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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"

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High-accuracy stereo depth maps using structured light

High-accuracy stereo depth maps using structured light

引用

conference on computer vision and pattern recognition (cvpr)

作者： D. Scharstein R. Szeliski Middlebury College USA Microsoft Research Limited USA

Progress in stereo algorithm performance is quickly outpacing the ability of existing stereo data sets to discriminate among the best-performing algorithms, motivating the need for more challenging scenes with accurate ground truth information. This paper describes a method for acquiring high-complexity stereo image pairs with pixel-accurate correspondence information using structured light. Unlike traditional range-sensing approaches, our method does not require the calibration of the light sources and yields registered disparity maps between all pairs of cameras and illumination projectors. We present new stereo data sets acquired with our method and demonstrate their suitability for stereo algorithm evaluation. Our results are available at http://***/stereo/.

关键词： Layout Pixel Testing Digital cameras Stereo vision Educational institutions Calibration Light sources Lighting Image databases

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Face authentication using the trace transform

Face authentication using the trace transform

引用

conference on computer vision and pattern recognition (cvpr)

作者： S. Srisuk M. Petrou W. Kurutach A. Kadyrov University of Surrey Guildford UK Mahanakom University of Technology Bangkok Thailand Informatics and Telematics Institute CERN Thermi Greece

We introduce a face representation, the shape trace transform (STT), for recognizing faces in an authentication system. The STT offers an alternative representation for faces that has a very high discriminatory power. We estimate the dissimilarity between two shapes by a new measure. We propose the Hausdorff context. Reinforcement learning is used to search the optimal parameters of the algorithm, for which the within class variance of the STT is minimized. This research demonstrates that the proposed method provides a new way for face representation. Our system is verified with experiments on the XM2VTS database.

关键词： Authentication Face recognition Humans Shape measurement Learning Shape control Pixel Image representation Informatics Telematics

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Multi-view stereo beyond Lambert

Multi-view stereo beyond Lambert

引用

conference on computer vision and pattern recognition (cvpr)

作者： Hailin Jin S. Soatto A.J. Yezzi Electrical Engineering Department Washington University Saint Louis MO USA Computer Science Department University of California Los Angeles CA USA Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

We consider the problem of estimating the shape and radiance of an object from a calibrated set of views under the assumption that the reflectance of the object is non-Lambertian. Unlike traditional stereo, we do not solve the correspondence problem by comparing image-to-image. Instead, we exploit a rank constraint on the radiance tensor field of the surface in space, and use it to define a discrepancy measure between each image and the underlying model. Our approach automatically returns an estimate of the radiance of the scene, along with its shape, represented by a dense surface. The former can be used to generate novel views that capture the non-Lambertian appearance of the scene.

关键词： Layout Stereo vision Shape Stereo image processing Optical reflection Reflectivity Surface reconstruction Image reconstruction computer vision Photometry

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Adaptation for multiple cue integration

Adaptation for multiple cue integration

引用

conference on computer vision and pattern recognition (cvpr)

作者： Zhaohui Sun Electric Imaging Products Research & Development Eastman Kodak (Japan) Limited Rochester NY USA

ISBN: (纸本)0769519008

Many classification tasks can be carried out by casting a domain-specific problem to general graph representation (with objects to be organized as graph nodes and pairwise similarities as graph edges) followed by a graph partition. In this paper, an adaptation scheme is proposed to integrate multiple graphs from various cues to a single graph, such that the distance between the ideal transition probability matrix to the one derived from cue integration is minimized. Four different distance measures, i.e., the Frobenius norm, the Kullback-Leibler directed divergence, the Jeffrey divergence and the cross entropy, are investigated to minimize the discrepancy. It is shown that the minimization leads to a closed-form nonlinear optimization that can be solved by the Levenberg-Marguardt method. Domain and task-specific knowledge is explored to facilitate the generic pattern classification task. Experimental results are demonstrated for image content description by multiple cue integration.

关键词： Casting Entropy Minimization methods Optimization methods Image segmentation computer vision Sun pattern classification Pixel Joining processes

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Probabilistic tracking in joint feature-spatial spaces

Probabilistic tracking in joint feature-spatial spaces

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Elgammal R. Duraiswami L.S. Davis Department of Computer Science Rutgers University Piscataway NJ USA UMIACS University of Maryland College Park MD USA Department of Computer Science University of Maryland College Park MD USA

In this paper, we present a probabilistic framework for tracking regions based on their appearance. We exploit the feature-spatial distribution of a region representing an object as a probabilistic constraint to track that region over time. The tracking is achieved by maximizing a similarity-based objective function over transformation space given a nonparametric representation of the joint feature-spatial distribution. Such a representation imposes a probabilistic constraint on the region feature distribution coupled with the region structure, which yields an appearance tracker that is robust to small local deformations and partial occlusion. We present the approach for the general form of joint feature-spatial distributions and apply it to tracking with different types of image features including row intensity, color and image gradient.

关键词： Target tracking computer science Educational institutions Robustness computer vision Region 6 Deformable models Random variables Kernel Measurement uncertainty

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Practical non-parametric density estimation on a transformation group for vision

Practical non-parametric density estimation on a transformat...

引用

conference on computer vision and pattern recognition (cvpr)

作者： E.G. Miller C. Chefd'hotel Computer Science Division University of California Berkeley Berkeley CA USA Siemens AG Corporate Research and Development Princeton NJ USA Odyssée Laboratory INRIA Sophia-Antipolis France

It is now common practice in machine vision to define the variability in an object's appearance in a factored manner, as a combination of shape and texture transformations. In this context, we present a simple and practical method for estimating non-parametric probability densities over a group of linear shape deformations. Samples drawn from such a distribution do not lie in a Euclidean space, and standard kernel density estimates may perform poorly. While variable kernel estimators may mitigate this problem to some extent, the geometry of the underlying configuration space ultimately demands a kernel, which accommodates its group structure. In this perspective, we propose a suitable invariant estimator on the linear group of non-singular matrices with positive determinant. We illustrate this approach by modeling image transformations in digit recognition problems, and present results showing the superiority of our estimator to comparable Euclidean estimators in this domain.

关键词： Shape Kernel Deformable models Costs Independent component analysis Machine vision computer science Information geometry Image recognition Probability

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Switching observation models for contour tracking in clutter

Switching observation models for contour tracking in clutter

引用

conference on computer vision and pattern recognition (cvpr)

作者： Ying Wu Gang Hua Ting Yu Department of Electrical & Computer Engineering Northwestern University Evanston IL USA

ISBN: (纸本)0769519008

We propose a generative model approach to contour tracking against nonstationary clutter and to coping with occlusions by explicit modelling and inferring. The proposed dynamic Bayesian networks consist of multiple hidden processes, which model the target, the clutter and the occlusions. The image observation models, which depict the generation of the image features, are conditioned on all the hidden processes. Based on this framework, the tracker can automatically switch among different observation models according to the hidden states of the clutter and occlusions. In addition, the inference of these hidden states provides self-evaluations for the tracker. The tracking and inference are implemented based on sequence Monte Carlo techniques. The effectiveness of the proposed approach to robust tracking and inferring nonstationary clutter and occlusion is demonstrated for a variety of image sequences.

关键词： Clutter Target tracking Interference Bayesian methods Image generation Switches Robustness Gaussian processes Monte Carlo methods Image sequences

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What went where [motion segmentation]

What went where [motion segmentation]

引用

conference on computer vision and pattern recognition (cvpr)

作者： J. Wills S. Agarwal S. Belongie Department of Computer Science and Engineering University of California La Jolla CA USA

We present a framework for motion segmentation that combines the concepts of layer-based methods and feature-based motion estimation. We estimate the initial correspondences by comparing vectors of filter outputs at interest points, from which we compute candidate scene relations via random sampling of minimal subsets of correspondences. We achieve a dense, piecewise smooth assignment of pixels to motion layers using a fast approximate graph-cut algorithm based on a Markov random field formulation. We demonstrate our approach on image pairs containing large inter-frame motion and partial occlusion. The approach is efficient and it successfully segments scenes with inter-frame disparities previously beyond the scope of layer-based motion segmentation methods.

关键词： Image segmentation Markov processes Motion analysis Image sampling Feature extraction

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Joint 3D-reconstruction and background separation in multiple views using graph cuts

Joint 3D-reconstruction and background separation in multipl...

引用

conference on computer vision and pattern recognition (cvpr)

作者： B. Goldlucke M.A. Magnor Graphics-Optics-Vision Max Planck Institut für Informatik Saarbrucken Germany

This paper deals with simultaneous depth map estimation and background separation in a multi-view setting with several fixed calibrated cameras, two problems which have previously been addressed separately. We demonstrate that their strong interdependency can be exploited elegantly by minimizing a discrete energy functional, which evaluates both properties at the same time. Our algorithm is derived from the powerful "multi-camera scene reconstruction via graph cuts" algorithm presented by Kolmogorov and Zabih (2002). Experiments with both real-world as well as synthetic scenes demonstrate that the presented combined approach yields even more correct depth estimates. In particular, the additional information gained by taking background into account increases considerably the algorithm's robustness against noise.

关键词： Layout Image reconstruction Cameras Stereo vision Pixel computer vision Iterative algorithms Yield estimation Noise robustness Background noise

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A novel support vector classifier with better rejection performance

A novel support vector classifier with better rejection perf...

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conference on computer vision and pattern recognition (cvpr)

作者： Chao Yuan D. Casasent ECE Department Carnegie Mellon University Pittsburgh PA USA

Support vector machines (SVMs) have been successfully used in many classification fields. However, conventional SVMs do not consider rejecting inputs and thus suffer from false alarms. The first reason for this is that every input is assumed to belong to one of the object classes and is accepted in some class. In this paper, we show that the second reason is that conventional SVMs do not describe each object class well. Thus, use of an output threshold does not solve this problem. We present a new support vector representation and discrimination machine (SVRDM), which has a discrimination capability comparable to that of the conventional SVM, and also offers good rejection ability. False alarm rates are greatly reduced. We analyze the properties of these two classifiers (SVM and SVRDM) in transformed feature space and compare their performances using both synthetic and real data.

关键词： Support vector machines Support vector machine classification pattern recognition Chaos Electronic mail Performance analysis Fingerprint recognition Law Legal factors Testing

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