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Intelligent Collaborative Tracking by Mining Auxiliary Objects

Intelligent Collaborative Tracking by Mining Auxiliary Objec...

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conference on computer vision and pattern recognition (cvpr)

作者： Ming Yang Ying Wu Shihong Lao EECS Department Northwestern University Evanston IL USA Sensing & Control Technology Laboratory OMRON Soraku-gun Kyoto Japan

Many tracking methods face a fundamental dilemma in practice: tracking has to be computationally efficient but verifying if or not the tracker is following the true target tends to be demanding, especially when the background is cluttered and/or when occlusion occurs. Due to the lack of a good solution to this problem, many existing methods tend to be either computationally intensive with the use of sophisticated image observation models, or vulnerable to the false alarms. This greatly threatens long-duration robust tracking. This paper presents a novel solution to this dilemma by integrating into the tracking process a set of auxiliary objects that are automatically discovered in the video on the fly by data mining. Auxiliary objects have three properties at least in a short time interval: (1) persistent co-occurrence with the target; (2) consistent motion correlation with the target; and (3) easy to track. The collaborative tracking of these auxiliary objects leads to an efficient computation as well as a strong verification. Our extensive experiments have exhibited exciting performance in very challenging real-world testing cases.

关键词： Collaboration Target tracking Robustness Target recognition Data mining Testing Motion estimation Histograms Support vector machines Computational efficiency

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Multigrid and multi-level Swendsen-Wang cuts for hierarchic graph partition

Multigrid and multi-level Swendsen-Wang cuts for hierarchic ...

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conference on computer vision and pattern recognition (cvpr)

作者： A. Barbu Song-Chun Zhu Computer Science Department University of California Los Angeles USA Statistics Department University of California Los Angeles USA

Many vision tasks can be formulated as partitioning an adjacency graph through optimizing a Bayesian posterior probability p defined on the partition-space. In this paper two approaches are proposed to generalize the Swendsen-Wang cut algorithm [A. Barbu and S.C. Zhu 2003] for sampling p. The first method is called multigrid SW-cut which runs SW-cut within a sequence of local "attentional" windows and thus simulates conditional probabilities of p in the partition space. The second method is called multi-level SW-cut which projects the adjacency graph into a hierarchical representation with each vertex in the high level graph corresponding to a sub-graph at the low level, and runs SW-cut at each level. Thus it simulates conditional probabilities of p at the higher level. Both methods are shown to observe the detailed balance equation and thus provide flexibilities in sampling the posterior probability p. We demonstrate the algorithms in image and motion segmentation with three levels (see Fig.1), and compare the speed improvement of the proposed methods.

关键词： Partitioning algorithms Labeling Sampling methods Equations computer vision computer science Statistics Bayesian methods Probability Image sampling

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Modeling Correspondences for Multi-Camera Tracking Using Nonlinear Manifold Learning and Target Dynamics

Modeling Correspondences for Multi-Camera Tracking Using Non...

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conference on computer vision and pattern recognition (cvpr)

作者： V.I. Morariu O.I. Camps Computer Vision Laboratory University of Maryland College Park MD USA Department of Electrical Engineering Pennsylvania State University University Park PA USA

Multi-camera tracking systems often must maintain consistent identity labels of the targets across views to recover 3D trajectories and fully take advantage of the additional information available from the multiple sensors. Previous approaches to the "correspondence across views" problem include matching features, using camera calibration information, and computing homographies between views under the assumption that the world is planar. However, it can be difficult to match features across significantly different views. Furthermore, calibration information is not always available and planar world hypothesis can be too restrictive. In this paper, a new approach is presented for matching correspondences based on the use of nonlinear manifold learning and system dynamics identification. The proposed approach does not require similar views, calibration nor geometric assumptions of the 3D environment, and is robust to noise and occlusion. Experimental results demonstrate the use of this approach to generate and predict views in cases where identity labels become ambiguous.

关键词： Target tracking Cameras Calibration Nonlinear dynamical systems Manifolds computer vision Trajectory Sensor systems Noise robustness Laboratories

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On the relation between dependence and diversity in multiple classifier systems

On the relation between dependence and diversity in multiple...

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ITCC 2005 - International conference on Information Technology: Coding and Computing

作者： Chen, Dechang Hua, Dong Sirlantzis, Konstantinos Ma, Xiaobin Preventive Medicine and Biometrics Uniformed Services University of the Health Sciences Bethesda MD 20814 United States Department of Computer Science George Washington University 801 22nd St. NW Washington DC 20052 United States Department of Electronics University of Kent Canterbury Kent CT2 7NT United Kingdom Computer Science and Engineering University of Minnesota 200 Union Street SE Minneapolis MN 55455 United States

ISBN: (纸本)0769523153

In this paper we investigate the issues of independence and diversity among individual classifiers participating in a multiple classifier fusion scheme. First we present a formal definition of statistically independent classifiers. Then we focus on testing the independence between two classifiers. Dependence of two classifiers leads to the conclusion that every ensemble of classifiers in which they participate is not an independent scheme. Previous studies have argued that independence of the classifiers infuses diversity in the multi-classifier system, which is directly related to improved performance. Consequently, we introduce a measure for the degree of diversity as expressed by the agreement among the classifiers' outputs in such an ensemble. A number of examples drawn from diverse domains in pattern recognition are also given to illustrate the relation between classifier dependence and diversity estimation. Our results suggest the measurement of the classifiers ' decisions agreement as an informative measure of the strength of association among dependent classifiers. © 2005 ieee.

关键词： Classification (of information)

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Reconstructing open surfaces from unorganized data points

Reconstructing open surfaces from unorganized data points

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conference on computer vision and pattern recognition (cvpr)

作者： J.E. Solem A. Heyden School of Technology and Society Malmö University Sweden

In this paper a method for fitting open surfaces to an unorganized set of data points is presented using a level set representation of the surface. This is done by tracking a curve, representing the boundary, on the implicitly defined surface. This curve is given as the intersection of the level set describing the surface and an auxiliary level set. These two level sets are propagated using the same motion vector field. Special care has to be taken in order for the surfaces not to intersect at other places than at the desired boundary. Methods for accomplishing this are presented and a novel fast scheme for finding good initial values is proposed. This novel method gives a piecewise linear approximation of the initial surface boundary using a partition of the convex hull. With the described method open surfaces can be fitted to point clouds obtained using structure from motion techniques. This paper solves an important practical problem since in many cases the surfaces in the scene are open or can only be viewed from certain directions. Successful experiments on several data sets support the method.

关键词： Surface reconstruction Surface fitting Level set Image reconstruction Layout Cameras Clouds computer vision Surface treatment Vectors

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A variational approach to problems in calibration of multiple cameras

A variational approach to problems in calibration of multipl...

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conference on computer vision and pattern recognition (cvpr)

作者： G. Unal A. Yezzi Siemens AG Corporate Research and Development Princeton NJ USA Georgia Institute of Technology Atlanta GA USA

This paper addresses the problem of calibrating camera parameters using variational methods. One problem addressed in this paper is the severe lens distortion in wide angle/inexpensive camera lenses. The camera distortion effects lead to inaccurate 3D reconstructions and geometrical measurements if not accounted for. A second problem is the color calibration problem caused by variations in camera responses which results in different color measurements and affects the algorithms that depend on these measurements. We present multi-view stereo techniques based on variational ideas to address these calibration problems. To reduce computational complexity of such algorithms, we utilize a prior knowledge on the calibration object which is used in the process, and evolve the pose, orientation, and scale parameters of such a 3D model object. We derive the evolution equations for the distortion coefficients, the color calibration parameters of the cameras, and present experimental results which demonstrate their potential use.

关键词： Calibration Cameras Nonlinear distortion Lenses Distortion measurement Parameter estimation Image segmentation Image reconstruction computer vision Image edge detection

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Measurement in three dimensions by motion stereo and spherical mapping

Measurement in three dimensions by motion stereo and spheric...

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conference on computer vision and pattern recognition (cvpr)

作者： T. Morita Y. Yasukawa Y. Inamoto U. Takashi S. Kawakami Fujitsu Laboratories Limited Kawasaki Japan Fujitsu Ltd. Kawasaki Japan

A motion stereo algorithm for measuring straight lines three-dimensionally is presented. The three-dimensional information is obtained by moving a single camera linearly and processing the resulting sequential multiple images. The algorithm utilizes spherical mapping, which is a transformation from a point to a great circle on a sphere. This algorithm makes it possible to process an image from a fisheye lens, which has a wide field of view. A straight line composed of several pixels is concentrated at a single point on a two-dimensional image. A three-dimensional image measurement is then performed by use of the above point. This yields high measurement accuracy and reliability. The measurement accuracy can be improved by lengthening the span of the camera movement and by increasing the number of frames. The measurement reliability can also be improved by increasing the number of frames. The basic algorithm is described, and its characteristics relating to accuracy and reliability are tested.< >

关键词： Motion measurement Lenses Cameras Power generation Robot vision systems Navigation Pixel Length measurement Testing Parameter estimation

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Uncalibrated and unsynchronized human motion capture: a stereo factorization approach

Uncalibrated and unsynchronized human motion capture: a ster...

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conference on computer vision and pattern recognition (cvpr)

作者： P. Tresadern I. Reid Department of Engineering Science University of Oxford Oxford UK

Human motion capture typically requires several high quality, synchronized and calibrated cameras in a studio environment and can be potentially costly and technically complex. Instead, we propose a system which combines and improves upon two existing techniques, yielding an efficient method that recovers maximum likelihood joint angles and anthropomorphic data of the subject by factorization. The first technique concerns using a rank constraint framework to synchronize sequences of non-rigid motions where we extend affine methods to perspective and homography projection models. The second is a self-calibration method for two affine cameras, using constraints derived from prior knowledge of the underlying structure. We propose a minimal parameterization of the system to obtain an initial solution then apply a full bundle adjustment over the free parameters based on a geometric error We demonstrate the efficacy of our method by comparing the recovered structure and motion with that from a commercial motion capture system.

关键词： Humans Cameras Calibration Motion estimation computer vision Anthropomorphism Motion control Control systems Tracking Image reconstruction

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Bayesian face recognition using support vector machine and face clustering

Bayesian face recognition using support vector machine and f...

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conference on computer vision and pattern recognition (cvpr)

作者： Zhifeng Li Xiaoou Tang Department of Information Engineering Chinese University of Hong Kong Sha Tin Hong Kong China

In this paper, we first develop a direct Bayesian based support vector machine by combining the Bayesian analysis with the SVM. Unlike traditional SVM-based face recognition method that needs to train a large number of SVMs, the direct Bayesian SVM needs only one SVM trained to classify the face difference between intra-personal variation and extra-personal variation. However, the added simplicity means that the method has to separate two complex subspaces by one hyper-plane thus affects the recognition accuracy. In order to improve the recognition performance we develop three more Bayesian based SVMs, including the one-versus-all method, the hierarchical agglomerative clustering based method, and the adaptive clustering method. We show the improvement of the new algorithms over traditional subspace methods through experiments on two face databases, the FERET database and the XM2VTS database.

关键词： Bayesian methods Face recognition Support vector machines Support vector machine classification Linear discriminant analysis Clustering algorithms Databases Karhunen-Loeve transforms Face detection Information analysis

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Finding folds: on the appearance and identification of occlusion

Finding folds: on the appearance and identification of occlu...

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conference on computer vision and pattern recognition (cvpr)

作者： P.S. Huggins H.F. Chen P.N. Belhumeur S.W. Zucker Center for Computational Vision and Control Yale University New Heaven CT USA

ISBN: (纸本)0769512720

A natural sequel to edge detection is the interpretation of edges. This interpretation can provide useful information to various computer vision processes, including recognition, reconstruction, and tracking. In this paper we consider the problem of identifying occlusion edges in a single image. We examine the appearance of occlusion edges under variable illumination, both analytically and empirically, and find that the pattern of shading in the neighborhood of occlusion edges is a stable feature. Finally, we derive a filter for detecting occlusion and present the results of its application.

关键词： Image edge detection computer vision Lighting Layout Reflectivity Computed tomography pattern analysis Image segmentation Image recognition Shape

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