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Circle Detection Based on Multiscale Edge Curvature Estimation

pattern recognition and Image Analysis 2011年第2期21卷 318-320页

作者： Semeikina, E.V. Yurin, D.V. Faculty of Computational Mathematics and Cybernetics Laboratory of Mathematical Methods of Image Processing Moscow State University Leninskie gory Moscow 119992 Russia

A procedure for adapting the isoline curvature formula to estimate an edge curvature in the images is presented. The curvature estimation is used in the framework of the scale-space algorithm, which makes it possible to choose the required scale adaptively at each point of interest. The procedure for edge points clustering to the subsets, each of which contains the points of one circumference, has been developed to detect the circumferences. © 2011 Pleiades Publishing, Ltd.

关键词： computer vision

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Extracting local texture features for image-based coin recognition

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IET IMAGE PROCESSING 2011年第5期5卷 394-401页

作者： Shen, L. Jia, S. Ji, Z. Chen, W. -S. ShenZhen Univ Sch Comp Sci & Software Engn Shenzhen Guangdong Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100088 Peoples R China ShenZhen Univ Sch Math Shenzhen Peoples R China

The authors propose to extract local texture features for image-based coin recognition in this study. A set of Gabor wavelets and local binary pattern (LBP) operator are employed to represent texture information. Concentric ring structure is used to divide the coin image into a number of small sections. Statistics of Gabor coefficients or LBP values within each section is then concatenated into a feature vector to represent the image. A circular shift operator is proposed to make Gabor features robust against rotation variance. Matching between two coin images is done via distance measurement. The nearest-neighbour classifier is used to classify a given test coin. The public MUSCLE database consisting of over 10 000 images is used to test our algorithms;results show that significant improvements over edge distance-based methods have been achieved. The authors have also analysed the performance of the system on recognising unregistered coins and the analysis suggests further improvement could be achieved if physical properties like diameter and thickness are included for feature representation.

关键词： Image recognition feature representation computer vision and image processing techniques local binary pattern operator Gabor filters MUSCLE database image based coin recognition feature extraction Gabor wavelets image texture local texture feature extraction circular shift operator

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Discriminative High Order SVD: Adaptive Tensor Subspace Selection for Image Classification, Clustering, and Retrieval

Discriminative High Order SVD: Adaptive Tensor Subspace Sele...

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IEEE International Conference on computer vision (ICCV)

作者： Luo, Dijun Huang, Heng Ding, Chris Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9781457711022

Tensor based dimensionality reduction has recently attracted attention from computer vision and pattern recognition communities for both feature extraction and data compression. As an unsupervised method, High-Order Singular Value Decomposition (HOSVD) searches for low-rank subspaces such that the low-rank approximation error is minimized. In this paper, we propose a new unsupervised high-order tensor decomposition approach which employs the strength of discriminative analysis and K-means clustering to adaptively select subspaces that improve the clustering, classification, and retrieval capabilities of HOSVD. We provide both theoretical analysis to guarantee that our new method generates more discriminative subspaces and empirical studies on several public computer vision data sets to show the consistent improvement of our method over existing methods.

关键词： Accuracy Clustering algorithms computer vision Measurement Principal component analysis Tensile stress Vectors

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Interclass Visual Similarity Based Visual Vocabulary Learning

Interclass Visual Similarity Based Visual Vocabulary Learnin...

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1st Asian Conference on pattern recognition (ACPR)

作者： Chang, Guangming Yuan, Chunfen Hu, Weiming Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China

ISBN: (纸本)9781457701214;9781457701221

Visual vocabulary is now widely used in many video analysis tasks, such as event detection, video retrieval and video classification. In most approaches the vocabularies are solely based on statistics of visual features and generated by clustering. Little attention has been paid to the interclass similarity among different events or actions. In this paper, we present a novel approach to mine the interclass visual similarity statistically and then use it to supervise the generation of visual vocabulary. We construct a measurement of interclass similarity, embed the similarity to the Euclidean distance and use the refined distance to generate visual vocabulary iteratively. The experiments in Weizmann and KTH datasets show that our approach outperforms the traditional vocabulary based approach by about 5%.

关键词： Clustering algorithms computer vision Conferences pattern recognition Vectors Visualization Vocabulary

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Partial discharge pattern recognition using Hilbert-Huang Transform in acoustic signal analysis

Partial discharge pattern recognition using Hilbert-Huang Tr...

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2011 International Conference on Image Processing, computer vision, and pattern recognition, IPCV 2011

作者： Gu, Feng-Chang Chang, Hong-Chan Chen, Hung-Cheng Kuo, Cheng-Chien Hsu, Chao-Hsiang Department of Electrical Engineering National Taiwan University of Science and Technology Taipei 10607 Taiwan Department of Electrical Engineering National Chin-Yi University of Technology Taichung 41101 Taiwan Department of Electrical Engineering Saint John's University Taipei 25135 Taiwan

ISBN: (纸本)9781601321916

This paper proposes new partial discharge (PD) pattern recognition based on the Hilbert-Huang Transform (HHT). First, five types of defect models are well-designed on the base of investigation of many power equipment failures, and a commercial acoustic emission (AE) sensor measures the acoustic signal caused by the PD phenomenon. Next, the HHT can represent instantaneous frequency components through empirical mode decomposition (EMD), then transform to a 3D energy spectrum. Finally, the feature parameters of energy are extracted from the 3D Hilbert spectrum, using a neural network (NN) for PD recognition. To demonstrate the effectiveness of the proposed method, the identification ability is investigated on 150 sets of field-tested PD patterns of defect models. The proposed method can easily separate different defect types and shows good tolerance when random noise is added.

关键词： Empirical mode decomposition

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Automated industrial inspection of optical lenses using computer vision

Automated industrial inspection of optical lenses using comp...

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2011 International Conference on Image Processing, computer vision, and pattern recognition, IPCV 2011

作者： Lin, Hong-Dar Lin, Wan-Ting Tsai, Huan-Hua Department of Industrial Engineering and Management Chaoyang University of Technology Taichung 41349 Taiwan Department of Marketing Mays Business School Texas A and M University College Station TX 77843 United States

ISBN: (纸本)9781601321916

An LED (Light-Emitting Diode) lens with a transparent and curved surface is more difficult to detect surface defects than electronic components by current computer vision systems. The purpose of this research is to apply the block discrete cosine transform (BDCT), Hotelling T statistic, and grey clustering method to detect visual defects of LED lenses. An image with equal sized blocks is converted to DCT domain and some representative energy features of each DCT block are extracted. These energy features of each block are integrated by the T statistic and the suspected defect blocks can be determined by the multivariate statistical method. Then, the grey clustering algorithm is conducted to further confirm the block locations of the real defects. Finally, a simple segmentation method is applied to separate the defect areas. Experimental results demonstrate the defect detection rate of the proposed method is better than those of current techniques.

关键词： Multivariant analysis

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Introduction to the Special Section on Real-World Face recognition

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2011年第10期33卷 1921-1924页

作者： Hua, Gang Yang, Ming-Hsuan Learned-Miller, Erik Ma, Yi Turk, Matthew Kriegman, David J. Huang, Thomas S. IBM TJ Watson Res Ctr Hawthorne NY 10532 USA Univ Calif Dept Elect Engn & Comp Sci Merced CA 95344 USA Univ Massachusetts Dept Comp Sci Amherst MA 01003 USA Microsoft Res Asia Beijing Peoples R China Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 93093 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

The motivations for organizing this special section were to better address the challenges of face recognition in real-world scenarios, to promote systematic research and evaluation of promising methods and systems, to provide a snapshot of where we are in this domain, and to stimulate discussion about future directions. We solicited original contributions of research on all aspects of real-world face recognition, including: the design of robust face similarity features and metrics; robust face clustering and sorting algorithms; novel user interaction models and face recognition algorithms for face tagging; novel applications of web face recognition; novel computational paradigms for face recognition; challenges in large scale face recognition tasks, e.g., on the Internet; face recognition with contextual information; face recognition benchmarks and evaluation methodology for moderately controlled or uncontrolled environments; and video face recognition. We received 42 original submissions, four of which were rejected without review; the other 38 papers entered the normal review process. Each paper was reviewed by three reviewers who are experts in their respective topics. More than 100 expert reviewers have been involved in the review process. The papers were equally distributed among the guest editors. A final decision for each paper was made by at least two guest editors assigned to it. To avoid conflict of interest, no guest editor submitted any papers to this special section.

关键词： Special Issues And Sections Face recognition Three Dimensional Displays computer vision Digital Images

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A system for change detection and human recognition in voxel space using the Microsoft Kinect sensor

A system for change detection and human recognition in voxel...

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2011 IEEE Applied Imagery pattern recognition Workshop: Imaging for Decision Making, AIPR 2011

作者： Gill, T. Keller, J.M. Anderson, D.T. Luke III, R.H. Electrical and Computer Engineering University of Missouri Columbia MO United States Electrical and Computer Engineering Mississippi State University Mississippi State MS United States U.S. Army Night Vision and Electronic Sensors Directorate - Countermine Div. Fort Belvoir VA United States

ISBN: (纸本)9781467302159

Previously, we put forth a new computer vision system for indoor well-being monitoring of elderly populations based on the use of multiple stereo camera pairs. That approach involves combining the strengths of image space with three dimensional volume element (voxel) space techniques. However, that system is fundamentally limited because it is based on color imagery from visible light cameras. In this article, we extend our prior research and consider a new, inexpensive infrared depth camera device, the Microsoft Kinect. Advantages, such as the ability to operate 24-7 in low-to-no light conditions, and shortcomings are detailed. In addition, we discuss necessary algorithmic extensions to our mixed image and voxel space framework for the Kinect sensor. Experiments are performed in a laboratory designed to resemble an elders living quarter. vision findings are evaluated using our prior high-level linguistic summarization of human activity work. Preliminary results indicate that the Kinect sensor does indeed work in a wider range of operating conditions and it can produce activity descriptions that match that of a human. © 2011 IEEE.

关键词： Cameras

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An Analog VLSI Implementation for Cepstral Technique for Disparity Estimation in Stereoscopic vision

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1st International Conference on Digital Image Processing and pattern recognition (DPPR 2011)

作者： Agarwal, Harshit Sharma, Sheena Markan, Chota Dayalbagh Educ Inst Fac Engn Agra Uttar Pradesh India Dayalbagh Educ Inst Dept Phys & Comp Sci Agra Uttar Pradesh India

ISBN: (纸本)9783642240546;9783642240553

The stereoscopic vision algorithms for binocular vision are very popular and widely applied. Some of the algorithms are biologically inspired. Like. Cepstral filtering technique is applied on an interlaced image, the pattern similar to that which is found in layer IV of primate visual cortex. It involves Power spectrum in computation, which is square of absolute of Fast Fourier Transform (FFT), is a complicated and hardware unfriendly. We propose a new algorithm, in which Gabor filters, instead of Power Spectrum, are applied to the interlaced image in the Cepstral algorithm. This new algorithm makes it hardware friendly as it gives us the flexibility of working with modules which can be imitated in hardware. Such as building a FFT module is a tough ask in analog circuit but determining gabor energy, an alternative to it, is achieved by elementary circuits. A hardware scheme has also been proposed that can be used to estimate disparity and the idea can be extended in building complex modules that can perform real time - real image operations with a handful of resources as compared to employing complex digital FPGAs and CPLDs.

关键词： computer vision Stereo Imaging Biologically-inspired vision

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Human behavior clustering for anomaly detection

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中国计算机科学前沿 2011年第3期5卷 279-289页

作者： Xudong ZHU Zhijing LIU School of Computer Science and Technology Xidian University Xi'an 710071 China

This paper aims to address the problem of modeling human behavior patterns captured in surveillance videos for the application of online normal behavior recognition and anomaly detection.A novel framework is developed for automatic behavior modeling and online anomaly detection without the need for manual labeling of the training data *** framework consists of the following key components.1) A compact and effective behavior representation method is developed based on spatial-temporal interest point detection.2) The natural grouping of behavior pattems is determined through a novel clustering algorithm,topic hidden Markov model (THMM) built upon the existing hidden Markov model (HMM) and latent Dirichlet allocation (LDA),which overcomes the current limitations in accuracy,robustness,and computational *** new model is a fourlevel hierarchical Bayesian model,in which each video is modeled as a Markov chain of behavior patterns where each behavior pattern is a distribution over some segments of the *** of these segments in the video can be modeled as a mixture of actions where each action is a distribution over spatial-temporal words.3) An online anomaly measure is introduced to detect abnormal behavior,whereas normal behavior is recognized by runtime accumulative visual evidence using the likelihood ratio test (LRT) *** results demonstrate the effectiveness and robustness of our approach using noisy and sparse data sets collected from a real surveillance scenario.

关键词： computer vision unsupervised anomaly detection Bayesian topic models hidden Markov model (HMM) spatiotemporal interest points

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