial pressures of an ammonia-water gas mixture.

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

    ERIC Educational Resources Information Center

    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…

  13. Digital Holographic Logic

    NASA Technical Reports Server (NTRS)

    Preston, K., Jr.

    1972-01-01

    The characteristics of the holographic logic computer are discussed. The holographic operation is reviewed from the Fourier transform viewpoint, and the formation of holograms for use in performing digital logic are described. The operation of the computer with an experiment in which the binary identity function is calculated is discussed along with devices for achieving real-time performance. An application in pattern recognition using neighborhood logic is presented.

  14. Design and development of an ancient Chinese document recognition system

    NASA Astrophysics Data System (ADS)

    Peng, Liangrui; Xiu, Pingping; Ding, Xiaoqing

    2003-12-01

    The digitization of ancient Chinese documents presents new challenges to OCR (Optical Character Recognition) research field due to the large character set of ancient Chinese characters, variant font types, and versatile document layout styles, as these documents are historical reflections to the thousands of years of Chinese civilization. After analyzing the general characteristics of ancient Chinese documents, we present a solution for recognition of ancient Chinese documents with regular font-types and layout-styles. Based on the previous work on multilingual OCR in TH-OCR system, we focus on the design and development of two key technologies which include character recognition and page segmentation. Experimental results show that the developed character recognition kernel of 19,635 Chinese characters outperforms our original traditional Chinese recognition kernel; Benchmarked test on printed ancient Chinese books proves that the proposed system is effective for regular ancient Chinese documents.

  15. Speculative Method in Digital Education Research

    ERIC Educational Resources Information Center

    Ross, Jen

    2017-01-01

    The question of "what works" is currently dominating educational research, often to the exclusion of other kinds of inquiries and without enough recognition of its limitations. At the same time, digital education practice, policy and research over-emphasises control, efficiency and enhancement, neglecting the "not-yetness" of…

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

    NASA Astrophysics Data System (ADS)

    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.

  17. ASERA: A Spectrum Eye Recognition Assistant

    NASA Astrophysics Data System (ADS)

    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.

  18. Ultrafast learning in a hard-limited neural network pattern recognizer

    NASA Astrophysics Data System (ADS)

    Hu, Chia-Lun J.

    1996-03-01

    As we published in the last five years, the supervised learning in a hard-limited perceptron system can be accomplished in a noniterative manner if the input-output mapping to be learned satisfies a certain positive-linear-independency (or PLI) condition. When this condition is satisfied (for most practical pattern recognition applications, this condition should be satisfied,) the connection matrix required to meet this mapping can be obtained noniteratively in one step. Generally, there exist infinitively many solutions for the connection matrix when the PLI condition is satisfied. We can then select an optimum solution such that the recognition of any untrained patterns will become optimally robust in the recognition mode. The learning speed is very fast and close to real-time because the learning process is noniterative and one-step. This paper reports the theoretical analysis and the design of a practical charter recognition system for recognizing hand-written alphabets. The experimental result is recorded in real-time on an unedited video tape for demonstration purposes. It is seen from this real-time movie that the recognition of the untrained hand-written alphabets is invariant to size, location, orientation, and writing sequence, even the training is done with standard size, standard orientation, central location and standard writing sequence.

  19. YADCLAN: yet another digitally-controlled linear artificial neuron.

    PubMed

    Frenger, Paul

    2003-01-01

    This paper updates the author's 1999 RMBS presentation on digitally controlled linear artificial neuron design. Each neuron is based on a standard operational amplifier having excitatory and inhibitory inputs, variable gain, an amplified linear analog output and an adjustable threshold comparator for digital output. This design employs a 1-wire serial network of digitally controlled potentiometers and resistors whose resistance values are set and read back under microprocessor supervision. This system embodies several unique and useful features, including: enhanced neuronal stability, dynamic reconfigurability and network extensibility. This artificial neuronal is being employed for feature extraction and pattern recognition in an advanced robotic application.

  20. U.S. Army Research Laboratory (ARL) Corporate Dari Document Transcription and Translation Guidelines

    DTIC Science & Technology

    2012-10-01

    text file format. 15. SUBJECT TERMS Transcription, Translation, guidelines, ground truth, Optical character recognition , OCR, Machine Translation, MT...foreign language into a target language in order to train, test, and evaluate optical character recognition (OCR) and machine translation (MT) embedded...graphic element and should not be transcribed. Elements that are not part of the primary text such as handwritten annotations or stamps should not be

  1. Karen and George: Face Recognition by Visually Impaired Children.

    ERIC Educational Resources Information Center

    Ellis, Hadyn D.; And Others

    1988-01-01

    Two visually impaired children, aged 8 and 10, appeared to have severe difficulty in recognizing faces. After assessment, it became apparent that only one had unusually poor facial recognition skills. After training, which included matching face photographs, schematic faces, and digitized faces, there was no evidence of any improvement.…

  2. Optical character recognition based on nonredundant correlation measurements.

    PubMed

    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.

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

    PubMed

    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.

  4. Review of integrated digital systems: evolution and adoption

    NASA Astrophysics Data System (ADS)

    Fritz, Lawrence W.

    The factors that are influencing the evolution of photogrammetric and remote sensing technology to transition into fully integrated digital systems are reviewed. These factors include societal pressures for new, more timely digital products from the Spatial Information Sciencesand the adoption of rapid technological advancements in digital processing hardware and software. Current major developments in leading government mapping agencies of the USA, such as the Digital Production System (DPS) modernization programme at the Defense Mapping Agency, and the Automated Nautical Charting System II (ANCS-II) programme and Integrated Digital Photogrammetric Facility (IDPF) at NOAA/National Ocean Service, illustrate the significant benefits to be realized. These programmes are examples of different levels of integrated systems that have been designed to produce digital products. They provide insights to the management complexities to be considered for very large integrated digital systems. In recognition of computer industry trends, a knowledge-based architecture for managing the complexity of the very large spatial information systems of the future is proposed.

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

    PubMed

    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.

  6. Examining Authenticity: An Initial Exploration of the Suitability of Handwritten Electronic Signatures.

    PubMed

    Heckeroth, J; Boywitt, C D

    2017-06-01

    Considering the increasing relevance of handwritten electronically captured signatures, we evaluated the ability of forensic handwriting examiners (FHEs) to distinguish between authentic and simulated electronic signatures. Sixty-six professional FHEs examined the authenticity of electronic signatures captured with software by signotec on a smartphone Galaxy Note 4 by Samsung and signatures made with a ballpoint pen on paper (conventional signatures). In addition, we experimentally varied the name ("J. König" vs. "A. Zaiser") and the status (authentic vs. simulated) of the signatures in question. FHEs' conclusions about the authenticity did not show a statistically significant general difference between electronic and conventional signatures. Furthermore, no significant discrepancies between electronic and conventional signatures were found with regard to other important aspects of the authenticity examination such as questioned signatures' graphic information content, the suitability of the provided sample signatures, the necessity of further examinations and the levels of difficulty of the cases under examination. Thus, this study did not reveal any indications that electronic signatures captured with software by signotec on a Galaxy Note 4 are less well suited than conventional signatures for the examination of authenticity, precluding potential technical problems concerning the integrity of electronic signatures. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Neural network face recognition using wavelets

    NASA Astrophysics Data System (ADS)

    Karunaratne, Passant V.; Jouny, Ismail I.

    1997-04-01

    The recognition of human faces is a phenomenon that has been mastered by the human visual system and that has been researched extensively in the domain of computer neural networks and image processing. This research is involved in the study of neural networks and wavelet image processing techniques in the application of human face recognition. The objective of the system is to acquire a digitized still image of a human face, carry out pre-processing on the image as required, an then, given a prior database of images of possible individuals, be able to recognize the individual in the image. The pre-processing segment of the system includes several procedures, namely image compression, denoising, and feature extraction. The image processing is carried out using Daubechies wavelets. Once the images have been passed through the wavelet-based image processor they can be efficiently analyzed by means of a neural network. A back- propagation neural network is used for the recognition segment of the system. The main constraints of the system is with regard to the characteristics of the images being processed. The system should be able to carry out effective recognition of the human faces irrespective of the individual's facial-expression, presence of extraneous objects such as head-gear or spectacles, and face/head orientation. A potential application of this face recognition system would be as a secondary verification method in an automated teller machine.

  8. Fuzzy logic and neural networks in artificial intelligence and pattern recognition

    NASA Astrophysics Data System (ADS)

    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.

  9. A dynamical pattern recognition model of gamma activity in auditory cortex

    PubMed Central

    Zavaglia, M.; Canolty, R.T.; Schofield, T.M.; Leff, A.P.; Ursino, M.; Knight, R.T.; Penny, W.D.

    2012-01-01

    This paper describes a dynamical process which serves both as a model of temporal pattern recognition in the brain and as a forward model of neuroimaging data. This process is considered at two separate levels of analysis: the algorithmic and implementation levels. At an algorithmic level, recognition is based on the use of Occurrence Time features. Using a speech digit database we show that for noisy recognition environments, these features rival standard cepstral coefficient features. At an implementation level, the model is defined using a Weakly Coupled Oscillator (WCO) framework and uses a transient synchronization mechanism to signal a recognition event. In a second set of experiments, we use the strength of the synchronization event to predict the high gamma (75–150 Hz) activity produced by the brain in response to word versus non-word stimuli. Quantitative model fits allow us to make inferences about parameters governing pattern recognition dynamics in the brain. PMID:22327049

  10. Digital Archiving: Where the Past Lives Again

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

  13. Digital and biological computing in organizations.

    PubMed

    Kampfner, Roberto R

    2002-01-01

    Michael Conrad unveiled many of the fundamental characteristics of biological computing. Underlying the behavioral variability and the adaptability of biological systems are these characteristics, including the ability of biological information processing to exploit quantum features at the atomic level, the powerful 3-D pattern recognition capabilities of macromolecules, the computational efficiency, and the ability to support biological function. Among many other things, Conrad formalized and explicated the underlying principles of biological adaptability, characterized the differences between biological and digital computing in terms of a fundamental tradeoff between adaptability and programmability of information processing, and discussed the challenges of interfacing digital computers and human society. This paper is about the encounter of biological and digital computing. The focus is on the nature of the biological information processing infrastructure of organizations and how it can be extended effectively with digital computing. In order to achieve this goal effectively, however, we need to embed properly digital computing into the information processing aspects of human and social behavior and intelligence, which are fundamentally biological. Conrad's legacy provides a firm, strong, and inspiring foundation for this endeavor.

  14. Pattern recognition and feature extraction with an optical Hough transform

    NASA Astrophysics Data System (ADS)

    Fernández, Ariel

    2016-09-01

    Pattern recognition and localization along with feature extraction are image processing applications of great interest in defect inspection and robot vision among others. In comparison to purely digital methods, the attractiveness of optical processors for pattern recognition lies in their highly parallel operation and real-time processing capability. This work presents an optical implementation of the generalized Hough transform (GHT), a well-established technique for the recognition of geometrical features in binary images. Detection of a geometric feature under the GHT is accomplished by mapping the original image to an accumulator space; the large computational requirements for this mapping make the optical implementation an attractive alternative to digital- only methods. Starting from the integral representation of the GHT, it is possible to device an optical setup where the transformation is obtained, and the size and orientation parameters can be controlled, allowing for dynamic scale and orientation-variant pattern recognition. A compact system for the above purposes results from the use of an electrically tunable lens for scale control and a rotating pupil mask for orientation variation, implemented on a high-contrast spatial light modulator (SLM). Real-time (as limited by the frame rate of the device used to capture the GHT) can also be achieved, allowing for the processing of video sequences. Besides, by thresholding of the GHT (with the aid of another SLM) and inverse transforming (which is optically achieved in the incoherent system under appropriate focusing setting), the previously detected features of interest can be extracted.

  15. Latent log-linear models for handwritten digit classification.

    PubMed

    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.

  16. Digital mammography, cancer screening: Factors important for image compression

    NASA Technical Reports Server (NTRS)

    Clarke, Laurence P.; Blaine, G. James; Doi, Kunio; Yaffe, Martin J.; Shtern, Faina; Brown, G. Stephen; Winfield, Daniel L.; Kallergi, Maria

    1993-01-01

    The use of digital mammography for breast cancer screening poses several novel problems such as development of digital sensors, computer assisted diagnosis (CAD) methods for image noise suppression, enhancement, and pattern recognition, compression algorithms for image storage, transmission, and remote diagnosis. X-ray digital mammography using novel direct digital detection schemes or film digitizers results in large data sets and, therefore, image compression methods will play a significant role in the image processing and analysis by CAD techniques. In view of the extensive compression required, the relative merit of 'virtually lossless' versus lossy methods should be determined. A brief overview is presented here of the developments of digital sensors, CAD, and compression methods currently proposed and tested for mammography. The objective of the NCI/NASA Working Group on Digital Mammography is to stimulate the interest of the image processing and compression scientific community for this medical application and identify possible dual use technologies within the NASA centers.

  17. Rapid communication: Global-local processing affects recognition of distractor emotional faces.

    PubMed

    Srinivasan, Narayanan; Gupta, Rashmi

    2011-03-01

    Recent studies have shown links between happy faces and global, distributed attention as well as sad faces to local, focused attention. Emotions have been shown to affect global-local processing. Given that studies on emotion-cognition interactions have not explored the effect of perceptual processing at different spatial scales on processing stimuli with emotional content, the present study investigated the link between perceptual focus and emotional processing. The study investigated the effects of global-local processing on the recognition of distractor faces with emotional expressions. Participants performed a digit discrimination task with digits at either the global level or the local level presented against a distractor face (happy or sad) as background. The results showed that global processing associated with broad scope of attention facilitates recognition of happy faces, and local processing associated with narrow scope of attention facilitates recognition of sad faces. The novel results of the study provide conclusive evidence for emotion-cognition interactions by demonstrating the effect of perceptual processing on emotional faces. The results along with earlier complementary results on the effect of emotion on global-local processing support a reciprocal relationship between emotional processing and global-local processing. Distractor processing with emotional information also has implications for theories of selective attention.

  18. Method of synthesized phase objects for pattern recognition with rotation invariance

    NASA Astrophysics Data System (ADS)

    Ostroukh, Alexander P.; Butok, Alexander M.; Shvets, Rostislav A.; Yezhov, Pavel V.; Kim, Jin-Tae; Kuzmenko, Alexander V.

    2015-11-01

    We present a development of the method of synthesized phase objects (SPO-method) [1] for the rotation-invariant pattern recognition. For the standard method of recognition and the SPO-method, the comparison of the parameters of correlation signals for a number of amplitude objects is executed at the realization of a rotation in an optical-digital correlator with the joint Fourier transformation. It is shown that not only the invariance relative to a rotation at a realization of the joint correlation for synthesized phase objects (SP-objects) but also the main advantage of the method of SP-objects over the reference one such as the unified δ-like recognition signal with the largest possible signal-to-noise ratio independent of the type of an object are attained.

  19. Static sign language recognition using 1D descriptors and neural networks

    NASA Astrophysics Data System (ADS)

    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.

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

    ERIC Educational Resources Information Center

    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…

  1. Improving the recognition of fingerprint biometric system using enhanced image fusion

    NASA Astrophysics Data System (ADS)

    Alsharif, Salim; El-Saba, Aed; Stripathi, Reshma

    2010-04-01

    Fingerprints recognition systems have been widely used by financial institutions, law enforcement, border control, visa issuing, just to mention few. Biometric identifiers can be counterfeited, but considered more reliable and secure compared to traditional ID cards or personal passwords methods. Fingerprint pattern fusion improves the performance of a fingerprint recognition system in terms of accuracy and security. This paper presents digital enhancement and fusion approaches that improve the biometric of the fingerprint recognition system. It is a two-step approach. In the first step raw fingerprint images are enhanced using high-frequency-emphasis filtering (HFEF). The second step is a simple linear fusion process between the raw images and the HFEF ones. It is shown that the proposed approach increases the verification and identification of the fingerprint biometric recognition system, where any improvement is justified using the correlation performance metrics of the matching algorithm.

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

    NASA Astrophysics Data System (ADS)

    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.

  3. Learning and Inductive Inference

    DTIC Science & Technology

    1982-07-01

    a set of graph grammars to describe visual scenes . Other researchers have applied graph grammars to the pattern recognition of handwritten characters...345 1. Issues / 345 2. Mostows’ operationalizer / 350 0. Learning from ezamples / 360 1. Issues / 3t60 2. Learning in control and pattern recognition ...art.icleis on rote learntinig and ailvice- tAik g. K(ennieth Clarkson contributed Ltte article on grmvit atical inference, anid Geoff’ lroiney wrote

  4. Digital Images and Human Vision

    NASA Technical Reports Server (NTRS)

    Watson, Andrew B.; Null, Cynthia H. (Technical Monitor)

    1997-01-01

    Processing of digital images destined for visual consumption raises many interesting questions regarding human visual sensitivity. This talk will survey some of these questions, including some that have been answered and some that have not. There will be an emphasis upon visual masking, and a distinction will be drawn between masking due to contrast gain control processes, and due to processes such as hypothesis testing, pattern recognition, and visual search.

  5. Attention and recognition memory bias for alcohol-related stimuli among alcohol-dependent patients attending residential treatment.

    PubMed

    Klein, Audrey A; Nelson, Lindsay M; Anker, Justin J

    2013-03-01

    Though studies have examined attentional bias for alcohol-related information among alcohol-dependent individuals, few have examined memory bias. This study examined attention and recognition memory biases for alcohol-related information among patients recently admitted to residential alcohol treatment (n=100; 40% female). Participants completed a computerized attentional task wherein they classified a centrally-presented digit as odd or even. On some trials, an alcohol word, neutral word, or anagram was presented along with the digit. On these dual trials participants first classified the digit and then classified the other stimulus as a word or nonword. Participants took longer to classify digits that appeared with alcohol words compared to neutral words; suggesting the alcohol words distracted them from processing the digit. In a subsequent recognition memory test, participants showed significantly higher hit rates (i.e., correctly classifying an old item as old) and false alarm rates (i.e., incorrectly classifying a new item as old) to the alcohol words compared to the neutral words, and they also showed a more liberal response bias to alcohol words. The findings suggest that alcohol-dependent individuals exhibit both attention and memory bias for alcohol-related information. Copyright © 2012 Elsevier Ltd. All rights reserved.

  6. Impaired processing of self-face recognition in anorexia nervosa.

    PubMed

    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 < 0.001) or BSQ (p < 0.001). Among factors involved, nutritional status and intensity of eating disorders could play a part in impaired self-face recognition.

  7. Digital imaging technology assessment: Digital document storage project

    NASA Technical Reports Server (NTRS)

    1989-01-01

    An ongoing technical assessment and requirements definition project is examining the potential role of digital imaging technology at NASA's STI facility. The focus is on the basic components of imaging technology in today's marketplace as well as the components anticipated in the near future. Presented is a requirement specification for a prototype project, an initial examination of current image processing at the STI facility, and an initial summary of image processing projects at other sites. Operational imaging systems incorporate scanners, optical storage, high resolution monitors, processing nodes, magnetic storage, jukeboxes, specialized boards, optical character recognition gear, pixel addressable printers, communications, and complex software processes.

  8. Distributed digital music archives and libraries

    NASA Astrophysics Data System (ADS)

    Fujinaga, Ichiro

    2005-09-01

    The main goal of this research program is to develop and evaluate practices, frameworks, and tools for the design and construction of worldwide distributed digital music archives and libraries. Over the last few millennia, humans have amassed an enormous amount of musical information that is scattered around the world. It is becoming abundantly clear that the optimal path for acquisition is to distribute the task of digitizing the wealth of historical and cultural heritage material that exists in analogue formats, which may include books and manuscripts related to music, music scores, photographs, videos, audio tapes, and phonograph records. In order to achieve this goal, libraries, museums, and archives throughout the world, large or small, need well-researched policies, proper guidance, and efficient tools to digitize their collections and to make them available economically. The research conducted within the program addresses unique and imminent challenges posed by the digitization and dissemination of music media. The are four major research projects in progress: development and evaluation of digitization methods for preservation of analogue recordings; optical music recognition using microfilms; design of workflow management system with automatic metadata extraction; and formulation of interlibrary communication strategies.

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

    NASA Astrophysics Data System (ADS)

    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.

  10. Digital Badging at The Open University: Recognition for Informal Learning

    ERIC Educational Resources Information Center

    Law, Patrina

    2015-01-01

    Awarding badges to recognise achievement is not a new development. Digital badging now offers new ways to recognise learning and motivate learners, providing evidence of skills and achievements in a variety of formal and informal settings. Badged open courses (BOCs) were piloted in various forms by the Open University (OU) in 2013 to provide a…

  11. Rocket Ozone Data Recovery for Digital Archival

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    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.

  13. Prediction of Word Recognition in the First Half of Grade 1

    ERIC Educational Resources Information Center

    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…

  14. Primary Stability Recognition of the Newly Designed Cementless Femoral Stem Using Digital Signal Processing

    PubMed Central

    Salleh, Sh-Hussain; Hamedi, Mahyar; Zulkifly, Ahmad Hafiz; Lee, Muhammad Hisyam; Mohd Noor, Alias; Harris, Arief Ruhullah A.; Abdul Majid, Norazman

    2014-01-01

    Stress shielding and micromotion are two major issues which determine the success of newly designed cementless femoral stems. The correlation of experimental validation with finite element analysis (FEA) is commonly used to evaluate the stress distribution and fixation stability of the stem within the femoral canal. This paper focused on the applications of feature extraction and pattern recognition using support vector machine (SVM) to determine the primary stability of the implant. We measured strain with triaxial rosette at the metaphyseal region and micromotion with linear variable direct transducer proximally and distally using composite femora. The root mean squares technique is used to feed the classifier which provides maximum likelihood estimation of amplitude, and radial basis function is used as the kernel parameter which mapped the datasets into separable hyperplanes. The results showed 100% pattern recognition accuracy using SVM for both strain and micromotion. This indicates that DSP could be applied in determining the femoral stem primary stability with high pattern recognition accuracy in biomechanical testing. PMID:24800230

  15. Primary stability recognition of the newly designed cementless femoral stem using digital signal processing.

    PubMed

    Baharuddin, Mohd Yusof; Salleh, Sh-Hussain; Hamedi, Mahyar; Zulkifly, Ahmad Hafiz; Lee, Muhammad Hisyam; Mohd Noor, Alias; Harris, Arief Ruhullah A; Abdul Majid, Norazman

    2014-01-01

    Stress shielding and micromotion are two major issues which determine the success of newly designed cementless femoral stems. The correlation of experimental validation with finite element analysis (FEA) is commonly used to evaluate the stress distribution and fixation stability of the stem within the femoral canal. This paper focused on the applications of feature extraction and pattern recognition using support vector machine (SVM) to determine the primary stability of the implant. We measured strain with triaxial rosette at the metaphyseal region and micromotion with linear variable direct transducer proximally and distally using composite femora. The root mean squares technique is used to feed the classifier which provides maximum likelihood estimation of amplitude, and radial basis function is used as the kernel parameter which mapped the datasets into separable hyperplanes. The results showed 100% pattern recognition accuracy using SVM for both strain and micromotion. This indicates that DSP could be applied in determining the femoral stem primary stability with high pattern recognition accuracy in biomechanical testing.

  16. Face photo-sketch synthesis and recognition.

    PubMed

    Wang, Xiaogang; Tang, Xiaoou

    2009-11-01

    In this paper, we propose a novel face photo-sketch synthesis and recognition method using a multiscale Markov Random Fields (MRF) model. Our system has three components: 1) given a face photo, synthesizing a sketch drawing; 2) given a face sketch drawing, synthesizing a photo; and 3) searching for face photos in the database based on a query sketch drawn by an artist. It has useful applications for both digital entertainment and law enforcement. We assume that faces to be studied are in a frontal pose, with normal lighting and neutral expression, and have no occlusions. To synthesize sketch/photo images, the face region is divided into overlapping patches for learning. The size of the patches decides the scale of local face structures to be learned. From a training set which contains photo-sketch pairs, the joint photo-sketch model is learned at multiple scales using a multiscale MRF model. By transforming a face photo to a sketch (or transforming a sketch to a photo), the difference between photos and sketches is significantly reduced, thus allowing effective matching between the two in face sketch recognition. After the photo-sketch transformation, in principle, most of the proposed face photo recognition approaches can be applied to face sketch recognition in a straightforward way. Extensive experiments are conducted on a face sketch database including 606 faces, which can be downloaded from our Web site (http://mmlab.ie.cuhk.edu.hk/facesketch.html).

  17. FPGA design of correlation-based pattern recognition

    NASA Astrophysics Data System (ADS)

    Jridi, Maher; Alfalou, Ayman

    2017-05-01

    Optical/Digital pattern recognition and tracking based on optical/digital correlation are a well-known techniques to detect, identify and localize a target object in a scene. Despite the limited number of treatments required by the correlation scheme, computational time and resources are relatively high. The most computational intensive treatment required by the correlation is the transformation from spatial to spectral domain and then from spectral to spatial domain. Furthermore, these transformations are used on optical/digital encryption schemes like the double random phase encryption (DRPE). In this paper, we present a VLSI architecture for the correlation scheme based on the fast Fourier transform (FFT). One interesting feature of the proposed scheme is its ability to stream image processing in order to perform correlation for video sequences. A trade-off between the hardware consumption and the robustness of the correlation can be made in order to understand the limitations of the correlation implementation in reconfigurable and portable platforms. Experimental results obtained from HDL simulations and FPGA prototype have demonstrated the advantages of the proposed scheme.

  18. Automatic target recognition apparatus and method

    DOEpatents

    Baumgart, Chris W.; Ciarcia, Christopher A.

    2000-01-01

    An automatic target recognition apparatus (10) is provided, having a video camera/digitizer (12) for producing a digitized image signal (20) representing an image containing therein objects which objects are to be recognized if they meet predefined criteria. The digitized image signal (20) is processed within a video analysis subroutine (22) residing in a computer (14) in a plurality of parallel analysis chains such that the objects are presumed to be lighter in shading than the background in the image in three of the chains and further such that the objects are presumed to be darker than the background in the other three chains. In two of the chains the objects are defined by surface texture analysis using texture filter operations. In another two of the chains the objects are defined by background subtraction operations. In yet another two of the chains the objects are defined by edge enhancement processes. In each of the analysis chains a calculation operation independently determines an error factor relating to the probability that the objects are of the type which should be recognized, and a probability calculation operation combines the results of the analysis chains.

  19. Audio-based deep music emotion recognition

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    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.

  1. Recognition intent and visual word recognition.

    PubMed

    Wang, Man-Ying; Ching, Chi-Le

    2009-03-01

    This study adopted a change detection task to investigate whether and how recognition intent affects the construction of orthographic representation in visual word recognition. Chinese readers (Experiment 1-1) and nonreaders (Experiment 1-2) detected color changes in radical components of Chinese characters. Explicit recognition demand was imposed in Experiment 2 by an additional recognition task. When the recognition was implicit, a bias favoring the radical location informative of character identity was found in Chinese readers (Experiment 1-1), but not nonreaders (Experiment 1-2). With explicit recognition demands, the effect of radical location interacted with radical function and word frequency (Experiment 2). An estimate of identification performance under implicit recognition was derived in Experiment 3. These findings reflect the joint influence of recognition intent and orthographic regularity in shaping readers' orthographic representation. The implication for the role of visual attention in word recognition was also discussed.

  2. Validation of the international labour office digitized standard images for recognition and classification of radiographs of pneumoconiosis.

    PubMed

    Halldin, Cara N; Petsonk, Edward L; Laney, A Scott

    2014-03-01

    Chest radiographs are recommended for prevention and detection of pneumoconiosis. In 2011, the International Labour Office (ILO) released a revision of the International Classification of Radiographs of Pneumoconioses that included a digitized standard images set. The present study compared results of classifications of digital chest images performed using the new ILO 2011 digitized standard images to classification approaches used in the past. Underground coal miners (N = 172) were examined using both digital and film-screen radiography (FSR) on the same day. Seven National Institute for Occupational Safety and Health-certified B Readers independently classified all 172 digital radiographs, once using the ILO 2011 digitized standard images (DRILO2011-D) and once using digitized standard images used in the previous research (DRRES). The same seven B Readers classified all the miners' chest films using the ILO film-based standards. Agreement between classifications of FSR and digital radiography was identical, using a standard image set (either DRILO2011-D or DRRES). The overall weighted κ value was 0.58. Some specific differences in the results were seen and noted. However, intrareader variability in this study was similar to the published values and did not appear to be affected by the use of the new ILO 2011 digitized standard images. These findings validate the use of the ILO digitized standard images for classification of small pneumoconiotic opacities. When digital chest radiographs are obtained and displayed appropriately, results of pneumoconiosis classifications using the 2011 ILO digitized standards are comparable to film-based ILO classifications and to classifications using earlier research standards. Published by Elsevier Inc.

  3. Optical Fourier diffractometry applied to degraded bone structure recognition

    NASA Astrophysics Data System (ADS)

    Galas, Jacek; Godwod, Krzysztof; Szawdyn, Jacek; Sawicki, Andrzej

    1993-09-01

    Image processing and recognition methods are useful in many fields. This paper presents the hybrid optical and digital method applied to recognition of pathological changes in bones involved by metabolic bone diseases. The trabecular bone structure, registered by x ray on the photographic film, is analyzed in the new type of computer controlled diffractometer. The set of image parameters, extracted from diffractogram, is evaluated by statistical analysis. The synthetic image descriptors in discriminant space, constructed on the base of 3 training groups of images (control, osteoporosis, and osteomalacia groups) by discriminant analysis, allow us to recognize bone samples with degraded bone structure and to recognize the disease. About 89% of the images were classified correctly. This method after optimization process will be verified in medical investigations.

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

    PubMed

    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.

  5. Localization and recognition of traffic signs for automated vehicle control systems

    NASA Astrophysics Data System (ADS)

    Zadeh, Mahmoud M.; Kasvand, T.; Suen, Ching Y.

    1998-01-01

    We present a computer vision system for detection and recognition of traffic signs. Such systems are required to assist drivers and for guidance and control of autonomous vehicles on roads and city streets. For experiments we use sequences of digitized photographs and off-line analysis. The system contains four stages. First, region segmentation based on color pixel classification called SRSM. SRSM limits the search to regions of interest in the scene. Second, we use edge tracing to find parts of outer edges of signs which are circular or straight, corresponding to the geometrical shapes of traffic signs. The third step is geometrical analysis of the outer edge and preliminary recognition of each candidate region, which may be a potential traffic sign. The final step in recognition uses color combinations within each region and model matching. This system maybe used for recognition of other types of objects, provided that the geometrical shape and color content remain reasonably constant. The method is reliable, easy to implement, and fast, This differs form the road signs recognition method in the PROMETEUS. The overall structure of the approach is sketched.

  6. Apply lightweight recognition algorithms in optical music recognition

    NASA Astrophysics Data System (ADS)

    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%.

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

    ERIC Educational Resources Information Center

    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…

  8. Use of Adaptive Digital Signal Processing to Improve Speech Communication for Normally Hearing aand Hearing-Impaired Subjects.

    ERIC Educational Resources Information Center

    Harris, Richard W.; And Others

    1988-01-01

    A two-microphone adaptive digital noise cancellation technique improved word-recognition ability for 20 normal and 12 hearing-impaired adults by reducing multitalker speech babble and speech spectrum noise 18-22 dB. Word recognition improvements averaged 37-50 percent for normal and 27-40 percent for hearing-impaired subjects. Improvement was best…

  9. Digital video steganalysis exploiting collusion sensitivity

    NASA Astrophysics Data System (ADS)

    Budhia, Udit; Kundur, Deepa

    2004-09-01

    In this paper we present an effective steganalyis technique for digital video sequences based on the collusion attack. Steganalysis is the process of detecting with a high probability and low complexity the presence of covert data in multimedia. Existing algorithms for steganalysis target detecting covert information in still images. When applied directly to video sequences these approaches are suboptimal. In this paper, we present a method that overcomes this limitation by using redundant information present in the temporal domain to detect covert messages in the form of Gaussian watermarks. Our gains are achieved by exploiting the collusion attack that has recently been studied in the field of digital video watermarking, and more sophisticated pattern recognition tools. Applications of our scheme include cybersecurity and cyberforensics.

  10. Integrated segmentation and recognition of connected Ottoman script

    NASA Astrophysics Data System (ADS)

    Yalniz, Ismet Zeki; Altingovde, Ismail Sengor; Güdükbay, Uğur; Ulusoy, Özgür

    2009-11-01

    We propose a novel context-sensitive segmentation and recognition method for connected letters in Ottoman script. This method first extracts a set of segments from a connected script and determines the candidate letters to which extracted segments are most similar. Next, a function is defined for scoring each different syntactically correct sequence of these candidate letters. To find the candidate letter sequence that maximizes the score function, a directed acyclic graph is constructed. The letters are finally recognized by computing the longest path in this graph. Experiments using a collection of printed Ottoman documents reveal that the proposed method provides >90% precision and recall figures in terms of character recognition. In a further set of experiments, we also demonstrate that the framework can be used as a building block for an information retrieval system for digital Ottoman archives.

  11. Distant Speech Recognition Using a Microphone Array Network

    NASA Astrophysics Data System (ADS)

    Nakano, Alberto Yoshihiro; Nakagawa, Seiichi; Yamamoto, Kazumasa

    In this work, spatial information consisting of the position and orientation angle of an acoustic source is estimated by an artificial neural network (ANN). The estimated position of a speaker in an enclosed space is used to refine the estimated time delays for a delay-and-sum beamformer, thus enhancing the output signal. On the other hand, the orientation angle is used to restrict the lexicon used in the recognition phase, assuming that the speaker faces a particular direction while speaking. To compensate the effect of the transmission channel inside a short frame analysis window, a new cepstral mean normalization (CMN) method based on a Gaussian mixture model (GMM) is investigated and shows better performance than the conventional CMN for short utterances. The performance of the proposed method is evaluated through Japanese digit/command recognition experiments.

  12. Pattern-Recognition Processor Using Holographic Photopolymer

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin; Cammack, Kevin

    2006-01-01

    proposed joint-transform optical correlator (JTOC) would be capable of operating as a real-time pattern-recognition processor. The key correlation-filter reading/writing medium of this JTOC would be an updateable holographic photopolymer. The high-resolution, high-speed characteristics of this photopolymer would enable pattern-recognition processing to occur at a speed three orders of magnitude greater than that of state-of-the-art digital pattern-recognition processors. There are many potential applications in biometric personal identification (e.g., using images of fingerprints and faces) and nondestructive industrial inspection. In order to appreciate the advantages of the proposed JTOC, it is necessary to understand the principle of operation of a conventional JTOC. In a conventional JTOC (shown in the upper part of the figure), a collimated laser beam passes through two side-by-side spatial light modulators (SLMs). One SLM displays a real-time input image to be recognized. The other SLM displays a reference image from a digital memory. A Fourier-transform lens is placed at its focal distance from the SLM plane, and a charge-coupled device (CCD) image detector is placed at the back focal plane of the lens for use as a square-law recorder. Processing takes place in two stages. In the first stage, the CCD records the interference pattern between the Fourier transforms of the input and reference images, and the pattern is then digitized and saved in a buffer memory. In the second stage, the reference SLM is turned off and the interference pattern is fed back to the input SLM. The interference pattern thus becomes Fourier-transformed, yielding at the CCD an image representing the joint-transform correlation between the input and reference images. This image contains a sharp correlation peak when the input and reference images are matched. The drawbacks of a conventional JTOC are the following: The CCD has low spatial resolution and is not an ideal square

  13. Hybrid neuro-fuzzy approach for automatic vehicle license plate recognition

    NASA Astrophysics Data System (ADS)

    Lee, Hsi-Chieh; Jong, Chung-Shi

    1998-03-01

    Most currently available vehicle identification systems use techniques such as R.F., microwave, or infrared to help identifying the vehicle. Transponders are usually installed in the vehicle in order to transmit the corresponding information to the sensory system. It is considered expensive to install a transponder in each vehicle and the malfunction of the transponder will result in the failure of the vehicle identification system. In this study, novel hybrid approach is proposed for automatic vehicle license plate recognition. A system prototype is built which can be used independently or cooperating with current vehicle identification system in identifying a vehicle. The prototype consists of four major modules including the module for license plate region identification, the module for character extraction from the license plate, the module for character recognition, and the module for the SimNet neuro-fuzzy system. To test the performance of the proposed system, three hundred and eighty vehicle image samples are taken by a digital camera. The license plate recognition success rate of the prototype is approximately 91% while the character recognition success rate of the prototype is approximately 97%.

  14. Digital holographic-based cancellable biometric for personal authentication

    NASA Astrophysics Data System (ADS)

    Verma, Gaurav; Sinha, Aloka

    2016-05-01

    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%.

  15. Recognition Of Complex Three Dimensional Objects Using Three Dimensional Moment Invariants

    NASA Astrophysics Data System (ADS)

    Sadjadi, Firooz A.

    1985-01-01

    A technique for the recognition of complex three dimensional objects is presented. The complex 3-D objects are represented in terms of their 3-D moment invariants, algebraic expressions that remain invariant independent of the 3-D objects' orientations and locations in the field of view. The technique of 3-D moment invariants has been used successfully for simple 3-D object recognition in the past. In this work we have extended this method for the representation of more complex objects. Two complex objects are represented digitally; their 3-D moment invariants have been calculated, and then the invariancy of these 3-D invariant moment expressions is verified by changing the orientation and the location of the objects in the field of view. The results of this study have significant impact on 3-D robotic vision, 3-D target recognition, scene analysis and artificial intelligence.

  16. Edge detection techniques for iris recognition system

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    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.

  17. Goal Setting and Open Digital Badges in Higher Education

    ERIC Educational Resources Information Center

    Cheng, Zui; Watson, Sunnie Lee; Newby, Timothy James

    2018-01-01

    While Open Digital Badges (ODBs) has gained an increasing recognition as micro-credentials, many researchers foresee the role of ODBs as an innovative learning tool to enhance learning experiences beyond that of an alternative credential. However, little research has explored this topic. The purposes of this paper are to 1) argue that one way to…

  18. Design of a Digital Library for Human Movement.

    ERIC Educational Resources Information Center

    Ben-Arie, Jezekiel; Pandit, Purvin; Rajaram, ShyamSundar

    This paper is focused on a central aspect in the design of a planned digital library for human movement, i.e. on the aspect of representation and recognition of human activity from video data. The method of representation is important since it has a major impact on the design of all the other building blocks of the system such as the user…

  19. A smart sensor architecture based on emergent computation in an array of outer-totalistic cells

    NASA Astrophysics Data System (ADS)

    Dogaru, Radu; Dogaru, Ioana; Glesner, Manfred

    2005-06-01

    A novel smart-sensor architecture is proposed, capable to segment and recognize characters in a monochrome image. It is capable to provide a list of ASCII codes representing the recognized characters from the monochrome visual field. It can operate as a blind's aid or for industrial applications. A bio-inspired cellular model with simple linear neurons was found the best to perform the nontrivial task of cropping isolated compact objects such as handwritten digits or characters. By attaching a simple outer-totalistic cell to each pixel sensor, emergent computation in the resulting cellular automata lattice provides a straightforward and compact solution to the otherwise computationally intensive problem of character segmentation. A simple and robust recognition algorithm is built in a compact sequential controller accessing the array of cells so that the integrated device can provide directly a list of codes of the recognized characters. Preliminary simulation tests indicate good performance and robustness to various distortions of the visual field.

  20. Using Digital Badges for Developing High School Chemistry Laboratory Skills

    ERIC Educational Resources Information Center

    Hennah, Naomi; Seery, Michael K.

    2017-01-01

    Digital badges are emerging as an approach to offer microaccreditation for student achievements obtained in ongoing course work. They act to offer a formal recognition and framework for multiple small components which together make a significant contribution to student learning. Badges are promoted as a way of highlighting these particular…

  1. ARCH: Adaptive recurrent-convolutional hybrid networks for long-term action recognition

    PubMed Central

    Xin, Miao; Zhang, Hong; Wang, Helong; Sun, Mingui; Yuan, Ding

    2017-01-01

    Recognition of human actions from digital video is a challenging task due to complex interfering factors in uncontrolled realistic environments. In this paper, we propose a learning framework using static, dynamic and sequential mixed features to solve three fundamental problems: spatial domain variation, temporal domain polytrope, and intra- and inter-class diversities. Utilizing a cognitive-based data reduction method and a hybrid “network upon networks” architecture, we extract human action representations which are robust against spatial and temporal interferences and adaptive to variations in both action speed and duration. We evaluated our method on the UCF101 and other three challenging datasets. Our results demonstrated a superior performance of the proposed algorithm in human action recognition. PMID:29290647

  2. Personal recognition using hand shape and texture.

    PubMed

    Kumar, Ajay; Zhang, David

    2006-08-01

    This paper proposes a new bimodal biometric system using feature-level fusion of hand shape and palm texture. The proposed combination is of significance since both the palmprint and hand-shape images are proposed to be extracted from the single hand image acquired from a digital camera. Several new hand-shape features that can be used to represent the hand shape and improve the performance are investigated. The new approach for palmprint recognition using discrete cosine transform coefficients, which can be directly obtained from the camera hardware, is demonstrated. None of the prior work on hand-shape or palmprint recognition has given any attention on the critical issue of feature selection. Our experimental results demonstrate that while majority of palmprint or hand-shape features are useful in predicting the subjects identity, only a small subset of these features are necessary in practice for building an accurate model for identification. The comparison and combination of proposed features is evaluated on the diverse classification schemes; naive Bayes (normal, estimated, multinomial), decision trees (C4.5, LMT), k-NN, SVM, and FFN. Although more work remains to be done, our results to date indicate that the combination of selected hand-shape and palmprint features constitutes a promising addition to the biometrics-based personal recognition systems.

  3. Russian Character Recognition using Self-Organizing Map

    NASA Astrophysics Data System (ADS)

    Gunawan, D.; Arisandi, D.; Ginting, F. M.; Rahmat, R. F.; Amalia, A.

    2017-01-01

    The World Tourism Organization (UNWTO) in 2014 released that there are 28 million visitors who visit Russia. Most of the visitors might have problem in typing Russian word when using digital dictionary. This is caused by the letters, called Cyrillic that used by the Russian and the countries around it, have different shape than Latin letters. The visitors might not familiar with Cyrillic. This research proposes an alternative way to input the Cyrillic words. Instead of typing the Cyrillic words directly, camera can be used to capture image of the words as input. The captured image is cropped, then several pre-processing steps are applied such as noise filtering, binary image processing, segmentation and thinning. Next, the feature extraction process is applied to the image. Cyrillic letters recognition in the image is done by utilizing Self-Organizing Map (SOM) algorithm. SOM successfully recognizes 89.09% Cyrillic letters from the computer-generated images. On the other hand, SOM successfully recognizes 88.89% Cyrillic letters from the image captured by the smartphone’s camera. For the word recognition, SOM successfully recognized 292 words and partially recognized 58 words from the image captured by the smartphone’s camera. Therefore, the accuracy of the word recognition using SOM is 83.42%

  4. Arabic Optical Character Recognition (OCR) Evaluation in Order to Develop a Post-OCR Module

    DTIC Science & Technology

    2011-09-01

    handwritten, and many more have some handwriting in the margins. Some images are blurred or faded to the point of illegibility. Others are mostly or...it is to English, because Arabic has more features such as agreement. We say that Arabic is more “morphologically rich” than English. We intend to

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

    PubMed Central

    Park, Keunyeol; Song, Minkyu

    2018-01-01

    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. PMID:29495273

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

    PubMed

    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.

  7. Reading recognition of pointer meter based on pattern recognition and dynamic three-points on a line

    NASA Astrophysics Data System (ADS)

    Zhang, Yongqiang; Ding, Mingli; Fu, Wuyifang; Li, Yongqiang

    2017-03-01

    Pointer meters are frequently applied to industrial production for they are directly readable. They should be calibrated regularly to ensure the precision of the readings. Currently the method of manual calibration is most frequently adopted to accomplish the verification of the pointer meter, and professional skills and subjective judgment may lead to big measurement errors and poor reliability and low efficiency, etc. In the past decades, with the development of computer technology, the skills of machine vision and digital image processing have been applied to recognize the reading of the dial instrument. In terms of the existing recognition methods, all the parameters of dial instruments are supposed to be the same, which is not the case in practice. In this work, recognition of pointer meter reading is regarded as an issue of pattern recognition. We obtain the features of a small area around the detected point, make those features as a pattern, divide those certified images based on Gradient Pyramid Algorithm, train a classifier with the support vector machine (SVM) and complete the pattern matching of the divided mages. Then we get the reading of the pointer meter precisely under the theory of dynamic three points make a line (DTPML), which eliminates the error caused by tiny differences of the panels. Eventually, the result of the experiment proves that the proposed method in this work is superior to state-of-the-art works.

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

    Code of Federal Regulations, 2010 CFR

    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...

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

    ERIC Educational Resources Information Center

    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…

  10. Higher-order neural network software for distortion invariant object recognition

    NASA Technical Reports Server (NTRS)

    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.

  11. Design and application of pulse information acquisition and analysis system with dynamic recognition in traditional Chinese medicine.

    PubMed

    Zhang, Jian; Niu, Xin; Yang, Xue-zhi; Zhu, Qing-wen; Li, Hai-yan; Wang, Xuan; Zhang, Zhi-guo; Sha, Hong

    2014-09-01

    To design the pulse information which includes the parameter of pulse-position, pulse-number, pulse-shape and pulse-force acquisition and analysis system with function of dynamic recognition, and research the digitalization and visualization of some common cardiovascular mechanism of single pulse. To use some flexible sensors to catch the radial artery pressure pulse wave and utilize the high frequency B mode ultrasound scanning technology to synchronously obtain the information of radial extension and axial movement, by the way of dynamic images, then the gathered information was analyzed and processed together with ECG. Finally, the pulse information acquisition and analysis system was established which has the features of visualization and dynamic recognition, and it was applied to serve for ten healthy adults. The new system overcome the disadvantage of one-dimensional pulse information acquisition and process method which was common used in current research area of pulse diagnosis in traditional Chinese Medicine, initiated a new way of pulse diagnosis which has the new features of dynamic recognition, two-dimensional information acquisition, multiplex signals combination and deep data mining. The newly developed system could translate the pulse signals into digital, visual and measurable motion information of vessel.

  12. Digitizing Villanova University's Eclipsing Binary Card Catalogue

    NASA Astrophysics Data System (ADS)

    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.

  13. PCANet: A Simple Deep Learning Baseline for Image Classification?

    PubMed

    Chan, Tsung-Han; Jia, Kui; Gao, Shenghua; Lu, Jiwen; Zeng, Zinan; Ma, Yi

    2015-12-01

    In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets for different tasks, including Labeled Faces in the Wild (LFW) for face verification; the MultiPIE, Extended Yale B, AR, Facial Recognition Technology (FERET) data sets for face recognition; and MNIST for hand-written digit recognition. Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)]. Even more surprisingly, the model sets new records for many classification tasks on the Extended Yale B, AR, and FERET data sets and on MNIST variations. Additional experiments on other public data sets also demonstrate the potential of PCANet to serve as a simple but highly competitive baseline for texture classification and object recognition.

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

    PubMed

    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.

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

    PubMed

    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.

  16. An optical processor for object recognition and tracking

    NASA Technical Reports Server (NTRS)

    Sloan, J.; Udomkesmalee, S.

    1987-01-01

    The design and development of a miniaturized optical processor that performs real time image correlation are described. The optical correlator utilizes the Vander Lugt matched spatial filter technique. The correlation output, a focused beam of light, is imaged onto a CMOS photodetector array. In addition to performing target recognition, the device also tracks the target. The hardware, composed of optical and electro-optical components, occupies only 590 cu cm of volume. A complete correlator system would also include an input imaging lens. This optical processing system is compact, rugged, requires only 3.5 watts of operating power, and weighs less than 3 kg. It represents a major achievement in miniaturizing optical processors. When considered as a special-purpose processing unit, it is an attractive alternative to conventional digital image recognition processing. It is conceivable that the combined technology of both optical and ditital processing could result in a very advanced robot vision system.

  17. Randomized Prediction Games for Adversarial Machine Learning.

    PubMed

    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

  18. Comparison of eye imaging pattern recognition using neural network

    NASA Astrophysics Data System (ADS)

    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.

  19. Study of optical design of three-dimensional digital ophthalmoscopes.

    PubMed

    Fang, Yi-Chin; Yen, Chih-Ta; Chu, Chin-Hsien

    2015-10-01

    This study primarily involves using optical zoom structures to design a three-dimensional (3D) human-eye optical sensory system with infrared and visible light. According to experimental data on two-dimensional (2D) and 3D images, human-eye recognition of 3D images is substantially higher (approximately 13.182%) than that of 2D images. Thus, 3D images are more effective than 2D images when they are used at work or in high-recognition devices. In the optical system design, infrared and visible light wavebands were incorporated as light sources to perform simulations. The results can be used to facilitate the design of optical systems suitable for 3D digital ophthalmoscopes.

  20. Digital-Electronic/Optical Apparatus Would Recognize Targets

    NASA Technical Reports Server (NTRS)

    Scholl, Marija S.

    1994-01-01

    Proposed automatic target-recognition apparatus consists mostly of digital-electronic/optical cross-correlator that processes infrared images of targets. Infrared images of unknown targets correlated quickly with images of known targets. Apparatus incorporates some features of correlator described in "Prototype Optical Correlator for Robotic Vision System" (NPO-18451), and some of correlator described in "Compact Optical Correlator" (NPO-18473). Useful in robotic system; to recognize and track infrared-emitting, moving objects as variously shaped hot workpieces on conveyor belt.

  1. Assessing the impact of graphical quality on automatic text recognition in digital maps

    NASA Astrophysics Data System (ADS)

    Chiang, Yao-Yi; Leyk, Stefan; Honarvar Nazari, Narges; Moghaddam, Sima; Tan, Tian Xiang

    2016-08-01

    Converting geographic features (e.g., place names) in map images into a vector format is the first step for incorporating cartographic information into a geographic information system (GIS). With the advancement in computational power and algorithm design, map processing systems have been considerably improved over the last decade. However, the fundamental map processing techniques such as color image segmentation, (map) layer separation, and object recognition are sensitive to minor variations in graphical properties of the input image (e.g., scanning resolution). As a result, most map processing results would not meet user expectations if the user does not "properly" scan the map of interest, pre-process the map image (e.g., using compression or not), and train the processing system, accordingly. These issues could slow down the further advancement of map processing techniques as such unsuccessful attempts create a discouraged user community, and less sophisticated tools would be perceived as more viable solutions. Thus, it is important to understand what kinds of maps are suitable for automatic map processing and what types of results and process-related errors can be expected. In this paper, we shed light on these questions by using a typical map processing task, text recognition, to discuss a number of map instances that vary in suitability for automatic processing. We also present an extensive experiment on a diverse set of scanned historical maps to provide measures of baseline performance of a standard text recognition tool under varying map conditions (graphical quality) and text representations (that can vary even within the same map sheet). Our experimental results help the user understand what to expect when a fully or semi-automatic map processing system is used to process a scanned map with certain (varying) graphical properties and complexities in map content.

  2. Sign Language Recognition System using Neural Network for Digital Hardware Implementation

    NASA Astrophysics Data System (ADS)

    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.

  3. Image processing for a tactile/vision substitution system using digital CNN.

    PubMed

    Lin, Chien-Nan; Yu, Sung-Nien; Hu, Jin-Cheng

    2006-01-01

    In view of the parallel processing and easy implementation properties of CNN, we propose to use digital CNN as the image processor of a tactile/vision substitution system (TVSS). The digital CNN processor is used to execute the wavelet down-sampling filtering and the half-toning operations, aiming to extract important features from the images. A template combination method is used to embed the two image processing functions into a single CNN processor. The digital CNN processor is implemented on an intellectual property (IP) and is implemented on a XILINX VIRTEX II 2000 FPGA board. Experiments are designated to test the capability of the CNN processor in the recognition of characters and human subjects in different environments. The experiments demonstrates impressive results, which proves the proposed digital CNN processor a powerful component in the design of efficient tactile/vision substitution systems for the visually impaired people.

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

    PubMed Central

    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

  5. A microcomputer interface for a digital audio processor-based data recording system.

    PubMed

    Croxton, T L; Stump, S J; Armstrong, W M

    1987-10-01

    An inexpensive interface is described that performs direct transfer of digitized data from the digital audio processor and video cassette recorder based data acquisition system designed by Bezanilla (1985, Biophys. J., 47:437-441) to an IBM PC/XT microcomputer. The FORTRAN callable software that drives this interface is capable of controlling the video cassette recorder and starting data collection immediately after recognition of a segment of previously collected data. This permits piecewise analysis of long intervals of data that would otherwise exceed the memory capability of the microcomputer.

  6. A microcomputer interface for a digital audio processor-based data recording system.

    PubMed Central

    Croxton, T L; Stump, S J; Armstrong, W M

    1987-01-01

    An inexpensive interface is described that performs direct transfer of digitized data from the digital audio processor and video cassette recorder based data acquisition system designed by Bezanilla (1985, Biophys. J., 47:437-441) to an IBM PC/XT microcomputer. The FORTRAN callable software that drives this interface is capable of controlling the video cassette recorder and starting data collection immediately after recognition of a segment of previously collected data. This permits piecewise analysis of long intervals of data that would otherwise exceed the memory capability of the microcomputer. PMID:3676444

  7. The application of automatic recognition techniques in the Apollo 9 SO-65 experiment

    NASA Technical Reports Server (NTRS)

    Macdonald, R. B.

    1970-01-01

    A synoptic feature analysis is reported on Apollo 9 remote earth surface photographs that uses the methods of statistical pattern recognition to classify density points and clusterings in digital conversion of optical data. A computer derived geological map of a geological test site indicates that geological features of the range are separable, but that specific rock types are not identifiable.

  8. Semi-automated contour recognition using DICOMautomaton

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

    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.

  9. Accurate, fast, and secure biometric fingerprint recognition system utilizing sensor fusion of fingerprint patterns

    NASA Astrophysics Data System (ADS)

    El-Saba, Aed; Alsharif, Salim; Jagapathi, Rajendarreddy

    2011-04-01

    Fingerprint recognition is one of the first techniques used for automatically identifying people and today it is still one of the most popular and effective biometric techniques. With this increase in fingerprint biometric uses, issues related to accuracy, security and processing time are major challenges facing the fingerprint recognition systems. Previous work has shown that polarization enhancementencoding of fingerprint patterns increase the accuracy and security of fingerprint systems without burdening the processing time. This is mainly due to the fact that polarization enhancementencoding is inherently a hardware process and does not have detrimental time delay effect on the overall process. Unpolarized images, however, posses a high visual contrast and when fused (without digital enhancement) properly with polarized ones, is shown to increase the recognition accuracy and security of the biometric system without any significant processing time delay.

  10. Dimension Reduction With Extreme Learning Machine.

    PubMed

    Kasun, Liyanaarachchi Lekamalage Chamara; Yang, Yan; Huang, Guang-Bin; Zhang, Zhengyou

    2016-08-01

    Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed, and learns the between-class scatter subspace. To this end, this paper investigates a linear and non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights, respectively. Experimental results on USPS handwritten digit recognition data set, CIFAR-10 object recognition, and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time, and normalized mean square error.

  11. Exploring a recognition-induced recognition decrement

    PubMed Central

    Dopkins, Stephen; Ngo, Catherine Trinh; Sargent, Jesse

    2007-01-01

    Four experiments explored a recognition decrement that is associated with the recognition of a word from a short list. The stimulus material for demonstrating the phenomenon was a list of words of different syntactic types. A word from the list was recognized less well following a decision that a word of the same type had occurred in the list than following a decision that such a word had not occurred in the list. A recognition decrement did not occur for a word of a given type following a positive recognition decision to a word of a different type. A recognition decrement did not occur when the list consisted exclusively of nouns. It was concluded that the phenomenon may reflect a criterion shift but probably does not reflect a list strength effect, suppression, or familiarity attribution consequent to a perceived discrepancy between actual and expected fluency. PMID:17063915

  12. Giro form reading machine

    NASA Astrophysics Data System (ADS)

    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.

  13. Digital Management of a Hysteroscopy Surgery Using Parts of the SNOMED Medical Model

    PubMed Central

    Kollias, Anastasios; Paschopoulos, Minas; Evangelou, Angelos; Poulos, Marios

    2012-01-01

    This work describes a hysteroscopy surgery management application that was designed based on the medical information standard SNOMED. We describe how the application fulfils the needs of this procedure and the way in which existing handwritten medical information is effectively transmitted to the application’s database. PMID:22848338

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

    NASA Astrophysics Data System (ADS)

    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.

  15. The ACR-NEMA Digital Imaging And Communications Standard: Evolution, Overview And Implementation Considerations

    NASA Astrophysics Data System (ADS)

    Alzner, Edgar; Murphy, Laura

    1986-06-01

    The growing digital nature of radiology images led to a recognition that compatibility of communication between imaging, display and data storage devices of different modalities and different manufacturers is necessary. The ACR-NEMA Digital Imaging and Communications Standard Committee was formed to develop a communications standard for radiological images. This standard includes the overall structure of a communication message and the protocols for bi-directional communication using end-to-end connections. The evolution and rationale of the ACR-NEMA Digital Imaging and Communication Standard are described. An overview is provided and sane practical implementation considerations are discussed. PACS will became reality only if the medical community accepts and implements the ACR-NEMA Standard.

  16. A unified framework for gesture recognition and spatiotemporal gesture segmentation.

    PubMed

    Alon, Jonathan; Athitsos, Vassilis; Yuan, Quan; Sclaroff, Stan

    2009-09-01

    Within the context of hand gesture recognition, spatiotemporal gesture segmentation is the task of determining, in a video sequence, where the gesturing hand is located and when the gesture starts and ends. Existing gesture recognition methods typically assume either known spatial segmentation or known temporal segmentation, or both. This paper introduces a unified framework for simultaneously performing spatial segmentation, temporal segmentation, and recognition. In the proposed framework, information flows both bottom-up and top-down. A gesture can be recognized even when the hand location is highly ambiguous and when information about when the gesture begins and ends is unavailable. Thus, the method can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds. The proposed method consists of three novel contributions: a spatiotemporal matching algorithm that can accommodate multiple candidate hand detections in every frame, a classifier-based pruning framework that enables accurate and early rejection of poor matches to gesture models, and a subgesture reasoning algorithm that learns which gesture models can falsely match parts of other longer gestures. The performance of the approach is evaluated on two challenging applications: recognition of hand-signed digits gestured by users wearing short-sleeved shirts, in front of a cluttered background, and retrieval of occurrences of signs of interest in a video database containing continuous, unsegmented signing in American Sign Language (ASL).

  17. Retrieving handwriting by combining word spotting and manifold ranking

    NASA Astrophysics Data System (ADS)

    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%.

  18. 8 CFR 292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

    Code of Federal Regulations, 2010 CFR

    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...

  19. Multivariate interactive digital analysis system /MIDAS/ - A new fast multispectral recognition system

    NASA Technical Reports Server (NTRS)

    Kriegler, F.; Marshall, R.; Lampert, S.; Gordon, M.; Cornell, C.; Kistler, R.

    1973-01-01

    The MIDAS system is a prototype, multiple-pipeline digital processor mechanizing the multivariate-Gaussian, maximum-likelihood decision algorithm operating at 200,000 pixels/second. It incorporates displays and film printer equipment under control of a general purpose midi-computer and possesses sufficient flexibility that operational versions of the equipment may be subsequently specified as subsets of the system.

  20. Connected word recognition using a cascaded neuro-computational model

    NASA Astrophysics Data System (ADS)

    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.

  1. Towards fully analog hardware reservoir computing for speech recognition

    NASA Astrophysics Data System (ADS)

    Smerieri, Anteo; Duport, François; Paquot, Yvan; Haelterman, Marc; Schrauwen, Benjamin; Massar, Serge

    2012-09-01

    Reservoir computing is a very recent, neural network inspired unconventional computation technique, where a recurrent nonlinear system is used in conjunction with a linear readout to perform complex calculations, leveraging its inherent internal dynamics. In this paper we show the operation of an optoelectronic reservoir computer in which both the nonlinear recurrent part and the readout layer are implemented in hardware for a speech recognition application. The performance obtained is close to the one of to state-of-the-art digital reservoirs, while the analog architecture opens the way to ultrafast computation.

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

    PubMed

    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.

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

    PubMed Central

    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

  4. Automatic voice recognition using traditional and artificial neural network approaches

    NASA Technical Reports Server (NTRS)

    Botros, Nazeih M.

    1989-01-01

    The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time.

  5. Increasing the efficiency of digitization workflows for herbarium specimens.

    PubMed

    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.

  6. Target Recognition Using Neural Networks for Model Deformation Measurements

    NASA Technical Reports Server (NTRS)

    Ross, Richard W.; Hibler, David L.

    1999-01-01

    Optical measurements provide a non-invasive method for measuring deformation of wind tunnel models. Model deformation systems use targets mounted or painted on the surface of the model to identify known positions, and photogrammetric methods are used to calculate 3-D positions of the targets on the model from digital 2-D images. Under ideal conditions, the reflective targets are placed against a dark background and provide high-contrast images, aiding in target recognition. However, glints of light reflecting from the model surface, or reduced contrast caused by light source or model smoothness constraints, can compromise accurate target determination using current algorithmic methods. This paper describes a technique using a neural network and image processing technologies which increases the reliability of target recognition systems. Unlike algorithmic methods, the neural network can be trained to identify the characteristic patterns that distinguish targets from other objects of similar size and appearance and can adapt to changes in lighting and environmental conditions.

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

    PubMed

    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.

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

    PubMed

    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 (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. 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.

  9. Development of a written music-recognition system using Java and open source technologies

    NASA Astrophysics Data System (ADS)

    Loibner, Gernot; Schwarzl, Andreas; Kovač, Matthias; Paulus, Dietmar; Pölzleitner, Wolfgang

    2005-10-01

    We report on the development of a software system to recognize and interpret printed music. The overall goal is to scan printed music sheets, analyze and recognize the notes, timing, and written text, and derive the all necessary information to use the computers MIDI sound system to play the music. This function is primarily useful for musicians who want to digitize printed music for editing purposes. There exist a number of commercial systems that offer such a functionality. However, on testing these systems, we were astonished on how weak they behave in their pattern recognition parts. Although we submitted very clear and rather flawless scanning input, none of these systems was able to e.g. recognize all notes, staff lines, and systems. They all require a high degree of interaction, post-processing, and editing to get a decent digital version of the hard copy material. In this paper we focus on the pattern recognition area. In a first approach we tested more or less standard methods of adaptive thresholding, blob detection, line detection, and corner detection to find the notes, staff lines, and candidate objects subject to OCR. Many of the objects on this type of material can be learned in a training phase. None of the commercial systems we saw offers the option to train special characters or unusual signatures. A second goal in this project is to use a modern software engineering platform. We were interested in how well Java and open source technologies are suitable for pattern recognition and machine vision. The scanning of music served as a case-study.

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

    PubMed Central

    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. PMID:29666568

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

    PubMed

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

  13. DSP-Based dual-polarity mass spectrum pattern recognition for bio-detection

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Riot, V; Coffee, K; Gard, E

    2006-04-21

    The Bio-Aerosol Mass Spectrometry (BAMS) instrument analyzes single aerosol particles using a dual-polarity time-of-flight mass spectrometer recording simultaneously spectra of thirty to a hundred thousand points on each polarity. We describe here a real-time pattern recognition algorithm developed at Lawrence Livermore National Laboratory that has been implemented on a nine Digital Signal Processor (DSP) system from Signatec Incorporated. The algorithm first preprocesses independently the raw time-of-flight data through an adaptive baseline removal routine. The next step consists of a polarity dependent calibration to a mass-to-charge representation, reducing the data to about five hundred to a thousand channels per polarity. Themore » last step is the identification step using a pattern recognition algorithm based on a library of known particle signatures including threat agents and background particles. The identification step includes integrating the two polarities for a final identification determination using a score-based rule tree. This algorithm, operating on multiple channels per-polarity and multiple polarities, is well suited for parallel real-time processing. It has been implemented on the PMP8A from Signatec Incorporated, which is a computer based board that can interface directly to the two one-Giga-Sample digitizers (PDA1000 from Signatec Incorporated) used to record the two polarities of time-of-flight data. By using optimized data separation, pipelining, and parallel processing across the nine DSPs it is possible to achieve a processing speed of up to a thousand particles per seconds, while maintaining the recognition rate observed on a non-real time implementation. This embedded system has allowed the BAMS technology to improve its throughput and therefore its sensitivity while maintaining a large dynamic range (number of channels and two polarities) thus maintaining the systems specificity for bio-detection.« less

  14. Automatic recognition of light source from color negative films using sorting classification techniques

    NASA Astrophysics Data System (ADS)

    Sanger, Demas S.; Haneishi, Hideaki; Miyake, Yoichi

    1995-08-01

    This paper proposed a simple and automatic method for recognizing the light sources from various color negative film brands by means of digital image processing. First, we stretched the image obtained from a negative based on the standardized scaling factors, then extracted the dominant color component among red, green, and blue components of the stretched image. The dominant color component became the discriminator for the recognition. The experimental results verified that any one of the three techniques could recognize the light source from negatives of any film brands and all brands greater than 93.2 and 96.6% correct recognitions, respectively. This method is significant for the automation of color quality control in color reproduction from color negative film in mass processing and printing machine.

  15. Augmented reality three-dimensional object visualization and recognition with axially distributed sensing.

    PubMed

    Markman, Adam; Shen, Xin; Hua, Hong; Javidi, Bahram

    2016-01-15

    An augmented reality (AR) smartglass display combines real-world scenes with digital information enabling the rapid growth of AR-based applications. We present an augmented reality-based approach for three-dimensional (3D) optical visualization and object recognition using axially distributed sensing (ADS). For object recognition, the 3D scene is reconstructed, and feature extraction is performed by calculating the histogram of oriented gradients (HOG) of a sliding window. A support vector machine (SVM) is then used for classification. Once an object has been identified, the 3D reconstructed scene with the detected object is optically displayed in the smartglasses allowing the user to see the object, remove partial occlusions of the object, and provide critical information about the object such as 3D coordinates, which are not possible with conventional AR devices. To the best of our knowledge, this is the first report on combining axially distributed sensing with 3D object visualization and recognition for applications to augmented reality. The proposed approach can have benefits for many applications, including medical, military, transportation, and manufacturing.

  16. Advances in Testing Techniques for Digital Microfluidic Biochips

    PubMed Central

    Shukla, Vineeta; Hussin, Fawnizu Azmadi; Hamid, Nor Hisham; Zain Ali, Noohul Basheer

    2017-01-01

    With the advancement of digital microfluidics technology, applications such as on-chip DNA analysis, point of care diagnosis and automated drug discovery are common nowadays. The use of Digital Microfluidics Biochips (DMFBs) in disease assessment and recognition of target molecules had become popular during the past few years. The reliability of these DMFBs is crucial when they are used in various medical applications. Errors found in these biochips are mainly due to the defects developed during droplet manipulation, chip degradation and inaccuracies in the bio-assay experiments. The recently proposed Micro-electrode-dot Array (MEDA)-based DMFBs involve both fluidic and electronic domains in the micro-electrode cell. Thus, the testing techniques for these biochips should be revised in order to ensure proper functionality. This paper describes recent advances in the testing technologies for digital microfluidics biochips, which would serve as a useful platform for developing revised/new testing techniques for MEDA-based biochips. Therefore, the relevancy of these techniques with respect to testing of MEDA-based biochips is analyzed in order to exploit the full potential of these biochips. PMID:28749411

  17. Advances in Testing Techniques for Digital Microfluidic Biochips.

    PubMed

    Shukla, Vineeta; Hussin, Fawnizu Azmadi; Hamid, Nor Hisham; Zain Ali, Noohul Basheer

    2017-07-27

    With the advancement of digital microfluidics technology, applications such as on-chip DNA analysis, point of care diagnosis and automated drug discovery are common nowadays. The use of Digital Microfluidics Biochips (DMFBs) in disease assessment and recognition of target molecules had become popular during the past few years. The reliability of these DMFBs is crucial when they are used in various medical applications. Errors found in these biochips are mainly due to the defects developed during droplet manipulation, chip degradation and inaccuracies in the bio-assay experiments. The recently proposed Micro-electrode-dot Array (MEDA)-based DMFBs involve both fluidic and electronic domains in the micro-electrode cell. Thus, the testing techniques for these biochips should be revised in order to ensure proper functionality. This paper describes recent advances in the testing technologies for digital microfluidics biochips, which would serve as a useful platform for developing revised/new testing techniques for MEDA-based biochips. Therefore, the relevancy of these techniques with respect to testing of MEDA-based biochips is analyzed in order to exploit the full potential of these biochips.

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

    PubMed Central

    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 (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. PMID:23082137

  19. DESIGN OF A PATTERN RECOGNITION DIGITAL COMPUTER WITH APPLICATION TO THE AUTOMATIC SCANNING OF BUBBLE CHAMBER NEGATIVES

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    McCormick, B.H.; Narasimhan, R.

    1963-01-01

    The overall computer system contains three main parts: an input device, a pattern recognition unit (PRU), and a control computer. The bubble chamber picture is divided into a grid of st run. Concent 1-mm squares on the film. It is then processed in parallel in a two-dimensional array of 1024 identical processing modules (stalactites) of the PRU. The array can function as a two- dimensional shift register in which results of successive shifting operations can be accumulated. The pattern recognition process is generally controlled by a conventional arithmetic computer. (A.G.W.)

  20. 'Do not attempt resuscitation'--do standardised order forms make a clinical difference above hand-written note entries?

    PubMed

    Lewis, Keir Edward; Edwards, Victoria Middleton; Hall, Sian; Temblett, Paul; Hutchings, Hayley

    2009-01-01

    To quantify any effect of Standardised Order Forms (SOFs), versus hand-written note entries for 'Do Not Attempt Resuscitation'--on the selection and survival of remaining cardiopulmonary resuscitation (CPR) attempts. A prospective, observational study in two UK Hospitals, comparing numbers, demographics and survival rates from CPR attempts for 2 years prior to and 2 years after the introduction of SOFs (the only change in DNAR policy). There were 133 CPR attempts, representing 0.30% of the 44,792 admissions, pre SOFs and 147 CPR attempts representing 0.32% of the 45,340 admissions following the SOFs (p=0.46). The median duration of a CPR attempt was 11min prior to and 15min following the SOFs (p=0.02). Of the CPR attempts, there was no change in mean age (p=0.34), proportions occurring outside working hours (p=0.70) or proportions presenting with an initial shockable rhythm (p=0.30). Survival to discharge following CPR was unchanged (p=0.23). The introduction of SOFs for DNAR orders was associated with a significantly longer duration of CPR (on average by 3-4min) but no difference in overall number, demographics or type of arrest or survival in the remaining CPR attempts.

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

    PubMed

    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.

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

    PubMed Central

    Harris, Michael S.; Boyce, Lauren; Nittrouer, Susan

    2017-01-01

    Purpose 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. Method 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. Results 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. Conclusion 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. PMID:28384805

  3. Flexibility of orthographic and graphomotor coordination during a handwritten copy task: effect of time pressure

    PubMed Central

    Sausset, Solen; Lambert, Eric; Olive, Thierry

    2013-01-01

    The coordination of the various processes involved in language production is a subject of keen debate in writing research. Some authors hold that writing processes can be flexibly coordinated according to task demands, whereas others claim that process coordination is entirely inflexible. For instance, orthographic planning has been shown to be resource-dependent during handwriting, but inflexible in typing, even under time pressure. The present study therefore went one step further in studying flexibility in the coordination of orthographic processing and graphomotor execution, by measuring the impact of time pressure during a handwritten copy task. Orthographic and graphomotor processes were observed via syllable processing. Writers copied out two- and three-syllable words three times in a row, with and without time pressure. Latencies and letter measures at syllable boundaries were analyzed. We hypothesized that if coordination is flexible and varies according to task demands, it should be modified by time pressure, affecting both latency before execution and duration of execution. We therefore predicted that the extent of syllable processing before execution would be reduced under time pressure and, as a consequence, syllable effects during execution would be more salient. Results showed, however, that time pressure interacted neither with syllable number nor with syllable structure. Accordingly, syllable processing appears to remain the same regardless of time pressure. The flexibility of process coordination during handwriting is discussed, as is the operationalization of time pressure constraints. PMID:24319435

  4. One lens optical correlation: application to face recognition.

    PubMed

    Jridi, Maher; Napoléon, Thibault; Alfalou, Ayman

    2018-03-20

    Despite its extensive use, the traditional 4f Vander Lugt Correlator optical setup can be further simplified. We propose a lightweight correlation scheme where the decision is taken in the Fourier plane. For this purpose, the Fourier plane is adapted and used as a decision plane. Then, the offline phase and the decision metric are re-examined in order to keep a reasonable recognition rate. The benefits of the proposed approach are numerous: (1) it overcomes the constraints related to the use of a second lens; (2) the optical correlation setup is simplified; (3) the multiplication with the correlation filter can be done digitally, which offers a higher adaptability according to the application. Moreover, the digital counterpart of the correlation scheme is lightened since with the proposed scheme we get rid of the inverse Fourier transform (IFT) calculation (i.e., decision directly in the Fourier domain without resorting to IFT). To assess the performance of the proposed approach, an insight into digital hardware resources saving is provided. The proposed method involves nearly 100 times fewer arithmetic operators. Moreover, from experimental results in the context of face verification-based correlation, we demonstrate that the proposed scheme provides comparable or better accuracy than the traditional method. One interesting feature of the proposed scheme is that it could greatly outperform the traditional scheme for face identification application in terms of sensitivity to face orientation. The proposed method is found to be digital/optical implementation-friendly, which facilitates its integration on a very broad range of scenarios.

  5. CW-SSIM kernel based random forest for image classification

    NASA Astrophysics Data System (ADS)

    Fan, Guangzhe; Wang, Zhou; Wang, Jiheng

    2010-07-01

    Complex wavelet structural similarity (CW-SSIM) index has been proposed as a powerful image similarity metric that is robust to translation, scaling and rotation of images, but how to employ it in image classification applications has not been deeply investigated. In this paper, we incorporate CW-SSIM as a kernel function into a random forest learning algorithm. This leads to a novel image classification approach that does not require a feature extraction or dimension reduction stage at the front end. We use hand-written digit recognition as an example to demonstrate our algorithm. We compare the performance of the proposed approach with random forest learning based on other kernels, including the widely adopted Gaussian and the inner product kernels. Empirical evidences show that the proposed method is superior in its classification power. We also compared our proposed approach with the direct random forest method without kernel and the popular kernel-learning method support vector machine. Our test results based on both simulated and realworld data suggest that the proposed approach works superior to traditional methods without the feature selection procedure.

  6. Information based universal feature extraction

    NASA Astrophysics Data System (ADS)

    Amiri, Mohammad; Brause, Rüdiger

    2015-02-01

    In many real world image based pattern recognition tasks, the extraction and usage of task-relevant features are the most crucial part of the diagnosis. In the standard approach, they mostly remain task-specific, although humans who perform such a task always use the same image features, trained in early childhood. It seems that universal feature sets exist, but they are not yet systematically found. In our contribution, we tried to find those universal image feature sets that are valuable for most image related tasks. In our approach, we trained a neural network by natural and non-natural images of objects and background, using a Shannon information-based algorithm and learning constraints. The goal was to extract those features that give the most valuable information for classification of visual objects hand-written digits. This will give a good start and performance increase for all other image learning tasks, implementing a transfer learning approach. As result, in our case we found that we could indeed extract features which are valid in all three kinds of tasks.

  7. Geometrical structure of Neural Networks: Geodesics, Jeffrey's Prior and Hyper-ribbons

    NASA Astrophysics Data System (ADS)

    Hayden, Lorien; Alemi, Alex; Sethna, James

    2014-03-01

    Neural networks are learning algorithms which are employed in a host of Machine Learning problems including speech recognition, object classification and data mining. In practice, neural networks learn a low dimensional representation of high dimensional data and define a model manifold which is an embedding of this low dimensional structure in the higher dimensional space. In this work, we explore the geometrical structure of a neural network model manifold. A Stacked Denoising Autoencoder and a Deep Belief Network are trained on handwritten digits from the MNIST database. Construction of geodesics along the surface and of slices taken from the high dimensional manifolds reveal a hierarchy of widths corresponding to a hyper-ribbon structure. This property indicates that neural networks fall into the class of sloppy models, in which certain parameter combinations dominate the behavior. Employing this information could prove valuable in designing both neural network architectures and training algorithms. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No . DGE-1144153.

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

    PubMed

    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

  9. Some Factors Underlying Individual Differences in Speech Recognition on PRESTO: A First Report

    PubMed Central

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

    2013-01-01

    Background 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. Purpose To investigate sensory, perceptual, and neurocognitive differences between good and poor listeners on PRESTO, a new high-variability sentence recognition test under adverse listening conditions. Research Design 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. Study Sample Young, normal-hearing adults (N = 40) from the Indiana University community participated in the current study. Data Collection and Analysis 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 BRIEF-A self-report questionnaire on executive function, and two performance subtests of the WASI Performance IQ (non-verbal intelligence). Scores on self-report questionnaires and behavioral tasks were tallied and analyzed by listener group (HiPRESTO and LoPRESTO). Results The extreme groups did not differ overall on self-reported hearing difficulties in real-world listening environments

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

    PubMed

    Schafer, Phillip B; Jin, Dezhe Z

    2014-03-01

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

  11. A Signal Detection Analysis of Digitized and Photographic Image Modes and Color Realism in a Pictorial Recognition Memory Task.

    ERIC Educational Resources Information Center

    El-Gazzar, Abdel-Latif I.

    The relative effectiveness of digital versus photographic images was examined with 96 college students as subjects. A 2x2 balanced factorial design was employed to test eight hypotheses. The four groups were (1) digitized black and white; (2) digitized pseudocolor; (3) photographic black and white; and (4) photographic realistic color. Findings…

  12. Cost analysis of a project to digitize classic articles in neurosurgery*

    PubMed Central

    Bauer, Kathleen

    2002-01-01

    In summer 2000, the Cushing/Whitney Medical Library at Yale University began a demonstration project to digitize classic articles in neurosurgery from the late 1800s and early 1900s. The objective of the first phase of the project was to measure the time and costs involved in digitization, and those results are reported here. In the second phase, metadata will be added to the digitized articles, and the project will be publicized. Thirteen articles were scanned using optical character recognition (OCR) software, and the resulting text files were carefully proofread. Time for photocopying, scanning, and proofreading were recorded. This project achieved an average cost per item (total pages plus images) of $4.12, a figure at the high end of average costs found in other studies. This project experienced high costs for two reasons. First, the articles contained many images, which required extra processing. Second, the older fonts and the poor condition of many of these articles complicated the OCR process. The average article cost $84.46 to digitize. Although costs were high, the selection of historically important articles maximized the benefit gained from the investment in digitization. PMID:11999182

  13. Cost analysis of a project to digitize classic articles in neurosurgery.

    PubMed

    Bauer, Kathleen

    2002-04-01

    In summer 2000, the Cushing/Whitney Medical Library at Yale University began a demonstration project to digitize classic articles in neurosurgery from the late 1800s and early 1900s. The objective of the first phase of the project was to measure the time and costs involved in digitization, and those results are reported here. In the second phase, metadata will be added to the digitized articles, and the project will be publicized. Thirteen articles were scanned using optical character recognition (OCR) software, and the resulting text files were carefully proofread. Time for photocopying, scanning, and proofreading were recorded. This project achieved an average cost per item (total pages plus images) of $4.12, a figure at the high end of average costs found in other studies. This project experienced high costs for two reasons. First, the articles contained many images, which required extra processing. Second, the older fonts and the poor condition of many of these articles complicated the OCR process. The average article cost $84.46 to digitize. Although costs were high, the selection of historically important articles maximized the benefit gained from the investment in digitization.

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

    NASA Astrophysics Data System (ADS)

    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%.

  15. Recognition and privacy preservation of paper-based health records.

    PubMed

    Fenz, Stefan; Heurix, Johannes; Neubauer, Thomas

    2012-01-01

    While the digitization of medical data within electronic health records has been introduced in some areas, massive amounts of paper-based health records are still produced on a daily basis. This data has to be stored for decades due to legal reasons but is of no benefit for research organizations, as the unstructured medical data in paper-based health records cannot be efficiently used for clinical studies. This paper presents a system for the recognition and privacy preservation of personal data in paper-based health records with the aim to provide clinical studies with medical data gained from existing paper-based health records.

  16. Increasing the efficiency of digitization workflows for herbarium specimens

    PubMed Central

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

    2012-01-01

    Abstract 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. PMID:22859882

  17. Novel Tool for Complete Digitization of Paper Electrocardiography Data.

    PubMed

    Ravichandran, Lakshminarayan; Harless, Chris; Shah, Amit J; Wick, Carson A; Mcclellan, James H; Tridandapani, Srini

    We present a Matlab-based tool to convert electrocardiography (ECG) information from paper charts into digital ECG signals. The tool can be used for long-term retrospective studies of cardiac patients to study the evolving features with prognostic value. To perform the conversion, we: 1) detect the graphical grid on ECG charts using grayscale thresholding; 2) digitize the ECG signal based on its contour using a column-wise pixel scan; and 3) use template-based optical character recognition to extract patient demographic information from the paper ECG in order to interface the data with the patients' medical record. To validate the digitization technique: 1) correlation between the digital signals and signals digitized from paper ECG are performed and 2) clinically significant ECG parameters are measured and compared from both the paper-based ECG signals and the digitized ECG. The validation demonstrates a correlation value of 0.85-0.9 between the digital ECG signal and the signal digitized from the paper ECG. There is a high correlation in the clinical parameters between the ECG information from the paper charts and digitized signal, with intra-observer and inter-observer correlations of 0.8-0.9 (p < 0.05), and kappa statistics ranging from 0.85 (inter-observer) to 1.00 (intra-observer). The important features of the ECG signal, especially the QRST complex and the associated intervals, are preserved by obtaining the contour from the paper ECG. The differences between the measures of clinically important features extracted from the original signal and the reconstructed signal are insignificant, thus highlighting the accuracy of this technique. Using this type of ECG digitization tool to carry out retrospective studies on large databases, which rely on paper ECG records, studies of emerging ECG features can be performed. In addition, this tool can be used to potentially integrate digitized ECG information with digital ECG analysis programs and with the patient

  18. Chemical named entities recognition: a review on approaches and applications

    PubMed Central

    2014-01-01

    The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to “text mine” these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted. PMID:24834132

  19. Holographic implementation of a binary associative memory for improved recognition

    NASA Astrophysics Data System (ADS)

    Bandyopadhyay, Somnath; Ghosh, Ajay; Datta, Asit K.

    1998-03-01

    Neural network associate memory has found wide application sin pattern recognition techniques. We propose an associative memory model for binary character recognition. The interconnection strengths of the memory are binary valued. The concept of sparse coding is sued to enhance the storage efficiency of the model. The question of imposed preconditioning of pattern vectors, which is inherent in a sparsely coded conventional memory, is eliminated by using a multistep correlation technique an the ability of correct association is enhanced in a real-time application. A potential optoelectronic implementation of the proposed associative memory is also described. The learning and recall is possible by using digital optical matrix-vector multiplication, where full use of parallelism and connectivity of optics is made. A hologram is used in the experiment as a longer memory (LTM) for storing all input information. The short-term memory or the interconnection weight matrix required during the recall process is configured by retrieving the necessary information from the holographic LTM.

  20. Chemical named entities recognition: a review on approaches and applications.

    PubMed

    Eltyeb, Safaa; Salim, Naomie

    2014-01-01

    The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to "text mine" these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted.

  1. Fusion of Dependent and Independent Biometric Information Sources

    DTIC Science & Technology

    2005-03-01

    palmprint , DNA, ECG, signature, etc. The comparison of various biometric techniques is given in [13] and is presented in Table 1. Since, each...theory. Experimental studies on the M2VTS database [32] showed that a reduction in error rates is up to about 40%. Four combination strategies are...taken from the CEDAR benchmark database . The word recognition results were the highest (91%) among published results for handwritten words (before 2001

  2. Optical Pattern Recognition

    NASA Astrophysics Data System (ADS)

    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.

  3. Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

    PubMed Central

    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

  4. Identification of Alfalfa Leaf Diseases Using Image Recognition Technology.

    PubMed

    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

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

    PubMed

    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

  6. Discriminative Features Mining for Offline Handwritten Signature Verification

    NASA Astrophysics Data System (ADS)

    Neamah, Karrar; Mohamad, Dzulkifli; Saba, Tanzila; Rehman, Amjad

    2014-03-01

    Signature verification is an active research area in the field of pattern recognition. It is employed to identify the particular person with the help of his/her signature's characteristics such as pen pressure, loops shape, speed of writing and up down motion of pen, writing speed, pen pressure, shape of loops, etc. in order to identify that person. However, in the entire process, features extraction and selection stage is of prime importance. Since several signatures have similar strokes, characteristics and sizes. Accordingly, this paper presents combination of orientation of the skeleton and gravity centre point to extract accurate pattern features of signature data in offline signature verification system. Promising results have proved the success of the integration of the two methods.

  7. Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets

    DTIC Science & Technology

    2015-04-24

    Feature Representations usingProbabilistic Quadtrees and Deep Belief Nets Learning sparse feature representations is a useful instru- ment for solving an...novel framework for the classifi cation of handwritten digits that learns sparse representations using probabilistic quadtrees and Deep Belief Nets... Learning Sparse Feature Representations usingProbabilistic Quadtrees and Deep Belief Nets Report Title Learning sparse feature representations is a useful

  8. Comparative Study of Nonlinear Time Warping Techniques in Isolated Word Speech Recognition Systems

    DTIC Science & Technology

    1981-06-17

    all modules are loaded under a flexible research oriented supervisor, " Cicada ". Cicada allows for the integration of experimental ideas, extensions...evaluate alternate recognition methods. More detailed information about Cicada can be found in7 . In the following we limit our discussion to the design of...43.70 37.78 32.47 44.44 44.32 38 8. Figures Cicada - a flexible research oriented supervisor ReferenceSTernpl ates Front End Matching Digital Signal

  9. Spoken Word Recognition in Toddlers Who Use Cochlear Implants

    PubMed Central

    Grieco-Calub, Tina M.; Saffran, Jenny R.; Litovsky, Ruth Y.

    2010-01-01

    Purpose The purpose of this study was to assess the time course of spoken word recognition in 2-year-old children who use cochlear implants (CIs) in quiet and in the presence of speech competitors. Method Children who use CIs and age-matched peers with normal acoustic hearing listened to familiar auditory labels, in quiet or in the presence of speech competitors, while their eye movements to target objects were digitally recorded. Word recognition performance was quantified by measuring each child’s reaction time (i.e., the latency between the spoken auditory label and the first look at the target object) and accuracy (i.e., the amount of time that children looked at target objects within 367 ms to 2,000 ms after the label onset). Results Children with CIs were less accurate and took longer to fixate target objects than did age-matched children without hearing loss. Both groups of children showed reduced performance in the presence of the speech competitors, although many children continued to recognize labels at above-chance levels. Conclusion The results suggest that the unique auditory experience of young CI users slows the time course of spoken word recognition abilities. In addition, real-world listening environments may slow language processing in young language learners, regardless of their hearing status. PMID:19951921

  10. Colorimetric Recognition of Aldehydes and Ketones.

    PubMed

    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.

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

    NASA Astrophysics Data System (ADS)

    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

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

    NASA Astrophysics Data System (ADS)

    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.

  13. [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?].

    PubMed

    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

  14. Two-stage approach to keyword spotting in handwritten documents

    NASA Astrophysics Data System (ADS)

    Haji, Mehdi; Ameri, Mohammad R.; Bui, Tien D.; Suen, Ching Y.; Ponson, Dominique

    2013-12-01

    Separation of keywords from non-keywords is the main problem in keyword spotting systems which has traditionally been approached by simplistic methods, such as thresholding of recognition scores. In this paper, we analyze this problem from a machine learning perspective, and we study several standard machine learning algorithms specifically in the context of non-keyword rejection. We propose a two-stage approach to keyword spotting and provide a theoretical analysis of the performance of the system which gives insights on how to design the classifier in order to maximize the overall performance in terms of F-measure.

  15. Dynamic Features for Iris Recognition.

    PubMed

    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.

  16. ASERA: A spectrum eye recognition assistant for quasar spectra

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    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.

  18. Real-time optical multiple object recognition and tracking system and method

    NASA Technical Reports Server (NTRS)

    Chao, Tien-Hsin (Inventor); Liu, Hua Kuang (Inventor)

    1987-01-01

    The invention relates to an apparatus and associated methods for the optical recognition and tracking of multiple objects in real time. Multiple point spatial filters are employed that pre-define the objects to be recognized at run-time. The system takes the basic technology of a Vander Lugt filter and adds a hololens. The technique replaces time, space and cost-intensive digital techniques. In place of multiple objects, the system can also recognize multiple orientations of a single object. This later capability has potential for space applications where space and weight are at a premium.

  19. Clinical study of a digital vs an analogue hearing aid.

    PubMed

    Bille, M; Jensen, A M; Kjaerbøl, E; Vesterager, V; Sibelle, P; Nielsen, H

    1999-01-01

    Digital signal processing in hearing instruments has brought new perspectives to the compensation of hearing impairment and may result in alleviation of the adverse effects of hearing problems. This study compares a commercially available digital signal processing hearing aid (HA) (Senso) with a modern analogue HA with programmable fitting (Logo). The HAs tested are identical in appearance and, in spite of a different mode of operation, the study design ensured blinding of the test subjects. Outcome parameters were: improvements in speech recognition score in noise (deltaSRSN) with the HAs; overall preference for HA; overall satisfaction; and various measures of HA performance evaluated by a self-assessment questionnaire. A total of 28 experienced HA users with sensorineural hearing impairment were included and 25 completed the trial. No significant differences were found in deltaSRSN between the two HAs. Eleven subjects indicated an overall preference for the digital HA, 10 preferred the analogue HA and 4 had no preference. Concerning overall satisfaction, 8 subjects rated the digital HA superior to the analogue one, whereas 7 indicated a superior rating for the analogue HA and 10 rated the HAs equal. Acceptability of noise from traffic was the only outcome parameter which gave a significant difference between the HAs in favour of the digital HA. It is concluded that there are no significant differences in outcome between the digital and analogue signal processing HAs tested by these experienced HA-users.

  20. Counterfeit deterrence and digital imaging technology

    NASA Astrophysics Data System (ADS)

    Church, Sara E.; Fuller, Reese H.; Jaffe, Annette B.; Pagano, Lorelei W.

    2000-04-01

    The US government recognizes the growing problem of counterfeiting currency using digital imaging technology, as desktop systems become more sophisticated, less expensive and more prevalent. As the rate of counterfeiting with this type of equipment has grown, the need for specific prevention methods has become apparent to the banknote authorities. As a result, the Treasury Department and Federal Reserve have begun to address issues related specifically to this type of counterfeiting. The technical representatives of these agencies are taking a comprehensive approach to minimize counterfeiting using digital technology. This approach includes identification of current technology solutions for banknote recognition, data stream intervention and output marking, outreach to the hardware and software industries and enhancement of public education efforts. Other aspects include strong support and cooperation with existing international efforts to prevent counterfeiting, review and amendment of existing anti- counterfeiting legislation and investigation of currency design techniques to make faithful reproduction more difficult. Implementation of these steps and others are to lead to establishment of a formal, permanent policy to address and prevent the use of emerging technologies to counterfeit currency.

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

    PubMed

    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.

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

    PubMed Central

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

    2018-01-01

    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. PMID:29531874

  3. A 128-channel Time-to-Digital Converter (TDC) inside a Virtex-5 FPGA on the GANDALF module

    NASA Astrophysics Data System (ADS)

    Büchele, M.; Fischer, H.; Gorzellik, M.; Herrmann, F.; Königsmann, K.; Schill, C.; Schopferer, S.

    2012-03-01

    The GANDALF 6U-VME64x/VXS module has been developed for the digitization and real time analysis of detector signals. To perform different applications such as analog-to-digital or time-to-digital conversions, coincidence matrix formation, fast pattern recognition and trigger generation, this module comes with exchangeable analog and digital mezzanine cards. Based on this platform, we present a 128-channel TDC which is implemented in a single Xilinx Virtex-5 FPGA using a shifted clock sampling method. In contrast to common TDC concepts, the input signal is sampled by 16 equidistant phase-shifted clocks. A particular challenge of the design is the minimum skew routing of the input signals to the sampling flip-flops. We present measurement results for the differential nonlinearity and the time resolution of the TDC readout system.

  4. Drivers of Emerging Infectious Disease Events as a Framework for Digital Detection.

    PubMed

    Olson, Sarah H; Benedum, Corey M; Mekaru, Sumiko R; Preston, Nicholas D; Mazet, Jonna A K; Joly, Damien O; Brownstein, John S

    2015-08-01

    The growing field of digital disease detection, or epidemic intelligence, attempts to improve timely detection and awareness of infectious disease (ID) events. Early detection remains an important priority; thus, the next frontier for ID surveillance is to improve the recognition and monitoring of drivers (antecedent conditions) of ID emergence for signals that precede disease events. These data could help alert public health officials to indicators of elevated ID risk, thereby triggering targeted active surveillance and interventions. We believe that ID emergence risks can be anticipated through surveillance of their drivers, just as successful warning systems of climate-based, meteorologically sensitive diseases are supported by improved temperature and precipitation data. We present approaches to driver surveillance, gaps in the current literature, and a scientific framework for the creation of a digital warning system. Fulfilling the promise of driver surveillance will require concerted action to expand the collection of appropriate digital driver data.

  5. Eliminating chromatic aberration of lens and recognition of thermal images with artificial intelligence applications

    NASA Astrophysics Data System (ADS)

    Fang, Yi-Chin; Wu, Bo-Wen; Lin, Wei-Tang; Jon, Jen-Liung

    2007-11-01

    Resolution and color are two main directions for measuring optical digital image, but it will be a hard work to integral improve the image quality of optical system, because there are many limits such as size, materials and environment of optical system design. Therefore, it is important to let blurred images as aberrations and noises or due to the characteristics of human vision as far distance and small targets to raise the capability of image recognition with artificial intelligence such as genetic algorithm and neural network in the condition that decreasing color aberration of optical system and not to increase complex calculation in the image processes. This study could achieve the goal of integral, economically and effectively to improve recognition and classification in low quality image from optical system and environment.

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

    PubMed

    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.

  7. Is the digitization of laparoscopic movement using accessible alternative technologies possible?

    PubMed

    Lorias Espinoza, Daniel; Gutiérrez Gnecchi, José Antonio; Martínez, Arturo Minor

    2012-05-01

    It is widely documented that laparoscopic surgeons require training, and an objective evaluation of the training that they receive. The most advanced evaluation systems integrate the digitization of the movement of laparoscopic tools. A great number of these systems, however, do not permit the use of real tools and their high cost limits their academic impact. Likewise, it is documented that new and accessible systems need to be developed. The aim of this article is to explore the possibility of digitizing the movement of laparoscopic tools in a three-dimensional workspace, using accessible alternative technology. Our proposal uses a commercial Wii video game control in conjunction with a program for determining kinematic variables during the execution of a recognition task.

  8. Novel Tool for Complete Digitization of Paper Electrocardiography Data

    PubMed Central

    Harless, Chris; Shah, Amit J.; Wick, Carson A.; Mcclellan, James H.

    2013-01-01

    Objective: We present a Matlab-based tool to convert electrocardiography (ECG) information from paper charts into digital ECG signals. The tool can be used for long-term retrospective studies of cardiac patients to study the evolving features with prognostic value. Methods and procedures: To perform the conversion, we: 1) detect the graphical grid on ECG charts using grayscale thresholding; 2) digitize the ECG signal based on its contour using a column-wise pixel scan; and 3) use template-based optical character recognition to extract patient demographic information from the paper ECG in order to interface the data with the patients' medical record. To validate the digitization technique: 1) correlation between the digital signals and signals digitized from paper ECG are performed and 2) clinically significant ECG parameters are measured and compared from both the paper-based ECG signals and the digitized ECG. Results: The validation demonstrates a correlation value of 0.85–0.9 between the digital ECG signal and the signal digitized from the paper ECG. There is a high correlation in the clinical parameters between the ECG information from the paper charts and digitized signal, with intra-observer and inter-observer correlations of 0.8–0.9 \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$({\\rm p}<{0.05})$\\end{document}, and kappa statistics ranging from 0.85 (inter-observer) to 1.00 (intra-observer). Conclusion: The important features of the ECG signal, especially the QRST complex and the associated intervals, are preserved by obtaining the contour from the paper ECG. The differences between the measures of clinically important features extracted from the original signal and the reconstructed signal are insignificant, thus highlighting the accuracy of this technique

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

    ERIC Educational Resources Information Center

    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;…

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

    ERIC Educational Resources Information Center

    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…

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

    ERIC Educational Resources Information Center

    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…

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

    PubMed

    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.

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

    ERIC Educational Resources Information Center

    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…

  14. [Digital vs. analog hearing aids for children. Is there a method for making an objective comparison possible?].

    PubMed

    Prinz, I; Nubel, K; Gross, M

    2002-09-01

    Until now, the assumed benefits of digital hearing aids are reflected only in subjective descriptions by patients with hearing aids, but cannot be documented adequately by routine diagnostic methods. Seventeen schoolchildren with moderate severe bilateral symmetrical sensorineural hearing loss were examined in a double-blinded crossover study. Differences in performance between a fully digital hearing aid (DigiFocus compact/Oticon) and an analogous digitally programmable two-channel hearing aid were evaluated. Of the 17 children, 13 choose the digital and 4 the analogous hearing aid. In contrast to the clear subjective preferences for the fully digital hearing aid, we could not obtain any significant results with routine diagnostic methods. Using the "virtual hearing aid," a subjective comparison and speech recognition performance task yielded significant differences. The virtual hearing aid proved to be suitable for a direct comparison of different hearing aids and can be used for double-blind testing in a pediatric population.

  15. Digital Imprinting of RNA Recognition and Processing on a Self-Assembled Nucleic Acid Matrix

    NASA Astrophysics Data System (ADS)

    Redhu, Shiv K.; Castronovo, Matteo; Nicholson, Allen W.

    2013-08-01

    The accelerating progress of research in nanomedicine and nanobiotechnology has included initiatives to develop highly-sensitive, high-throughput methods to detect biomarkers at the single-cell level. Current sensing approaches, however, typically involve integrative instrumentation that necessarily must balance sensitivity with rapidity in optimizing biomarker detection quality. We show here that laterally-confined, self-assembled monolayers of a short, double-stranded(ds)[RNA-DNA] chimera enable permanent digital detection of dsRNA-specific inputs. The action of ribonuclease III and the binding of an inactive, dsRNA-binding mutant can be permanently recorded by the input-responsive action of a restriction endonuclease that cleaves an ancillary reporter site within the dsDNA segment. The resulting irreversible height change of the arrayed ds[RNA-DNA], as measured by atomic force microscopy, provides a distinct digital output for each dsRNA-specific input. These findings provide the basis for developing imprinting-based bio-nanosensors, and reveal the versatility of AFM as a tool for characterizing the behaviour of highly-crowded biomolecules at the nanoscale.

  16. 3-D Object Recognition from Point Cloud Data

    NASA Astrophysics Data System (ADS)

    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

  17. Optical demodulation system for digitally encoded suspension array in fluoroimmunoassay

    NASA Astrophysics Data System (ADS)

    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.

  18. Digital atlas of fetal brain MRI.

    PubMed

    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.

  19. Archive of digital Boomer seismic reflection data collected during USGS Cruises 94CCT01 and 95CCT01, eastern Texas and western Louisiana, 1994 and 1995

    USGS Publications Warehouse

    Calderon, Karynna; Dadisman, Shawn V.; Kindinger, Jack G.; Flocks, James G.; Morton, Robert A.; Wiese, Dana S.

    2004-01-01

    In June of 1994 and August and September of 1995, the U.S. Geological Survey, in cooperation with the University of Texas Bureau of Economic Geology, conducted geophysical surveys of the Sabine and Calcasieu Lake areas and the Gulf of Mexico offshore eastern Texas and western Louisiana. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, observers' logbooks, GIS information, and formal FGDC metadata. In addition, a filtered and gained GIF image of each seismic profile is provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and in-house (USGS) software for viewing SEG-Y files (Zihlman, 1992) are also provided. Processed profile images, trackline maps, navigation files, and formal metadata may be viewed with a web browser. Scanned handwritten logbooks and Field Activity Collection System (FACS) logs may be viewed with Adobe Reader.

  20. 3D interactive augmented reality-enhanced digital learning systems for mobile devices

    NASA Astrophysics Data System (ADS)

    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.

  1. Effects of compression and individual variability on face recognition performance

    NASA Astrophysics Data System (ADS)

    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

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

    PubMed

    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.

  3. Dichotic and dichoptic digit perception in normal adults.

    PubMed

    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

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

    PubMed

    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.

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

    PubMed Central

    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. PMID:12463786

  6. Inverse scattering approach to improving pattern recognition

    NASA Astrophysics Data System (ADS)

    Chapline, George; Fu, Chi-Yung

    2005-05-01

    The Helmholtz machine provides what may be the best existing model for how the mammalian brain recognizes patterns. Based on the observation that the "wake-sleep" algorithm for training a Helmholtz machine is similar to the problem of finding the potential for a multi-channel Schrodinger equation, we propose that the construction of a Schrodinger potential using inverse scattering methods can serve as a model for how the mammalian brain learns to extract essential information from sensory data. In particular, inverse scattering theory provides a conceptual framework for imagining how one might use EEG and MEG observations of brain-waves together with sensory feedback to improve human learning and pattern recognition. Longer term, implementation of inverse scattering algorithms on a digital or optical computer could be a step towards mimicking the seamless information fusion of the mammalian brain.

  7. Inverse Scattering Approach to Improving Pattern Recognition

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Chapline, G; Fu, C

    2005-02-15

    The Helmholtz machine provides what may be the best existing model for how the mammalian brain recognizes patterns. Based on the observation that the ''wake-sleep'' algorithm for training a Helmholtz machine is similar to the problem of finding the potential for a multi-channel Schrodinger equation, we propose that the construction of a Schrodinger potential using inverse scattering methods can serve as a model for how the mammalian brain learns to extract essential information from sensory data. In particular, inverse scattering theory provides a conceptual framework for imagining how one might use EEG and MEG observations of brain-waves together with sensorymore » feedback to improve human learning and pattern recognition. Longer term, implementation of inverse scattering algorithms on a digital or optical computer could be a step towards mimicking the seamless information fusion of the mammalian brain.« less

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

    PubMed

    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.

  9. Fundamental remote sensing science research program. Part 1: Status report of the mathematical pattern recognition and image analysis project

    NASA Technical Reports Server (NTRS)

    Heydorn, R. D.

    1984-01-01

    The Mathematical Pattern Recognition and Image Analysis (MPRIA) Project is concerned with basic research problems related to the study of the Earth from remotely sensed measurement of its surface characteristics. The program goal is to better understand how to analyze the digital image that represents the spatial, spectral, and temporal arrangement of these measurements for purposing of making selected inference about the Earth.

  10. Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

    NASA Astrophysics Data System (ADS)

    Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y.; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J.; Adatia, Ian

    2016-09-01

    We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p  < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral.

  11. The Heinz Electronic Library Interactive Online System (HELIOS): Building a Digital Archive Using Imaging, OCR, and Natural Language Processing Technologies.

    ERIC Educational Resources Information Center

    Galloway, Edward A.; Michalek, Gabrielle V.

    1995-01-01

    Discusses the conversion project of the congressional papers of Senator John Heinz into digital format and the provision of electronic access to these papers by Carnegie Mellon University. Topics include collection background, project team structure, document processing, scanning, use of optical character recognition software, verification…

  12. Digitization and the Creation of Virtual Libraries: The Princeton University Image Card Catalog--Reaping the Benefits of Imaging.

    ERIC Educational Resources Information Center

    Henthorne, Eileen

    1995-01-01

    Describes a project at the Princeton University libraries that converted the pre-1981 public card catalog, using digital imaging and optical character recognition technology, to fully tagged and indexed records of text in MARC format that are available on an online database and will be added to the online catalog. (LRW)

  13. Adaptive detection of missed text areas in OCR outputs: application to the automatic assessment of OCR quality in mass digitization projects

    NASA Astrophysics Data System (ADS)

    Ben Salah, Ahmed; Ragot, Nicolas; Paquet, Thierry

    2013-01-01

    The French National Library (BnF*) has launched many mass digitization projects in order to give access to its collection. The indexation of digital documents on Gallica (digital library of the BnF) is done through their textual content obtained thanks to service providers that use Optical Character Recognition softwares (OCR). OCR softwares have become increasingly complex systems composed of several subsystems dedicated to the analysis and the recognition of the elements in a page. However, the reliability of these systems is always an issue at stake. Indeed, in some cases, we can find errors in OCR outputs that occur because of an accumulation of several errors at different levels in the OCR process. One of the frequent errors in OCR outputs is the missed text components. The presence of such errors may lead to severe defects in digital libraries. In this paper, we investigate the detection of missed text components to control the OCR results from the collections of the French National Library. Our verification approach uses local information inside the pages based on Radon transform descriptors and Local Binary Patterns descriptors (LBP) coupled with OCR results to control their consistency. The experimental results show that our method detects 84.15% of the missed textual components, by comparing the OCR ALTO files outputs (produced by the service providers) to the images of the document.

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

    PubMed

    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.

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

    PubMed Central

    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

  16. Multi-exemplar affinity propagation.

    PubMed

    Wang, Chang-Dong; Lai, Jian-Huang; Suen, Ching Y; Zhu, Jun-Yong

    2013-09-01

    The affinity propagation (AP) clustering algorithm has received much attention in the past few years. AP is appealing because it is efficient, insensitive to initialization, and it produces clusters at a lower error rate than other exemplar-based methods. However, its single-exemplar model becomes inadequate when applied to model multisubclasses in some situations such as scene analysis and character recognition. To remedy this deficiency, we have extended the single-exemplar model to a multi-exemplar one to create a new multi-exemplar affinity propagation (MEAP) algorithm. This new model automatically determines the number of exemplars in each cluster associated with a super exemplar to approximate the subclasses in the category. Solving the model is NP-hard and we tackle it with the max-sum belief propagation to produce neighborhood maximum clusters, with no need to specify beforehand the number of clusters, multi-exemplars, and superexemplars. Also, utilizing the sparsity in the data, we are able to reduce substantially the computational time and storage. Experimental studies have shown MEAP's significant improvements over other algorithms on unsupervised image categorization and the clustering of handwritten digits.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    Srinivasan, Gopalakrishnan; Sengupta, Abhronil; Roy, Kaushik

    2016-07-13

    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.

  19. Harmful Gas Recognition Exploiting a CTL Sensor Array

    PubMed Central

    Wang, Qihui; Xie, Lijun; Zhu, Bo; Zheng, Yao; Cao, Shihua

    2013-01-01

    In this paper, a novel cataluminescence (CTL)-based sensor array consisting of nine types of catalytic materials is developed for the recognition of several harmful gases, namely carbon monoxide, acetone, chloroform and toluene. First, the experimental setup is constructed by using sensing nanomaterials, a heating plate, a pneumatic pump, a gas flow meter, a digital temperature device, a camera and a BPCL Ultra Weak Chemiluminescence Analyzer. Then, unique CTL patterns for the four types of harmful gas are obtained from the sensor array. The harmful gases are successful recognized by the PCA method. The optimal conditions are also investigated. Finally, experimental results show high sensitivity, long-term stability and good linearity of the sensor array, which combined with simplicity, make our system a promising application in this field. PMID:24113681

  20. The Interaction between Central and Peripheral Processing in Chinese Handwritten Production: Evidence from the Effect of Lexicality and Radical Complexity

    PubMed Central

    Zhang, Qingfang; Feng, Chen

    2017-01-01

    The interaction between central and peripheral processing in written word production remains controversial. This study aims to investigate whether the effects of radical complexity and lexicality in central processing cascade into peripheral processing in Chinese written word production. The participants were asked to write characters and non-characters (lexicality) with different radical complexity (few- and many-strokes). The findings indicated that regardless of the lexicality, the writing latencies were longer for characters with higher complexity (the many-strokes condition) than for characters with lower complexity (the few-strokes condition). The participants slowed down their writing execution at the radicals' boundary strokes, which indicated a radical boundary effect in peripheral processing. Interestingly, the lexicality and the radical complexity affected the pattern of shift velocity and writing velocity during the execution of writing. Lexical processing cascades into peripheral processing but only at the beginning of Chinese characters. In contrast, the radical complexity influenced the execution of handwriting movement throughout the entire character, and the pattern of the effect interacted with the character frequency. These results suggest that the processes of the lexicality and the radical complexity function during the execution of handwritten word production, which suggests that central processing cascades over peripheral processing during Chinese characters handwriting. PMID:28348536

  1. [The monodigital recognition test (MRT)--a sensitivity-specific variant of Moberg's pick-up test].

    PubMed

    Clemens, R

    1979-01-01

    The Test for Mono-digital Recognition (MRT) is a procedure specifically designed for the measurement of gnostic performance. The new test was derived from MOBERG's Pick-up Test with the aim of eliminating its shortcomings. Additional injuries do not restrict the applicability of the MRT and the test is not confined to the skin area supplied by the median nerve. The MRT expresses the degree of gnostic disturbances by means of a point system. For testing a patient only two to four minutes are needed.

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

    NASA Astrophysics Data System (ADS)

    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.

  3. New directions in medical e-curricula and the use of digital repositories.

    PubMed

    Fleiszer, David M; Posel, Nancy H; Steacy, Sean P

    2004-03-01

    Medical educators involved in the growth of multimedia-enhanced e-curricula are increasingly aware of the need for digital repositories to catalogue, store and ensure access to learning objects that are integrated within their online material. The experience at the Faculty of Medicine at McGill University during initial development of a mainstream electronic curriculum reflects this growing recognition that repositories can facilitate the development of a more comprehensive as well as effective electronic curricula. Also, digital repositories can help to ensure efficient utilization of resources through the use, re-use, and reprocessing of multimedia learning, addressing the potential for collaboration among repositories and increasing available material exponentially. The authors review different approaches to the development of a digital repository application, as well as global and specific issues that should be examined in the initial requirements definition and development phase, to ensure current initiatives meet long-term requirements. Often, decisions regarding creation of e-curricula and associated digital repositories are left to interested faculty and their individual development teams. However, the development of an e-curricula and digital repository is not predominantly a technical exercise, but rather one that affects global pedagogical strategies and curricular content and involves a commitment of large-scale resources. Outcomes of these decisions can have long-term consequences and as such, should involve faculty at the highest levels including the dean.

  4. Hough transform for human action recognition

    NASA Astrophysics Data System (ADS)

    Siemon, Mia S. N.

    2016-09-01

    Nowadays, the demand of computer analysis, especially regarding team sports, continues drastically growing. More and more decisions are made by electronic devices for the live to become `easier' to a certain context. There already exist application areas in sports, during which critical situations are being handled by means of digital software. This paper aims at the evaluation and introduction to the necessary foundation which would make it possible to develop a concept similar to that of `hawk-eye', a decision-making program to evaluate the impact of a ball with respect to a target line and to apply it to the sport of volleyball. The pattern recognition process is in this case performed by means of the mathematical model of Hough transform which is able of identifying relevant lines and circles in the image in order to later on use them for the necessary evaluation of the image and the decision-making process.

  5. Evaluation of Local Media Surveillance for Improved Disease Recognition and Monitoring in Global Hotspot Regions

    PubMed Central

    Schwind, Jessica S.; Wolking, David J.; Brownstein, John S.; Mazet, Jonna A. K.; Smith, Woutrina A.

    2014-01-01

    Digital disease detection tools are technologically sophisticated, but dependent on digital information, which for many areas suffering from high disease burdens is simply not an option. In areas where news is often reported in local media with no digital counterpart, integration of local news information with digital surveillance systems, such as HealthMap (Boston Children’s Hospital), is critical. Little research has been published in regards to the specific contribution of local health-related articles to digital surveillance systems. In response, the USAID PREDICT project implemented a local media surveillance (LMS) pilot study in partner countries to monitor disease events reported in print media. This research assessed the potential of LMS to enhance digital surveillance reach in five low- and middle-income countries. Over 16 weeks, select surveillance system attributes of LMS, such as simplicity, flexibility, acceptability, timeliness, and stability were evaluated to identify strengths and weaknesses in the surveillance method. Findings revealed that LMS filled gaps in digital surveillance network coverage by contributing valuable localized information on disease events to the global HealthMap database. A total of 87 health events were reported through the LMS pilot in the 16-week monitoring period, including 71 unique reports not found by the HealthMap digital detection tool. Furthermore, HealthMap identified an additional 236 health events outside of LMS. It was also observed that belief in the importance of the project and proper source selection from the participants was crucial to the success of this method. The timely identification of disease outbreaks near points of emergence and the recognition of risk factors associated with disease occurrence continue to be important components of any comprehensive surveillance system for monitoring disease activity across populations. The LMS method, with its minimal resource commitment, could be one tool used

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

    PubMed

    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.

  7. [Prosopagnosia and facial expression recognition].

    PubMed

    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.

  8. Incorporating Speech Recognition into a Natural User Interface

    NASA Technical Reports Server (NTRS)

    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

  9. Computer Recognition of Facial Profiles

    DTIC Science & Technology

    1974-08-01

    facial recognition 20. ABSTRACT (Continue on reverse side It necessary and Identify by block number) A system for the recognition of human faces from...21 2.6 Classification Algorithms ........... ... 32 III FACIAL RECOGNITION AND AUTOMATIC TRAINING . . . 37 3.1 Facial Profile Recognition...provide a fair test of the classification system. The work of Goldstein, Harmon, and Lesk [81 indicates, however, that for facial recognition , a ten class

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

    PubMed

    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.

  11. Fragrant pear sexuality recognition with machine vision

    NASA Astrophysics Data System (ADS)

    Ma, Benxue; Ying, Yibin

    2006-10-01

    In this research, a method to identify Kuler fragrant pear's sexuality with machine vision was developed. Kuler fragrant pear has male pear and female pear. They have an obvious difference in favor. To detect the sexuality of Kuler fragrant pear, images of fragrant pear were acquired by CCD color camera. Before feature extraction, some preprocessing is conducted on the acquired images to remove noise and unnecessary contents. Color feature, perimeter feature and area feature of fragrant pear bottom image were extracted by digital image processing technique. And the fragrant pear sexuality was determined by complexity obtained from perimeter and area. In this research, using 128 Kurle fragrant pears as samples, good recognition rate between the male pear and the female pear was obtained for Kurle pear's sexuality detection (82.8%). Result shows this method could detect male pear and female pear with a good accuracy.

  12. Phonological mismatch makes aided speech recognition in noise cognitively taxing.

    PubMed

    Rudner, Mary; Foo, Catharina; Rönnberg, Jerker; Lunner, Thomas

    2007-12-01

    The working memory framework for Ease of Language Understanding predicts that speech processing becomes more effortful, thus requiring more explicit cognitive resources, when there is mismatch between speech input and phonological representations in long-term memory. To test this prediction, we changed the compression release settings in the hearing instruments of experienced users and allowed them to train for 9 weeks with the new settings. After training, aided speech recognition in noise was tested with both the trained settings and orthogonal settings. We postulated that training would lead to acclimatization to the trained setting, which in turn would involve establishment of new phonological representations in long-term memory. Further, we postulated that after training, testing with orthogonal settings would give rise to phonological mismatch, associated with more explicit cognitive processing. Thirty-two participants (mean=70.3 years, SD=7.7) with bilateral sensorineural hearing loss (pure-tone average=46.0 dB HL, SD=6.5), bilaterally fitted for more than 1 year with digital, two-channel, nonlinear signal processing hearing instruments and chosen from the patient population at the Linköping University Hospital were randomly assigned to 9 weeks training with new, fast (40 ms) or slow (640 ms), compression release settings in both channels. Aided speech recognition in noise performance was tested according to a design with three within-group factors: test occasion (T1, T2), test setting (fast, slow), and type of noise (unmodulated, modulated) and one between-group factor: experience setting (fast, slow) for two types of speech materials-the highly constrained Hagerman sentences and the less-predictable Hearing in Noise Test (HINT). Complex cognitive capacity was measured using the reading span and letter monitoring tests. PREDICTION: We predicted that speech recognition in noise at T2 with mismatched experience and test settings would be associated with more

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

    NASA Astrophysics Data System (ADS)

    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.

  14. Rapid cognitive screening in multiple sclerosis accomplished by the Free Recall and Recognition Test.

    PubMed

    Claesson, I M; Ytterberg, C; Johansson, S; Almkvist, O; von Koch, L

    2007-03-01

    This study sought to investigate the feasibility of the Free Recall and Recognition Test (FRRT) as a practical screening tool for cognitive impairment in multiple sclerosis (MS). Persons with MS (n = 227) were consecutively recruited and assessed with four cognitive tests; FRRT, Symbol Digit Modalities Test (SDMT), Paced Auditory Serial Addition Test (PASAT), and the Mini-Mental State Examination (MMSE). Disease severity was assessed by the Expanded Disability Status Scale (EDSS). The FRRT, which was completed by 99% of the cohort in approximately 5 minutes per assessment, correlated significantly with the other cognitive tests, as well as with the disease severity rating. A cut-off of 4 for the FRRT recall rendered 90% sensitivity and 25% specificity, and a cut-off of 4.2 for the FRRT recognition resulted in 70% sensitivity and 51% specificity. We conclude that the FRRT proved feasible as a practical screening tool for cognitive impairment in MS within a clinical setting.

  15. Automated Coronal Loop Identification Using Digital Image Processing Techniques

    NASA Technical Reports Server (NTRS)

    Lee, Jong K.; Gary, G. Allen; Newman, Timothy S.

    2003-01-01

    The results of a master thesis project on a study of computer algorithms for automatic identification of optical-thin, 3-dimensional solar coronal loop centers from extreme ultraviolet and X-ray 2-dimensional images will be presented. These center splines are proxies of associated magnetic field lines. The project is pattern recognition problems in which there are no unique shapes or edges and in which photon and detector noise heavily influence the images. The study explores extraction techniques using: (1) linear feature recognition of local patterns (related to the inertia-tensor concept), (2) parametric space via the Hough transform, and (3) topological adaptive contours (snakes) that constrains curvature and continuity as possible candidates for digital loop detection schemes. We have developed synthesized images for the coronal loops to test the various loop identification algorithms. Since the topology of these solar features is dominated by the magnetic field structure, a first-order magnetic field approximation using multiple dipoles provides a priori information in the identification process. Results from both synthesized and solar images will be presented.

  16. Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology

    PubMed Central

    Bu, Qirong; Liu, Feihong; Zhang, Min; Ren, Yu; Lv, Yi

    2018-01-01

    Inspired by gestalt psychology, we combine human cognitive characteristics with knowledge of radiologists in medical image analysis. In this paper, a novel framework is proposed to detect breast masses in digitized mammograms. It can be divided into three modules: sensation integration, semantic integration, and verification. After analyzing the progress of radiologist's mammography screening, a series of visual rules based on the morphological characteristics of breast masses are presented and quantified by mathematical methods. The framework can be seen as an effective trade-off between bottom-up sensation and top-down recognition methods. This is a new exploratory method for the automatic detection of lesions. The experiments are performed on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) data sets. The sensitivity reached to 92% at 1.94 false positive per image (FPI) on MIAS and 93.84% at 2.21 FPI on DDSM. Our framework has achieved a better performance compared with other algorithms. PMID:29854359

  17. Profiles of Discourse Recognition

    ERIC Educational Resources Information Center

    Singer, Murray

    2013-01-01

    A discourse recognition theory derived from more general memory formulations would be broad in its psychological implications. This study compared discourse recognition with some established profiles of item recognition. Participants read 10 stories either once or twice each. They then rated their confidence in recognizing explicit, paraphrased,…

  18. Sudden Event Recognition: A Survey

    PubMed Central

    Suriani, Nor Surayahani; Hussain, Aini; Zulkifley, Mohd Asyraf

    2013-01-01

    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. PMID:23921828

  19. Oxycodone Ingestion Patterns in Acute Fracture Pain With Digital Pills.

    PubMed

    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

  20. Examining the Relationships among Item Recognition, Source Recognition, and Recall from an Individual Differences Perspective

    ERIC Educational Resources Information Center

    Unsworth, Nash; Brewer, Gene A.

    2009-01-01

    The authors of the current study examined the relationships among item-recognition, source-recognition, free recall, and other memory and cognitive ability tasks via an individual differences analysis. Two independent sources of variance contributed to item-recognition and source-recognition performance, and these two constructs related…

  1. The Recognition Memory Test Examination of ethnic differences and norm validity.

    PubMed

    O'Bryant, Sid E; Hilsabeck, Robin C; McCaffrey, Robert J; Drew Gouvier, Wm

    2003-03-01

    The possibility of racial bias in neuropsychological test materials has received increasing attention in recent years. The purpose of the present study was to investigate whether an own-race recognition bias would provide an advantage for Caucasian participants over African American participants on the Faces subtest of the Recognition Memory Test (RMT). Thirty Caucasian and 30 African American undergraduates completed the RMT, Shipley Institute of Living Scale (SILS), and Symbol Digit Modalities Test (SDMT). No significant group difference was found on RMT Faces. However, mean RMT Faces scores for both groups were below the 10th percentile in spite of average scores on the SDMT and SILS. A second study was conducted to further examine the validity of the RMT norms for this age range (i.e., 18-24) and to provide 2-week test-retest reliabilities. The mean RMT Faces subtest score was 39.78 (10th percentile), and 28% of the sample scored at or below the fifth percentile. Test-retest reliabilities were.63 and.64 for RMT Words and Faces, respectively. Results of these studies suggest that re-examination of the current norms for RMT Faces is warranted for adults aged.

  2. Digital Natives or Digital Tribes?

    ERIC Educational Resources Information Center

    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…

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

    PubMed

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

  5. Archive of digital Boomer and Chirp seismic reflection data collected during USGS Cruises 01RCE05 and 02RCE01 in the Lower Atchafalaya River, Mississippi River Delta, and offshore southeastern Louisiana, October 23-30, 2001, and August 18-19, 2002

    USGS Publications Warehouse

    Calderon, Karynna; Dadisman, Shawn V.; Kindinger, Jack G.; Flocks, James G.; Ferina, Nicholas F.; Wiese, Dana S.

    2004-01-01

    In October of 2001 and August of 2002, the U.S. Geological Survey conducted geophysical surveys of the Lower Atchafalaya River, the Mississippi River Delta, Barataria Bay, and the Gulf of Mexico south of East Timbalier Island, Louisiana. This report serves as an archive of unprocessed digital marine seismic reflection data, trackline maps, navigation files, observers' logbooks, GIS information, and formal FGDC metadata. In addition, a filtered and gained GIF image of each seismic profile is provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and othes, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and in-house (USGS) software for viewing SEG-Y files (Zihlman, 1992) are also provided. Processed profile images, trackline maps, navigation files, and formal metadata may be viewed with a web browser. Scanned handwritten logbooks and Field Activity Collection System (FACS) logs may be viewed with Adobe Reader.

  6. Left ear dichotic listening performance on consonant-vowel combinations and digits in subtypes of reading-disabled children.

    PubMed

    Morton, L L; Siegel, L S

    1991-02-01

    Twenty reading comprehension-disabled (CD) and 20 reading comprehension and word recognition-disabled (CWRD), right-handed male children were matched with 20 normal-achieving age-matched controls and 20 normal-achieving reading level-matched controls and tested for left ear report on dichotic listening tasks using digits and consonant-vowel combinations (CVs). Left ear report for CVs and digits did not correlate for any of the groups. Both reading-disabled groups showed lower left ear report on digits. On CVs the CD group showed a high left ear report but only when there were no priming precursors, such as directions to attend right first and to process digits first. Priming effects interfered with the processing of both digits and CVs. Theoretically, the CWRD group seems to be characterized by a depressed right hemisphere, whereas the CD group may have a more labile right hemisphere, perhaps tending to overengagement for CV tasks but vulnerable to situational precursors in the form of priming effects. Implications extend to (1) subtyping practices in research with the learning-disabled, (2) inferences drawn from studies using different dichotic stimuli, and (3) the neuropsychology of reading disorders.

  7. The Legal Recognition of Sign Languages

    ERIC Educational Resources Information Center

    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…

  8. Citrus fruit recognition using color image analysis

    NASA Astrophysics Data System (ADS)

    Xu, Huirong; Ying, Yibin

    2004-10-01

    An algorithm for the automatic recognition of citrus fruit on the tree was developed. Citrus fruits have different color with leaves and branches portions. Fifty-three color images with natural citrus-grove scenes were digitized and analyzed for red, green, and blue (RGB) color content. The color characteristics of target surfaces (fruits, leaves, or branches) were extracted using the range of interest (ROI) tool. Several types of contrast color indices were designed and tested. In this study, the fruit image was enhanced using the (R-B) contrast color index because results show that the fruit have the highest color difference among the objects in the image. A dynamic threshold function was derived from this color model and used to distinguish citrus fruit from background. The results show that the algorithm worked well under frontlighting or backlighting condition. However, there are misclassifications when the fruit or the background is under a brighter sunlight.

  9. Basic test framework for the evaluation of text line segmentation and text parameter extraction.

    PubMed

    Brodić, Darko; Milivojević, Dragan R; Milivojević, Zoran

    2010-01-01

    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.

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

    PubMed Central

    Brodić, Darko; Milivojević, Dragan R.; Milivojević, Zoran

    2010-01-01

    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. PMID:22399932

  11. 3D digitization methods based on laser excitation and active triangulation: a comparison

    NASA Astrophysics Data System (ADS)

    Aubreton, Olivier; Mériaudeau, Fabrice; Truchetet, Frédéric

    2016-04-01

    3D reconstruction of surfaces is an important topic in computer vision and corresponds to a large field of applications: industrial inspection, reverse engineering, object recognition, biometry, archeology… Because of the large varieties of applications, one can find in the literature a lot of approaches which can be classified into two families: passive and active [1]. Certainly because of their reliability, active approaches, using imaging system with an additional controlled light source, seem to be the most commonly used in the industrial field. In this domain, the 3D digitization approach based on active 3D triangulation has had important developments during the last ten years [2] and seems to be mature today if considering the important number of systems proposed by manufacturers. Unfortunately, the performances of active 3D scanners depend on the optical properties of the surface to digitize. As an example, on Fig 1.a, a 3D shape with a diffuse surface has been digitized with Comet V scanner (Steinbichler). The 3D reconstruction is presented on Fig 1.b. The same experiment was carried out on a similar object (same shape) but presenting a specular surface (Fig 1.c and Fig 1.d) ; it can clearly be observed, that the specularity influences of the performance of the digitization.

  12. Toward open set recognition.

    PubMed

    Scheirer, Walter J; de Rezende Rocha, Anderson; Sapkota, Archana; Boult, Terrance E

    2013-07-01

    To date, almost all experimental evaluations of machine learning-based recognition algorithms in computer vision have taken the form of "closed set" recognition, whereby all testing classes are known at training time. A more realistic scenario for vision applications is "open set" recognition, where incomplete knowledge of the world is present at training time, and unknown classes can be submitted to an algorithm during testing. This paper explores the nature of open set recognition and formalizes its definition as a constrained minimization problem. The open set recognition problem is not well addressed by existing algorithms because it requires strong generalization. As a step toward a solution, we introduce a novel "1-vs-set machine," which sculpts a decision space from the marginal distances of a 1-class or binary SVM with a linear kernel. This methodology applies to several different applications in computer vision where open set recognition is a challenging problem, including object recognition and face verification. We consider both in this work, with large scale cross-dataset experiments performed over the Caltech 256 and ImageNet sets, as well as face matching experiments performed over the Labeled Faces in the Wild set. The experiments highlight the effectiveness of machines adapted for open set evaluation compared to existing 1-class and binary SVMs for the same tasks.

  13. Digital rights language support for evolving digital cinema requirements

    NASA Astrophysics Data System (ADS)

    Orri, Xavier; Mas, Joan-Maria; Macq, Benoit M. M.

    2003-06-01

    Digital cinema can be defined as the digital electronic distribution and display of theatrical film content or live material to the theatre. However, this simple definition does not reflect the complex mesh of balanced business relationships between the different parties involved in the worldwide cinematic distribution. Its transition from an analogue to a digital business will happen progressively, starting with only content being digitally distributed and going until an all-digital business. From these facts derive strong requirements on digital rights management (DRM) systems for digital cinema and therefore on the digital rights language supporting it. This paper explores the requirements imposed by the cinematic distribution model and by its progressive transition to digital, and the impact these have on digital rights languages. We analyze the support provided by different digital rights languages, identifying weaknesses and exploring solutions in fulfilling the requirements of the digital cinema distribution model.

  14. Innate Pattern Recognition and Categorization in a Jumping Spider

    PubMed Central

    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

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

    PubMed

    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.

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

    PubMed

    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.

  17. Real-time implementation of electromyogram pattern recognition as a control command of man-machine interface.

    PubMed

    Chang, G C; Kang, W J; Luh, J J; Cheng, C K; Lai, J S; Chen, J J; Kuo, T S

    1996-10-01

    The purpose of this study was to develop a real-time electromyogram (EMG) discrimination system to provide control commands for man-machine interface applications. A host computer with a plug-in data acquisition and processing board containing a TMS320 C31 floating-point digital signal processor was used to attain real-time EMG classification. Two-channel EMG signals were collected by two pairs of surface electrodes located bilaterally between the sternocleidomastoid and the upper trapezius. Five motions of the neck and shoulders were discriminated for each subject. The zero-crossing rate was employed to detect the onset of muscle contraction. The cepstral coefficients, derived from autoregressive coefficients and estimated by a recursive least square algorithm, were used as the recognition features. These features were then discriminated using a modified maximum likelihood distance classifier. The total response time of this EMG discrimination system was achieved about within 0.17 s. Four able bodied and two C5/6 quadriplegic subjects took part in the experiment, and achieved 95% mean recognition rate in discrimination between the five specific motions. The response time and the reliability of recognition indicate that this system has the potential to discriminate body motions for man-machine interface applications.

  18. A digital acquisition and elaboration system for nuclear fast pulse detection

    NASA Astrophysics Data System (ADS)

    Esposito, B.; Riva, M.; Marocco, D.; Kaschuck, Y.

    2007-03-01

    A new digital acquisition and elaboration system has been developed and assembled in ENEA-Frascati for the direct sampling of fast pulses from nuclear detectors such as scintillators and diamond detectors. The system is capable of performing the digital sampling of the pulses (200 MSamples/s, 14-bit) and the simultaneous (compressed) data transfer for further storage and software elaboration. The design (FPGA-based) is oriented to real-time applications and has been developed in order to allow acquisition with no loss of pulses and data storage for long-time intervals (tens of s at MHz pulse count rates) without the need of large on-board memory. A dedicated pulse analysis software, written in LabVIEWTM, performs the treatment of the acquired pulses, including pulse recognition, pile-up rejection, baseline removal, pulse shape particle separation and pulse height spectra analysis. The acquisition and pre-elaboration programs have been fully integrated with the analysis software.

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

    NASA Astrophysics Data System (ADS)

    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.

  20. Automated thematic mapping and change detection of ERTS-A images. [digital interpretation of Arizona imagery

    NASA Technical Reports Server (NTRS)

    Gramenopoulos, N. (Principal Investigator)

    1973-01-01

    The author has identified the following significant results. For the recognition of terrain types, spatial signatures are developed from the diffraction patterns of small areas of ERTS-1 images. This knowledge is exploited for the measurements of a small number of meaningful spatial features from the digital Fourier transforms of ERTS-1 image cells containing 32 x 32 picture elements. Using these spatial features and a heuristic algorithm, the terrain types in the vicinity of Phoenix, Arizona were recognized by the computer with a high accuracy. Then, the spatial features were combined with spectral features and using the maximum likelihood criterion the recognition accuracy of terrain types increased substantially. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. Nonlinear transformations of the feature vectors are required so that the terrain class statistics become approximately Gaussian. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month but vary substantially between seasons.