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international conference of soft computing and pattern recognition (SOCPAR)

作者： Md Sah Bin Hj Salam Dzulkifli Mohamad Sheikh Hussain Shaikh Salleh Computer Science and Information System University Technology of Malaysia Skudai Johor Malaysia Bio-Med. and Health Science Engineering University Technology of Malaysia Skudai Johor Malaysia

Speech signal is temporally and acoustically varies. recognition of speech by static input neural network requires temporal normalization of the speech to be equal to the number of input nodes of the NN while maintaining the properties of the speech. This paper compares three methods for speech temporal normalization namely the linear, extended linear and zero padded normalizations on isolated speech using different sets of learning parameters on multi layer perceptron neural network with adaptive learning. Although, previous work shows that linear normalization able to give high accuracy up to 95% on similar problem, the result in this experiment shows the opposite. The experimental result shows that zero padded normalization outperformed the two linear normalization methods using all the parameter sets tested. The highest recognition rate using zero padded normalization is 99% while linear and extended linear normalizations give only 74% and 76% respectively. This paper end before conclusion by comparing data used from previous work using linear normalization which gave high accuracy and the data used in this experiment which perform poorer.

关键词： Speech recognition Neural networks pattern recognition Speech processing Speech analysis Adaptive systems Data mining Frequency Feature extraction Spectral analysis

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A Statistics and Local Homogeneity based Color Edge Detection Algorithm

A Statistics and Local Homogeneity based Color Edge Detectio...

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international conference on Advances in Recent Technologies in Communication and computing

作者： Dutta, Soumya Chaudhuri, Bidyut B. Netaji Subhash Engn Coll Dept Elect & Commun Kolkata 700152 India Indian Stat Inst Comp Vis & Pattern Recognit Unit Kolkata 700108 India

ISBN: (纸本)9781424451043

Edge detection is one of the most commonly used operations in image processing and pattern recognition, the reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detection is an essential tool. Efficient and accurate edge detection will lead to increase the performance of subsequent image processing techniques, including image segmentation, object-based image coding, and image retrieval. A novel color edge detection algorithm is proposed in this paper. On the basis of standard deviation calculation of pixels the discontinuity among the pixels are detected. Then the image is segmented into a binary image with a fixed threshold where black pixels signify homogeneous region and white pixels signify edges. Finally, a thinning technique is applied to extract thin edges. The proposed method is applied over large database of color images both synthetic and real life images and performance of the algorithm is evident from the results and is comparable with other edge detection algorithms.

关键词： color edge detection edge thinning standard deviation

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A novel pattern recognition algorithm: Combining ART network with SVM to reconstruct a multi-class classifier

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2009年第11-12期57卷 1908-1914页

作者： Wang, Anna Yuan, Wenjing Liu, Junfang Yu, Zhiguo Li, Hua Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China

Based on the principle of one-against-one support vector machines (SVMs) multi-class classification algorithm, this paper proposes an extended SVMs method which couples adaptive resonance theory (ART) network to reconstruct a multi-class classifier. Different coupling strategies to reconstruct a multi-class classifier from binary SVM classifiers are compared with application to fault diagnosis of transmission line. Majority voting, a mixture matrix and self-organizing map (SOM) network are compared in reconstructing the global classification decision. In order to evaluate the method's efficiency, one-against-all, decision directed acyclic graph (DDAG) and decision-tree (DT) algorithm based SVM are compared too. The comparison is done with Simulations and the best method is validated with experimental data. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： ART network Fault diagnosis One-against-one Multiclassification SVM

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A Color Edge Detection Algorithm in RGB Color Space

A Color Edge Detection Algorithm in RGB Color Space

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international conference on Advances in Recent Technologies in Communication and computing

作者： Dutta, Soumya Chaudhuri, Bidyut B. Netaji Subhash Engn Coll Dept Elect & Commun Kolkata 700152 India Indian Stat Inst Comp Vis & Pattern Recognit Unit Kolkata 700108 India

ISBN: (纸本)9781424451043

Edge detection is one of the most commonly used operations in image processing and pattern recognition, the reason for this is that edges form the outline of an object. An edge is the boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision involves the identification and classification of objects in an image, edge detection is an essential tool. Efficient and accurate edge detection will lead to increase the performance of subsequent image processing techniques, including image segmentation, object-based image coding, and image retrieval. A color image edge detection algorithm is proposed in this paper. Average maximum color difference value is used to predict the optimum threshold value for a color image and thinning technique is applied to extract proper edges. The proposed method is applied over large database of color images both synthetic and real life images and performance of the algorithm is evident from the results and is comparable with other edge detection algorithms.

关键词： color edge detection color difference calculation in RGB vector field edge thinning smoothing by adaptive median filter

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Computer Vision and Computer Graphics Analysis of Paintings and Drawings: An Introduction to the Literature

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13th international conference on Computer Analysis of Images and patterns

作者： Stork, David G. Ricoh Innovat Menlo Pk CA 94025 USA

ISBN: (纸本)9783642037665

In the past few years, a number of scholars trained in computer vision;pattern recognition, image processing, computer graphics;and art history have developed rigorous computer methods for addressing an increasing number of problems in the history of art. In some cases;these computer methods are more accurate than even highly trained connoisseurs, art historians and artists. Computer graphics models of artists' studios and subjects allow scholars to explore "what if" scenarios and determine artists' studio praxis. Rigorous computer ray-tracing software sheds light;on claims that;some artists employed optical tools. Computer methods win not replace tradition art historical methods of connoisseurship but enhance and extend them. As such, for these computer methods to be useful to the art community, they must continue to be refilled through application to a variety of significant art historical problems.

关键词： pattern recognition computer image analysis brush stroke analysis painting analysis image forensics compositing computer graphics reconstructions

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Development of correlation-based pattern recognition and its application to adaptive soft-sensor design

Development of correlation-based pattern recognition and its...

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ICROS-SICE international Joint conference 2009, ICCAS-SICE 2009

作者： Fujiwara, Koichi Kano, Manabu Hasebe, Shinji Dept. of Chemical Engineering Kyoto University Katsura Campus Nishikyo-ku Kyoto 615-8510 Japan

ISBN: (纸本)9784907764333

Although linear regression is a simple and useful method to build process models, they do not always function well in practice due to not only changes in process characteristics but differences of specifities between the equipments when multiple equipments are operated in parallel. To cope with them, the correlation between variables should be considered. In the present work, a new pattern recognition method, referred to as Nearest Correlation (NC) method that can select samples whose correlations are similar to the query point without supervised signal is proposed. The proposed procedures are as follows: 1) Subtract the query point from all the other samples. 2) Calculate the correlation coefficient between all pairs of arbitrary two subtracted samples, and the pairs whose correlation coefficients are close to -1 are selected. 4) Derive the subspace containing the query point from the selected samples. 4) The Q statistics between all samples and the derived subspace are calculated, and the samples whose Q statistic is small are selected as the similar samples to the query point. In addition, a new soft-sensor design method integrating the NC method and Just-In-Time (JIT) modeling is proposed. This method is referred to as Correlation-based JIT (C-JIT) modeling, and it cope with the changes in process characteristics and the differences of specifities between the equipments. The usefulness of the proposed NC method and C-JIT modeling are demonstrated through case studies of CSTR process. © 2009 SICE.

关键词： Identification (control systems)

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Graph neuron and hierarchical graph neuron, novel approaches toward real time pattern recognition in wireless sensor networks 09

Graph neuron and hierarchical graph neuron, novel approaches...

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2009 ACM international Wireless Communications and Mobile computing conference, IWCMC 2009

作者： Basirat, Amir H. Khan, Asad I. Faculty of Information Technology Monash University Clayton 3168 VIC Australia

ISBN: (纸本)9781605585697

The capability to support plethora of new diverse applications has placed Wireless Sensor Network (WSN) technology at threshold of an era of significant potential growth. In this paper, an attempt is made to analyze effectiveness of various available approaches toward pattern recognition in WSNs while introducing a novel method using a highly distributed associative memory technique called Graph Neuron (GN). The proposed approach not only enjoys from conserving the limited power resources of resourceconstrained sensor nodes but also can be scaled effectively to address scalability issues which are of primary concern in wireless sensor networks. In addition, to overcome the issues of crosstalk available in the GN algorithm, Hierarchical Graph Neuron (HGN) an extended model of GN is presented which not only promises to deliver accurate results but also can be used for diverse types of applications in a multidimensional domain. Copyright © 2009 ACM.

关键词： Bayesian networks

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BiSim: A Simple and Efficient Biclustering Algorithm

BiSim: A Simple and Efficient Biclustering Algorithm

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Nighat Noureen Muhammad Abdul Qadir Department of Computer Science & Bioinformatics Mohammad Ali Jinnah University Islamabad Pakistan

Analysis of gene expression data includes classification of the data into groups and subgroups based on similar expression patterns. Standard clustering methods for the analysis of gene expression data only identifies the global models while missing the local expression patterns. In order to identify the missed patterns biclustering approach has been introduced. Various biclustering algorithms have been proposed by scientists. Among them binary inclusion maximal algorithm (BiMax) forms biclusters when applied on a gene expression data through divide and conquer approach. The worst-case running-time complexity of BiMax for matrices containing disjoint biclusters is O(nmb) and for arbitrary matrices is of order O(nmb min{n, m}) where b is the number of all inclusion-maximal biclusters in matrix. In this paper we present an improved algorithm, BiSim, for biclustering which is easy and avoids complex computations as in BiMax. The complexity of our approach is O(n*m) for n genes and m conditions, i.e, a matrix of size n*m. Also it avoids extra computations within the same complexity class and avoids missing of any biclusters.

关键词： Gene expression Clustering methods Clustering algorithms pattern analysis Cells (biology) Biological processes pattern recognition Computer science Bioinformatics Algorithm design and analysis

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Weighted Aspect Moment Invariant in pattern recognition

Weighted Aspect Moment Invariant in Pattern Recognition

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international conference on Computational Science and Its Applications (ICCSA 2009)

作者： Pamungkas, Rela Puteri Shamsuddin, Siti Mariyam Univ Teknol Malaysia Soft Comp Res Grp Skudai 81310 Johor Malaysia

ISBN: (纸本)9783642024566

Many drawbacks has been found in Hu's moment Invariant or known as Geometric Moment Invariant (GMI). Due to its flexibility. GMI is still widely used by the researchers until now. This paper proposes an alternative approach, Weighted Aspect Moment Invariant (WAMI) by combining Weighted Central Moment (WCM) and Aspect Moment Invariant (AsMI) to solve GMI's drawbacks in term of noise and unequal data scaling. Various insect images are used in this study with two different sizes as simulation images. The simulation results show that the proposed WAMI improves inter-class and intra-class criteria for unequally scaling data compared to AsMI.

关键词： pattern recognition Aspect Moment Invariant Geometric Moment Invariant Weighting Function

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Design of Automatic Target-Scoring System of Shooting Game Based On Computer Vision

Design of Automatic Target-Scoring System of Shooting Game B...

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IEEE international conference on Automation and Logistics

作者： Fan, Xinnan Cheng, Qianqian Ding, Penghua Zhang, Xuewu Hohai Univ Comp & Informat Inst Changzhou Jiangsu Prov Peoples R China

ISBN: (纸本)9781424447947

This paper describes a machine vision system with back lighting illumination and friendly man-machine interface. Subtraction is used to segment target holes quickly and accurately. The oval obtained after tracing boundary is processed by Generalized Hough Transform to acquire the target's center. Marked-hole's area, perimeter and moment invariants are extracted as cluster features. The auto-scoring software, programmed by Visual C++, has successfully solved the recognition of off-target and overlapped holes through alarming surveillance and bullet tacking programs. The experimental results show that, when the target is distorted obviously, the system can recognize the overlapped holes on real time and also clusters random shape holes on the target correctly. The high accuracy, fast computing speed, easy debugging and low cost make the system can be widely used.

关键词： Moment Invariant Image Processing pattern recognition G'eneralized Hough Transform Auto-scoring System

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