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International Conference of Soft Computing and pattern recognition (SOCPAR)

作者： Ha Dai Duong Dinh Dung Nguyen Long Thanh Ngo Dao Thanh Tinh Department of Information Systems Faculty of Information Technology Le Quy Don Technical University Hanoi Vietnam

During the last ten years computer vision techniques have shown a great potential in solving the problem of automatic fire detection. vision-based fire detection offers many advantages over the conventional methods that use smoke and heat detectors. This paper presents a novel approach for fire detection by modeling the structure of spatial of fire, this structure is considered in terms of the color intensity of fire pixels. Furthers the type-2 fuzzy clustering technique is applied to separate fire-color pixels into some clusters, then these clusters are used to model structure of fire. Experimental results show that our method is capable of detecting fire in early state of fire and weak light-intensity environment; and this method uses only information on a single image so it can be integrated into the surveillance system that used dynamic camera.

关键词： Clustering algorithms Image color analysis Histograms pattern recognition Fuzzy sets Fires Shape

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Image Classification by Non-Negative Sparse Coding, Low-Rank and Sparse Decomposition

Image Classification by Non-Negative Sparse Coding, Low-Rank...

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IEEE Conference on computer vision and pattern recognition

作者： Chunjie Zhang Jing Liu Qi Tian Changsheng Xu Hanqing Lu Songde Ma National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Texas at San Antonio One UTSA Circle

ISBN: (纸本)9781457703942

We propose an image classification framework by leveraging the non-negative sparse coding, low-rank and sparse matrix decomposition techniques (LR-Sc+SPM). First, we propose a new non-negative sparse coding along with max pooling and spatial pyramid matching method (Sc+SPM) to extract local features' information in order to represent images, where non-negative sparse coding is used to encode local features. Max pooling along with spatial pyramid matching (SPM) is then utilized to get the feature vectors to represent images. Second, motivated by the observation that images of the same class often contain correlated (or common) items and specific (or noisy) items, we propose to leverage the low-rank and sparse matrix recovery technique to decompose the feature vectors of images per class into a low-rank matrix and a sparse error matrix. To incorporate the common and specific attributes into the image representation, we still adopt the idea of sparse coding to recode the Sc+SPM representation of each image. In particular, we collect the columns of the both matrixes as the bases and use the coding parameters as the updated image representation by learning them through the locality-constrained linear coding (LLC). Finally, linear SVM classifier is leveraged for the final classification. Experimental results show that the proposed method achieves or outperforms the state-of-the-art results on several benchmarks.

关键词： Sparse coding image representation Image classification images Eigenvector Nonnegative rank of matrix error matrix decomposition Sparse matrix

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High quality depth maps from stereo matching and ToF camera

High quality depth maps from stereo matching and ToF camera

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International Conference of Soft Computing and pattern recognition (SOCPAR)

作者： Jing Zhang Liang-Hao Wang Dong-Xiao Li Ming Zhang Institute of Information and Communication Engineering Zhejiang University China

Depth generation is a key technology in computer vision. Known methods mainly rely on stereo matching, or measuring devices like ToF camera. The ToF camera performs well in low-textured regions and repetitive regions where stereo matching fails. In contrast, stereo matching works better than ToF camera in textured regions. Based on their complementary characteristics, we introduce a method to combine ToF depth and stereo matching. In order to integrate their respective advantages, we measure their reliabilities and construct a new cost volume. Experiment results show that our fusion algorithm improves the accuracy and robustness, the generated depth map is much better than that obtained from an individual method.

关键词： Cameras Reliability Image color analysis Stereo vision Three dimensional displays Sensors pattern recognition

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computer vision-Guided Virtual Craniofacial Surgery 2011

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丛书名： Advances in computer vision and pattern recognition

2011年

作者： Ananda S. Chowdhury Suchendra M. Bhandarkar

ISBN: (数字)9780857292964

ISBN: (纸本)9780857292957

This unique text/reference discusses in depth the two integral components of reconstructive surgery; fracture detection, and reconstruction from broken bone fragments. In addition to supporting its application-oriented viewpoint with detailed coverage of theoretical issues, the work incorporates useful algorithms and relevant concepts from both graph theory and statistics. Topics and features: presents practical solutions for virtual craniofacial reconstruction and computer-aided fracture detection; discusses issues of image registration, object reconstruction, combinatorial pattern matching, and detection of salient points and regions in an image; investigates the concepts of maximum-weight graph matching, maximum-cardinality minimum-weight matching for a bipartite graph, determination of minimum cut in a flow network, and construction of automorphs of a cycle graph; examines the techniques of Markov random fields, hierarchical Bayesian restoration, Gibbs sampling, and Bayesian inference.

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A New Dataset of Persian Handwritten Documents and Its Segmentation

A New Dataset of Persian Handwritten Documents and Its Segme...

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Iranian Machine vision and Image Processing (MVIP)

作者： Alireza Alaei P. Nagabhushan Umapada Pal Department of Studies in Computer Science University of Mysore Mysore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

In document image analysis and especially in handwritten document image recognition, standard datasets play vital roles for evaluating performances of algorithms and comparing results obtained by different groups of researchers. In this paper, an unconstrained Persian handwritten text dataset (PHTD) is introduced. The PHTD contains 140 handwritten documents of three different categories written by 40 individuals. Total number of text-lines and words/subwords in the dataset are 1787 and 27073, respectively. In most of the PHTD documents either an overlapping or a touching text-lines is present. The average number of text-lines in documents of the PHTD is 13. Two types of ground truths based on pixels information and content information are generated for the dataset. Providing these two types of ground truths for the PHTD, it can be utilized in many areas of document image processing such as sentence recognition/understanding, text-line segmentation, word segmentation, word recognition, and character segmentation. To provide a framework for other researches, recent text-line segmentation results on this dataset are also reported.

关键词： Image segmentation Handwriting recognition Databases Character recognition Image recognition Text recognition

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Distributed computer vision algorithms through distributed averaging

Distributed computer vision algorithms through distributed a...

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Conference on computer vision and pattern recognition (CVPR)

作者： Roberto Tron René Vidal Center of Imaging Science Johns Hopkins University Baltimore MD USA

Traditional computer vision and machine learning algorithms have been largely studied in a centralized setting, where all the processing is performed at a single central location. However, a distributed approach might be more appropriate when a network with a large number of cameras is used to analyze a scene. In this paper we show how centralized algorithms based on linear algebraic operations can be made distributed by using simple distributed averages. We cover algorithms such as SVD, least squares, PCA, GPCA, 3-D point triangulation, pose estimation and affine SfM.

关键词： Cameras Principal component analysis Distributed databases Estimation Least squares approximation Polynomials computer vision

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High Level computer vision Using OpenCV

High Level Computer Vision Using OpenCV

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Tutorials of the Brazilian Symposium on computer Graphics and Image Processing (SIBGRAPI TUTORIALS)

作者： Mauricio Marengoni Denise Stringhini Faculdade de Computação e Informática Universidade Presbiteriana Mackenzie Sao Paulo Brazil

This paper presents some more advanced topics in image processing and computer vision, such as Principal Components Analysis, Matching Techniques, Machine Learning Techniques, Tracking and Optical Flow and Parallel computer vision using CUDA. These concepts will be presented using the open CV library, which is a free computer vision library for C/C++ programmers available for Windows, Linux Mac OS and Android platforms. These topics will be covered considering not only theoretical aspects but practical examples will be presented in order to understand how and when to use each of them.

关键词： computer vision Correlation Image segmentation pattern recognition Image edge detection Histograms Feature extraction

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mippr 2009: pattern recognition and computer vision - Introduction

MIPPR 2009: Pattern recognition and computer vision - Introd...

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mippr 2009 - pattern recognition and computer vision: 6th International Symposium on Multispectral Image Processing and pattern recognition

作者： Huang, Thomas S. Zhang, Bo University of Illinois at Urbana-Champaign United States Tsinghua University China

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. © 2009 Copyright SPI... 详细信息

ISBN: (纸本)9780819478078

关键词： computer vision

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High-Dimensional Signature Compression for Large-Scale Image Classification

High-Dimensional Signature Compression for Large-Scale Image...

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IEEE Conference on computer vision and pattern recognition

作者： Jorge Sanchez Florent Perronnin Textual and Visual Pattern Analysis (TVPA) group Xerox Research Centre Europe (XRCE)

ISBN: (纸本)9781457703942

We address image classification on a large-scale, i.e. when a large number of images and classes are involved. First, we study classification accuracy as a function of the image signature dimensionality and the training set size. We show experimentally that the larger the training set, the higher the impact of the dimensionality on the accuracy. In other words, high-dimensional signatures are important to obtain state-of-the-art results on large datasets. Second, we tackle the problem of data compression on very large signatures (on the order of 10~5 dimensions) using two lossy compression strategies: a dimensionality reduction technique known as the hash kernel and an encoding technique based on product quantizers. We explain how the gain in storage can be traded against a loss in accuracy and/or an increase in CPU cost. We report results on two large databases - Image-Net and a dataset of 1M Flickr images - showing that we can reduce the storage of our signatures by a factor 64 to 128 with little loss in accuracy. Integrating the decompression in the classifier learning yields an efficient and scalable training algorithm. On ILSVRC2010 we report a 74.3% accuracy at top-5, which corresponds to a 2.5% absolute improvement with respect to the state-of-the-art. On a subset of 10K classes of ImageNet we report a top-1 accuracy of 16.7%, a relative improvement of 160% with respect to the state-of-the-art.

关键词： Image classification signatures Dataset images Arts Data compression central processing units dimensionality reduction

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Human behavior clustering for anomaly detection

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Frontiers of Materials Science 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 surveil- lance 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 set. The 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 patterns 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 efficiency. The new model is a four- level 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 video. Each 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） method. Experimental 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 anomalydetection Bayesian topic models hidden Markov model（HMM） spatiotemporal interest points

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