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Human Activity Classification Based on Gait Energy Image and Coevolutionary Genetic Programming

Human Activity Classification Based on Gait Energy Image and...

引用

International conference on pattern recognition

作者： Xiaotao Zou B. Bhanu Center for Research in Intelligent Systems University of California Riverside CA USA

ISBN: (纸本)0769525210

In this paper, we present a novel approach based on gait energy image (GET) and co-evolutionary genetic programming (CGP) for human activity classification. Specifically, Hu's moment and normalized histogram bins are extracted from the original GEIs as input features. CGP is employed to reduce the feature dimensionality and learn the classifiers. The strategy of majority voting is applied to the CGP to improve the overall performance in consideration of the diversification of genetic programming. This learning-based approach improves the classification accuracy by approximately 7 percent in comparison to the traditional classifiers

关键词： Humans Genetic programming Biological system modeling Legged locomotion pattern recognition Surveillance Intelligent systems Histograms Voting computer vision

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Robust Segmentation of Hidden Layers in Video Sequences

Robust Segmentation of Hidden Layers in Video Sequences

引用

International conference on pattern recognition

作者： R. Dupont O. Juan R. Keriven CERTIS Laboratory Ecole des Ponts Paris France

In this paper, we propose a novel and robust method for extracting motion layers in video sequences. Taking advantage of temporal continuity, our framework considers both the visible and the hidden parts of each layer in order to increase robustness. Moreover, the hidden parts of the layers are recovered, which could be of great help in many high level vision tasks. Modeling the problem as a labeling task, we state it in a MRF-optimization framework and solve it with a graph-cut algorithm. Both synthetic and real video sequences show a visible layers extraction comparable to the one usually performed by state of the art methods, as well as a novel and successful segmentation of hidden layers

关键词： Robustness Video sequences Labeling Motion estimation Layout Laboratories Data mining Motion segmentation computer vision Video compression

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Localized Support Vector Machines for Classification

Localized Support Vector Machines for Classification

引用

International Joint conference on Neural Networks (IJCNN)

作者： Ming Dong Jing Wu Machine Vision and Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA

Support vector machines (SVMs) have been promising methods in pattern recognition because of their solid mathematical foundation. In this paper, we propose a localized SVM classification scheme (LSVM). In which we first cluster the training data in each category, and then train a set of SVMs based on these dusters. The SVMs trained from the clusters in each category that are nearest to the given input pattern are then selected for the final classification. Our experiments on six UCI datasets show that LSVM outperforms the traditional SVM.

关键词： Support vector machines Support vector machine classification Kernel USA Councils Solids Machine learning Training data Nails Neural networks Clustering algorithms

来源：评论

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Nonparametric Background Generation

Nonparametric Background Generation

引用

International conference on pattern recognition

作者： Yazhou Liu Hongxun Yao Wen Gao Xilin Chen Debin Zhao Harbin Institute of Technology Harbin China Institute of Computing Technology Chinese Academy and Sciences Beijing China

A novel background generation method based on non-parametric background model is presented for background subtraction. We introduce a new model, named as effect components description (ECD), to model the variation of the background, by which we can relate the best estimate of the background to the modes (local maxima) of the underlying distribution. Based on ECD, an effective background generation method, most reliable background mode (MRBM), is developed. The basic computational module of the method is an old pattern recognition procedure, the mean shift, which can be used recursively to find the nearest stationary point of the underlying density function. The advantages of this method are three-fold: first, backgrounds can be generated from image sequence with cluttered moving objects; second, backgrounds are very clear without blur effect; third, it is robust to noise and small vibration. Extensive experimental results illustrate its good performance

关键词： Object detection Layout pattern recognition computer vision Switches Kernel Probability Robustness computer science Density functional theory

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Fundamental matrix and slightly overlapping views

Fundamental matrix and slightly overlapping views

引用

International conference on pattern recognition

作者： R. Pflugfelder H. Bischof Video and Safety Technology ARC Seibersdorf research Austria Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

This paper proposes a method to compute the fundamental matrix from slightly overlapping views. Slight overlaps are preferable to substantial overlaps in large multi-camera surveillance applications, because otherwise the number of cameras and thus the overall application costs would gravely increase. Instead of using point correspondences alone, we additionally use the infinite homography. The infinite homography can be computed from line segments when we have a Manhattan world scene and assume cameras with square pixels and the same alignment of the scene in both views. All assumptions are not too restrictive and occur frequently. The infinite homography and one of the epipoles determine the fundamental matrix. The paper demonstrates a simple solution to compute the epipoles from at least two point correspondences without requiring points uniformly distributed within the images. Experiments on synthetic images clearly show the advantages of the proposed method compared to the classical eight point algorithm. Images of an office scene show the idea on real world images

关键词： Cameras Layout Surveillance Application software Costs Distributed computing Image segmentation Safety computer graphics computer vision

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Fast Linear Discriminant Analysis Using Binary Bases

Fast Linear Discriminant Analysis Using Binary Bases

引用

International conference on pattern recognition

作者： Feng Tang Hai Tao Department of Computer Engineering University of California Santa Cruz USA

Linear discriminant analysis (LDA) is a widely used technique for pattern classification. It seeks the linear projection of the data to a low dimensional subspace where the data features can be modeled with maximal discriminative power. The main computation involved in LDA is the dot product between LDA base vector and the data which is costly element-wise floating point multiplications. In this paper, we present a fast linear discriminant analysis method called binary LDA, which possesses the desirable property that the subspace projection operation can be computed very efficiently. We investigate the LDA guided non-orthogonal binary subspace method to find the binary LDA bases, each of which is a linear combination of a small number of Haar-like box functions. The proposed approach is applied to face recognition. Experiments show that the discriminative power of binary LDA is preserved and the projection computation is significantly reduced

关键词： Linear discriminant analysis Vectors Face recognition pattern classification Power engineering computing Power engineering and energy Image retrieval computer vision Face detection NIST

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Disparity Flow Estimation using Orthogonal Reliability-based Dynamic Programming

Disparity Flow Estimation using Orthogonal Reliability-based...

引用

International conference on pattern recognition

作者： Minglun Gong Yee-Hong Yang Department of Mathematics & Computer Science Laurentian University Greater Sudbury ONT Canada Department of Computing Science University of Alberta Edmonton AB Canada

ISBN: (纸本)0769525210

Disparity flow depicts the 3D motion of a scene in disparity space of a given view and can be considered as view-dependent scene flow. The disparity flow map of a given view is a 2D array of 3D vectors that depicts the 3D motion observed at different pixel locations. Estimating 3D motion in form of disparity flow map limits all computations in the 2D image space and converts the 3D motion estimation problem into a 2D labeling problem. A novel algorithm is presented in this paper for disparity flow estimation using the orthogonal reliability-based dynamic programming technique. Experimental results using captured stereo sequences show that the new algorithm can generate dense and smooth 3D motion for dynamic scenes

关键词： Dynamic programming Motion estimation Layout Labeling Stereo vision Image motion analysis Motion analysis computer science Image converters computer vision

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Activity recognition from Silhouettes using Linear Systems and Model (In)validation Techniques

Activity Recognition from Silhouettes using Linear Systems a...

引用

International conference on pattern recognition

作者： R. Lublinerman N. Ozay D. Zarpalas O. Camps Department of Computer Science and Engineering Pennsylvania State University University Park PA USA Department of Electrical Engineering Pennsylvania State University University Park PA USA

ISBN: (纸本)0769525210

In this work we propose a model (invalidation approach to gait recognition, using a system that tries to discriminate specific activities of people. The recognition process departs from an abstraction obtained from video image sequences for different activities performed by different people, by first using a suitable representation for each frame and for each frame sequence. For each frame two commonly used models for describing silhouettes are employed: Fourier descriptors and vectors of widths. Then each sequence is modeled as a linear time invariant (LTI) system that captures the dynamics of the evolution of the frame description vectors in time. Finally a standard classification tool, SVM, is used to recognize activities using similarity measures obtained through model (in)validation. The main contribution of this work is the provision of an activity recognition model and the performance evaluation of this model using two different feature spaces

关键词： Linear systems Hidden Markov models Image sequences computer vision computer science Image recognition Measurement standards Support vector machines Support vector machine classification Object recognition

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Face recognition Using Most Discriminative Local and Global Features

Face Recognition Using Most Discriminative Local and Global ...

引用

International conference on pattern recognition

作者： Yong Gao Yangsheng Wang Xuetao Feng Xiaoxu Zhou Institute of Automation Chinese Academy and Sciences China

ISBN: (纸本)0769525210

Numerous studies in psychophysics and neurophysiological literatures have shown that both local and global features are important for representing and recognizing face. In this paper, a face recognition method, using local and global multi-resolution discriminative information, is proposed. First, face is represented by multi-scale and multi-orientation Gabor features. Then AdaBoost is employed to learn local feature classifier, and LDA (linear discriminant analysis) is used to extract global discriminative information. Finally, their recognition results are fused. We evaluate both score and rank based combination schemes on FERET and XM2VTS face databases. Experimental results demonstrate that almost all combination methods improve recognition rates and the best fusion method achieves 99% rank-1 recognition rate on FERET fb probe set

关键词： Face recognition Gabor filters Face detection Linear discriminant analysis Psychology Spatial databases Probes Robustness computer vision pattern recognition

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Motion Segmentation by Multibody Trifocal Tensor Using Line Correspondence

Motion Segmentation by Multibody Trifocal Tensor Using Line ...

引用

International conference on pattern recognition

作者： Jing Zhang Fanhuai Shi Yuncai Liu Image Processing and Pattern Recognition Institute Shanghai Jiaotong University Shanghai China Image Processing and Pattern Recognition Institute School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai P. R. China

ISBN: (纸本)0769525210

We present a geometric approach to multibody motion segmentation from line correspondences. Given three perspective views containing multiple moving objects, we demonstrate that after applying a polynomial embedding to the line correspondences, they became related by the so-called multibody trilinear constrain and its associated multibody trifocal tensor. We show how to obtain the individual trifocal tensor from the second order derivatives of the multibody trilinear constraint. Given trifocal tensors, we obtain the clustering of the motions and correspondences. Experimental results on synthetic and real dynamic scenes are presented

关键词： Motion segmentation computer vision Tensile stress Layout Cameras Motion estimation pattern recognition Image processing Materials science and technology Polynomials

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