the problem of Writer Verification is to make a decision of whether or not two handwritten documents are written by the same person. Providing a strength of evidence for any such decision is an integral part of the wr...
ISBN:
(纸本)9780769528229
the problem of Writer Verification is to make a decision of whether or not two handwritten documents are written by the same person. Providing a strength of evidence for any such decision is an integral part of the writer verification problem. the strength of evidence should incorporate (i) the amount of information compared in each of the two documents (line/half page/full page etc.), (ii) the nature of content present in the document (same/different content), (iii) Features used for comparison and (iv) the error rate of the model used for making the decision. this paper describes the statistical model used for writer verification and also introduces a mathematical formulation to include the above four mentioned parameters, for calculating strength of evidence of same/different writer the statistical model uses Gamma and Gaussian densities to parametrically model the distance space distribution arising from comparing ensemble of pairs of documents. the strength of evidence is mapped to a 9-point qualitative scale for the decision;one that is often used by questioned document examiners. Experiments and results show that with increase in information content from just a single word to a full page of document, the verification accuracy of the model increases.
this Volume 1 of the conferenceproceedings contains 108 papers. Topics discussed include fuzzy relations, robots and optimization, decision making, fuzzy modeling, math, data analysis, control, soft computing applica...
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this Volume 1 of the conferenceproceedings contains 108 papers. Topics discussed include fuzzy relations, robots and optimization, decision making, fuzzy modeling, math, data analysis, control, soft computing applications to intelligent manufacturing and fault diagnosis, intelligent vision and sensor fusion, fuzzy relations, patternrecognition, data mining, recurrent fuzzy neural networks, tuning fuzzy controllers via adaptive critic based approximate dynamic programming, fuzzy artificial intelligence, soft computing applications to intelligent manufacturing and fault diagnosis and intelligent communications.
Essential information about the differential structure of smooth surfaces is embodied in their principal-curvature and principal-direction fields. A particular type of fibre bundle, a frame bundle, is defined over est...
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ISBN:
(纸本)0818608781
Essential information about the differential structure of smooth surfaces is embodied in their principal-curvature and principal-direction fields. A particular type of fibre bundle, a frame bundle, is defined over estimated surface trace points and the computation of these fields is presented as the determination of the appropriate cross section through the frame bundle. Any approach to the recovery of surface structure must deal withthe inherent limitations of local computations--an iterative constraint-satisfaction process is developed for the reliable extraction of the principal-direction cross section. Present methods are applied to the synthetic images corrupted by noise and to magnetic resonance imagery.
People with hearing impairment use sign language for communication. they use hand gestures to represent numbers, letters, words and sentences, which allows them to communicate among themselves. the problem arises when...
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ISBN:
(纸本)9781538644300
People with hearing impairment use sign language for communication. they use hand gestures to represent numbers, letters, words and sentences, which allows them to communicate among themselves. the problem arises when they need to interact with other people. An automation system that can convert sign language to text will make the interaction easier. Recently, many such systems for sign language recognition have been developed. But most of them were executed using laptop and computers, which are impractical to carry due to their weight and size. this article is based on the design and implementation of an Android application which converts the American Sign Language to text, so that it can be used anywhere and anytime. Image is captured by the smart phone camera and skin segmentation is done using YCbCr systems. Features are extracted from the image using HOG and classified to recognize the sign. the classification is done using Support Vector Machine (SVM).
GDPLL(k) grammars have been introduced as a tool for the construction of syntactic patternrecognition-based systems. the grammars have been successfully used in several different applications. the practical experienc...
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ISBN:
(纸本)9783319591629
GDPLL(k) grammars have been introduced as a tool for the construction of syntactic patternrecognition-based systems. the grammars have been successfully used in several different applications. the practical experience withthe implementation of a syntactic patternrecognition system based on GDPLL(k) grammars has served to define methodological guidelines for constructing such systems. In the paper key methodological issues are presented.
We consider a multi-class patternrecognition problem with linearly ordered labels and a loss function, which measures absolute deviations of decisions from true classes. In the bayesian setting the optimal decision r...
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ISBN:
(纸本)9783540695721
We consider a multi-class patternrecognition problem with linearly ordered labels and a loss function, which measures absolute deviations of decisions from true classes. In the bayesian setting the optimal decision rule is shown to be the median of a posteriori class probabilities. then, we propose three approaches to constructing an empirical decision rule, based on a learning sequence. Our starting point is the Parzen-Rosenblatt kernel density estimator. the second and the third approach are based on radial bases functions (RBF) nets estimators of class densities.
Software security patterns are a proven solution for recurring security problems. Security pattern catalogs are increasing rapidly. this creates difficulty in selecting appropriate software security patterns for a par...
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this book constitutes the refereed proceedings of the 4thinternationalconference on patternrecognition and Machine Intelligence, PReMI 2011, held in Moscow, Russia in June/July 2011. the 65 revised papers presented...
ISBN:
(数字)9783642217869
ISBN:
(纸本)9783642217852
this book constitutes the refereed proceedings of the 4thinternationalconference on patternrecognition and Machine Intelligence, PReMI 2011, held in Moscow, Russia in June/July 2011. the 65 revised papers presented together with 5 invited talks were carefully reviewed and selected from 140 submissions. the papers are organized in topical sections on patternrecognition and machine learning; image analysis; image and video information retrieval; natural language processing and text and data mining; watermarking, steganography and biometrics; soft computing and applications; clustering and network analysis; bio and chemo analysis; and document image processing.
Traffic Sign recognition is very crucial for self-driving cars and Advanced Driver Assistance Systems. As the vehicle moves within a region or across regions, it encounters a variety of signs which needs to be recogni...
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ISBN:
(纸本)9789897583971
Traffic Sign recognition is very crucial for self-driving cars and Advanced Driver Assistance Systems. As the vehicle moves within a region or across regions, it encounters a variety of signs which needs to be recognized with very high accuracy. It is generally observed that traffic signs have large intra-class variability and small inter-class variability. this makes visual distinguishability between distinct classes extremely irregular. In this paper we propose a hierarchical classifier in which the number of coarse classes is automatically determined. this gives the advantage of dedicated classifiers trained for classes which are more difficult to distinguish. this is an application oriented work which involves systematic and intelligent combination of machine learning and computer vision based algorithms with required modifications for designing fully automated hierarchical classification framework for traffic sign recognition. the proposed solution is a real-time scalable machine learning based approach which can efficiently take care of wide intra-class variations without extracting desired handcrafted features beforehand. It eliminates the need for manually observing and grouping relevant features, thereby reducing human time and efforts. the classifier performance accuracy is surpassing the accuracy achieved by humans on publicly available GTSRB traffic sign dataset with lesser parameters than the existing solutions.
Individuals classification and recognition processes are a substantial growing field in many industry fields. In this paper, we presented a dorsal palm vein patternrecognition approach. Two approaches are presented. ...
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