patternrecognition with computer systems presents a great challenge to researchers in computer science. In some cases patterns can be differentiated by their silhouette and recognized through the contour around its s...
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Human identification by recognizing the spontaneous gait recorded in real-world setting is a tough and not yet fully resolved problem in biometrics research. Several issues have contributed to the difficulties of this...
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ISBN:
(纸本)9781424447558
Human identification by recognizing the spontaneous gait recorded in real-world setting is a tough and not yet fully resolved problem in biometrics research. Several issues have contributed to the difficulties of this task. they include various poses, different clothes, moderate to large changes of normal walking manner due to carrying diverse goods when walking, and the uncertainty of the environments where the people are walking. In order to achieve a better gait recognition, this paper proposes a new method based on Weighted Binary pattern (WBP). WBP first constructs binary pattern from a sequence of aligned silhouettes. then, adaptive weighting technique is applied to discriminate significances of the bits in gait signatures. Being compared with most of existing methods in the literatures, this method can better deal with gait frequency, local spatial-temporal human pose features, and global body shape statistics. the proposed method is validated on several well known benchmark databases. the extensive and encouraging experimental results show that the proposed algorithm achieves high accuracy, but with low complexity and computational time.
Electromyographic control systems, based on patternrecognition, have become an established technique in upper limb prosthetic control. this paper describes the development of a control system that uses pattern inform...
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ISBN:
(纸本)9780889867888
Electromyographic control systems, based on patternrecognition, have become an established technique in upper limb prosthetic control. this paper describes the development of a control system that uses pattern information from surface electromyographic signals to control a grip posture of a prosthetic hand. A different hand grip posture is discriminated using fuzzy logic by processing the surface electromyographic from wrist muscles performed at different speeds of contraction. A moving data window of two hundred values is applied to the surface electromyographic data and a new method called moving approximate entropy is used to extract information from the signals. the analyses show differences at three states of contraction (start, middle and end). Also, significant differences were determined at different speeds of contractions. Mean absolute value is also used in the extraction process to increase the performance of the system. the extracted features were then fed to the fuzzy logic classifier and the output is selected appropriately. the experimental result demonstrates the ability of the system to classify the features related to different grip postures.
this PDF file contains the front matter associated with SPIE Proceedings Volume 7496, including the Title Page, Copyright information, Table of Contents, and the conference Committee listing. 69; 2009 Copyright SPI...
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In this paper, we propose a supervision method which aims at determining pertinent indicators to optimize predictive maintenance strategies. the supervision method, based on the AUto-adaptative and Dynamical Clusterin...
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ISBN:
(纸本)9789896740009
In this paper, we propose a supervision method which aims at determining pertinent indicators to optimize predictive maintenance strategies. the supervision method, based on the AUto-adaptative and Dynamical Clustering technique (AUDyC), consists in classifying in real time measured data into classes representative of the operating modes of the process. this technique also allows the detection and the tracking of the slow evolutions of the process modes. Based on the AUDyC technique, a method is proposed to estimate the probabilities of the failure occurence of components in real time. this method is illustrated on the real case of a temperature controller.
the paper considers the problem of classification error in patternrecognition. this model of classification is primarily based oil the Bayes rule and secondarily on the notion of intuitionistic fuzzy sets. A probabil...
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ISBN:
(纸本)9783642026102
the paper considers the problem of classification error in patternrecognition. this model of classification is primarily based oil the Bayes rule and secondarily on the notion of intuitionistic fuzzy sets. A probability of misclassifications is derived for a classifier under the assumption that the features are class-conditionally statistically independent, and we have intuitionistic fuzzy information on object features instead of exact information. Additionally, a probability of the intuitionistic fuzzy event is represented by the real number. Numerical example concludes the work.
Traditional kernelised classification methods Could not perforin well sometimes because of the using of a single and fixed kernel, especially oil sonic complicated data sets. In this paper. a novel optimal double-kern...
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ISBN:
(纸本)9783642030697
Traditional kernelised classification methods Could not perforin well sometimes because of the using of a single and fixed kernel, especially oil sonic complicated data sets. In this paper. a novel optimal double-kernel combination (ODKC) method is proposed for complicated classification tasks. Firstly, data sets are mapped by two basic kernels into different feature spaces respectively, and then three kinds of optimal composite kernels are constructed by integrating information of the two feature spaces. Comparative experiments demonstrate the effectiveness of our methods.
the proceedings contain 96 papers. the topics discussed include: night-time traffic surveillance: a robust framework for multi-vehicle detection, classification and tracking;vehicle tracking using projective particle ...
ISBN:
(纸本)9780769537184
the proceedings contain 96 papers. the topics discussed include: night-time traffic surveillance: a robust framework for multi-vehicle detection, classification and tracking;vehicle tracking using projective particle filter;comparative evaluation of stationary foreground object detection algorithms based on background subtraction techniques;creating human activity recognition systems using Pareto-based multiobjective optimization;automatic gait recognition using weighted binary pattern on video;incremental EM for probabilistic latent semantic analysis on human action recognition;towards generic detection of unusual events in video surveillance;a people counting system based on face detection and tracking in a video;a pruning approach improving face identification systems;emerging trends in persistent surveillance information fusion;human body articulation for action recognition in video sequences;and multisensor fusion for monitoring elderly activities at home.
Data from the elemental composition-ratios and experimental prompt gamma spectra of samples were used to develop suitable discriminant classes for suspect samples. the collected data and gamma spectra were applied to ...
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Data from the elemental composition-ratios and experimental prompt gamma spectra of samples were used to develop suitable discriminant classes for suspect samples. the collected data and gamma spectra were applied to principal component analysis (PCA) to discriminate explosives from non-explosive materials. (C) 2009 Elsevier Ltd. All rights reserved.
Syntactic methods in patternrecognition have been used extensively in bioinformatics, and in particular, in the analysis of gene and protein expressions, and in the recognition and classification of biosequences, the...
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ISBN:
(纸本)9783642040306
Syntactic methods in patternrecognition have been used extensively in bioinformatics, and in particular, in the analysis of gene and protein expressions, and in the recognition and classification of biosequences, these methods are almost universally distance-based. this paper concerns the use of an Optimal and Information theoretic (OIT) probabilistic model [11] to achieve peptide classification using the information residing in their syntactic representations. the latter has traditionally been achieved using the edit distances required in the respective peptide comparisons. We advocate that, one can model the differences between compared strings as a mutation model consisting of random Substitutions, Insertions and Deletions (SID) obeying the OIT model. thus, in this paper, we show that the probability measure obtained. from the OIT model can be perceived as a sequence similarity metric, using which a Support Vector Machine (SVM)-based peptide classifier, referred to as OIT-SVM, can be devised. the classifier, which we have built has been tested for eight different "substitution" matrices and for two different data sets, namely, the HIV-1 Protease Cleavage sites and the T-cell Epitopes. the results show that the OIT model performs significantly better than the one which uses a Needleman-Wunsch sequence alignment score, and the peptide classification methods that previously experimented withthe same two datasets.
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