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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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Hierarchical part-based visual object categorization

Hierarchical part-based visual object categorization

引用

conference on computer vision and pattern recognition (cvpr)

作者： G. Bouchard B. Triggs LEAR GRAVIR-INRIA Montbonnot France

We propose a generative model that codes the geometry and appearance of generic visual object categories as a loose hierarchy of parts, with probabilistic spatial relations linking parts to subparts, soft assignment of subparts to parts, and scale invariant keypoint based local features at the lowest level of the hierarchy. The method is designed to efficiently handle categories containing hundreds of redundant local features, such as those returned by current key-point detectors. This robustness allows it to outperform constellation style models, despite their stronger spatial models. The model is initialized by robust bottom-up voting over location-scale pyramids, and optimized by expectation-maximization. Training is rapid, and objects do not need to be marked in the training images. Experiments on several popular datasets show the method's ability to capture complex natural object classes.

关键词： Solid modeling Biological system modeling Detectors Robustness Humans Shape computer vision Geometry Joining processes Design methodology

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A cross-validatory statistical approach to scale selection for image denoising by nonlinear diffusion

A cross-validatory statistical approach to scale selection f...

引用

conference on computer vision and pattern recognition (cvpr)

作者： G. Papandreou P. Maragos School of Electrical and Computer Engineering National and Technical University of Athens Athens Greece

Scale-spaces induced by diffusion processes play an important role in many computer vision tasks. Automatically selecting the most appropriate scale for a particular problem is a central issue for the practical applicability of such scale-space techniques. This paper concentrates on automatic scale selection when nonlinear diffusion scale-spaces are utilized for image denoising. The problem is studied in a statistical model selection framework and cross-validation techniques are utilized to address it in a principled way. The proposed novel algorithms do not require knowledge of the noise variance and have acceptable computational cost. Extensive experiments on natural images show that the proposed methodology leads to robust algorithms, which outperform existing techniques for a wide range of noise types and noise levels.

关键词： Image denoising Noise reduction computer vision Diffusion processes Nonlinear equations Noise level Application software Image edge detection Multi-stage noise shaping Steady-state

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Spatiograms versus histograms for region-based tracking

Spatiograms versus histograms for region-based tracking

引用

conference on computer vision and pattern recognition (cvpr)

作者： S.T. Birchfield Sriram Rangarajan Electrical and Computer Engineering Department Clemson University Clemson SC USA

We introduce the concept of a spatiogram, which is a generalization of a histogram that includes potentially higher order moments. A histogram is a zeroth-order spatiogram, while second-order spatiograms contain spatial means and covariances for each histogram bin. This spatial information still allows quite general transformations, as in a histogram, but captures a richer description of the target to increase robustness in tracking. We show how to use spatiograms in kernel-based trackers, deriving a mean shift procedure in which individual pixels vote not only for the amount of shift but also for its direction. Experiments show improved tracking results compared with histograms, using both mean shift and exhaustive local search.

关键词： Histograms Robustness Target tracking Hafnium US Department of Transportation Voting Indexing Shape Pixel Probability distribution

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Two-view geometry estimation unaffected by a dominant plane

Two-view geometry estimation unaffected by a dominant plane

引用

conference on computer vision and pattern recognition (cvpr)

作者： O. Chum T. Werner J. Matas CMP Department of Cybernetics Czech Technical University Prague Czech Republic

A RANSAC-based algorithm for robust estimation of epipolar geometry from point correspondences in the possible presence of a dominant scene plane is presented. The algorithm handles scenes with (i) all points in a single plane, (ii) majority of points in a single plane and the rest off the plane, (iii) no dominant plane. It is not required to know a priori which of the cases (i)-(iii) occurs. The algorithm exploits a theorem we proved, that if five or more of seven correspondences are related by a homography then there is an epipolar geometry consistent with the seven-tuple as well as with all correspondences related by the homography. This means that a seven point sample consisting of two outliers and five inliers lying in a dominant plane produces an epipolar geometry which is wrong and yet consistent with a high number of correspondences. The theorem explains why RANSAC often fails to estimate epipolar geometry in the presence of a dominant plane. Rather surprisingly, the theorem also implies that RANSAC-based homography estimation is faster when drawing nonminimal samples of seven correspondences than minimal samples of four correspondences.

关键词： Geometry Layout Robustness Lamps Cybernetics Computational complexity Measurement standards computer society computer vision pattern recognition

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Monocular 3D tracking of the golf swing

Monocular 3D tracking of the golf swing

引用

conference on computer vision and pattern recognition (cvpr)

作者： R. Urtasun D.J. Fleet P. Fua EPFL Computer Vision Laboratory Lausanne Switzerland Department of Computer Science University of Toronto Canada

We propose an approach to incorporating dynamic models into the human body tracking process that yields full 3D reconstructions from monocular sequences. We formulate the tracking problem in terms of minimizing a differentiable criterion whose differential structure is rich enough for successful optimization using a simple hill-climbing approach as opposed to a multihypotheses probabilistic one. In other words, we avoid the computational complexity of multihypotheses algorithms while obtaining excellent results under challenging conditions. To demonstrate this, we focus on monocular tracking of a golf swing from ordinary video. It involves both dealing with potentially very different swing styles, recovering arm motions that are perpendicular to the camera plane and handling strong self-occlusions.

关键词： Tracking Biological system modeling computer vision Humans Cameras Laboratories Principal component analysis computer science Image reconstruction Computational complexity

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Simultaneous modeling and tracking (SMAT) of feature sets

Simultaneous modeling and tracking (SMAT) of feature sets

引用

conference on computer vision and pattern recognition (cvpr)

作者： N.D.H. Dowson R. Bowden Centre for Speech Vision and Signal Processing University of Surrey Guildford UK

A novel method for the simultaneous modeling and tracking (SMAT) of a feature set during motion sequence is proposed. The method requires no prior information. Instead the a posteriori distribution of appearance and shape is built up incrementally using an exemplar based approach. The resulting model is less optimal than when a priori data is used, but can be built in real-time. Data in any form may be used, provided a distance measure and a means to classify outliers exists. Here, a two tier implementation of SMAT is used: at the feature level, mutual information is used to track image patches; and at the object level, a structure model is built from the feature positions. As experiments demonstrate, the tracker is robust and operates in real-time without requiring prelearned data.

关键词： Robustness Tracking Shape Speech processing Signal processing Mutual information Layout Boosting Filters Newton method

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Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning

Lane Change Intent Analysis Using Robust Operators and Spars...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： J.C. McCall M.M. Trivedi D. Wipf Bhaskar Rao Computer Vision and Robotics Research Laboratory University of California San Diego USA Digital Signal Processing Laboratory University of California San Diego USA

In this paper we demonstrate a driver intent inference system (DIIS) based on lane positional information, vehicle parameters, and driver head motion. We present robust computer vision methods for identifying and tracking freeway lanes and driver head motion. These algorithms are then applied and evaluated on real-world data collected in a modular intelligent vehicle test-bed. Analysis of the data for lane change intent is performed using a sparse Bayesian learning methodology. Finally, the system as a whole is evaluated using a novel metric and real-world data of vehicle parameters, lane position, and driver head motion.

关键词： Robustness Bayesian methods Vehicle driving computer vision Tracking Traffic control Inference algorithms Intelligent vehicles Testing Data analysis

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Moving cast shadow detection from a Gaussian mixture shadow model

Moving cast shadow detection from a Gaussian mixture shadow ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： N. Martel-Brisson A. Zaccarin Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Laval University QUE Canada

Moving cast shadows are a major concern for foreground detection algorithms. Processing of foreground images in surveillance applications typically requires that such shadows have been identified and removed from the detected foreground. This paper presents a novel pixel-based statistical approach to model moving cast shadows of non-uniform and varying intensity. This approach uses the Gaussian mixture model (GMM) learning ability to build statistical models describing moving cast shadows on surfaces. This statistical modeling can deal with scenes with complex and time-varying illumination, and prevent false detection in regions where shadows cannot be detected. Gaussian mixture shadow models (GMSM) are automatically constructed and updated over time, are easily added to GMM architecture for foreground detection, and require only a small number of parameters. Results obtained with different scene types show the robustness of the approach.

关键词： Layout Lighting Brightness Detection algorithms Surveillance Robustness Image segmentation Solid modeling Geometry computer vision

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Interactive graph cut based segmentation with shape priors

Interactive graph cut based segmentation with shape priors

引用

conference on computer vision and pattern recognition (cvpr)

作者： D. Freedman Tao Zhang Computer Science Department Rensselaer Polytechnic Institute Troy NY USA

Interactive or semi-automatic segmentation is a useful alternative to pure automatic segmentation in many applications. While automatic segmentation can be very challenging, a small amount of user input can often resolve ambiguous decisions on the part of the algorithm. In this work, we devise a graph cut algorithm for interactive segmentation which incorporates shape priors. While traditional graph cut approaches to interactive segmentation are often quite successful, they may fail in cases where there are diffuse edges, or multiple similar objects in close proximity to one another. Incorporation of shape priors within this framework mitigates these problems. Positive results on both medical and natural images are demonstrated.

关键词： Shape Image segmentation Biomedical imaging Medical treatment Bladder computer science Application software Level set Biomedical applications of radiation Visualization

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Matching with PROSAC - progressive sample consensus

Matching with PROSAC - progressive sample consensus

引用

conference on computer vision and pattern recognition (cvpr)

作者： O. Chum J. Matas Center for Machine Perception Department of Cybernetics Czech Technical University Prague Czech Republic

A new robust matching method is proposed. The progressive sample consensus (PROSAC) algorithm exploits the linear ordering defined on the set of correspondences by a similarity function used in establishing tentative correspondences. Unlike RANSAC, which treats all correspondences equally and draws random samples uniformly from the full set, PROSAC samples are drawn from progressively larger sets of top-ranked correspondences. Under the mild assumption that the similarity measure predicts correctness of a match better than random guessing, we show that PROSAC achieves large computational savings. Experiments demonstrate it is often significantly faster (up to more than hundred times) than RANSAC. For the derived size of the sampled set of correspondences as a function of the number of samples already drawn, PROSAC converges towards RANSAC in the worst case. The power of the method is demonstrated on wide-baseline matching problems.

关键词： Robustness Testing Solid modeling computer vision pattern matching Cybernetics Image converters Stereo vision Motion estimation Image retrieval

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