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检索条件"任意字段=Proceedings - IEEE Computer Society Conference on Pattern Recognition and Image Processing."

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Segmentation for robust tracking in the presence of severe occlusion

Segmentation for robust tracking in the presence of severe o...

引用

conference on computer Vision and pattern recognition (CVPR)

作者： C. Gentile O. Camps M. Sznaier National Institute for Standards and Technology Gaithersburg MD USA Department of Electrical Engineering Pennsylvania State University University Park PA USA

ISBN: (纸本)0769512720

Tracking an object in a sequence of images can fail due to partial occlusion or clutter Robustness can be increased by tracking a set of "parts", provided that a suitable set can be identified. In this paper we propose a novel segmentation, specifically designed to improve robustness against occlusion in the context of tracking. The main result shows that tracking the parts resulting from this segmentation outperforms both tracking parts obtained through traditional segmentations, and tracking the entire target. Additional results include a statistical analysis of the correlation between features of a part and tracking error, and identifying a cost function highly correlated with the tracking error.

关键词： Robustness Target tracking Cost function image segmentation Statistical analysis Pixel Prototypes NIST Clustering algorithms Voting

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Using occlusions to aid position estimation for visual motion capture

Using occlusions to aid position estimation for visual motio...

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conference on computer Vision and pattern recognition (CVPR)

作者： M. Ringer T. Drummond L. Lasenby Engineering Department University of Cambridge Cambridge UK

ISBN: (纸本)0769512720

A new method for estimating the pose of a person in a marker-based visual motion capture system is presented. In such systems, certain markers are not detected at each time frame due to occlusion by the subject. It is proposed to estimate the person's pose subject to the constraint that non-detected markers remain occluded and that detected markers remain visible. In this paper, the complete posterior PDF for the person's pose is developed using the constraints mentioned above, as is a least squares solution for determining its maximum. It is shown that the new technique is able to correctly estimate a variety of poses in which many marker occlusions occur and in which typical motion capture systems fail.

关键词： Motion estimation State estimation Cameras image edge detection Motion detection Motion measurement Position measurement Least squares methods Video sequences Feature extraction

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A linear iterative method for auto-calibration using the DAC equation

A linear iterative method for auto-calibration using the DAC...

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conference on computer Vision and pattern recognition (CVPR)

作者： Yongduek Seo A. Heyden R. Cipolla IIP Laboratory POSTECH South Korea MIG Lund University Sweden SVR Laboratory University of Cambridge UK

ISBN: (纸本)0769512720

In this paper, an iterative algorithm for auto-calibration is presented. The proposed algorithm switches between linearly estimating the dual of the absolute conic and the intrinsic parameters, while also incorporating the rank-3 constraint on the intrinsic parameters. The most important property of the algorithm is that it is completely general in the sense that any type of constraint on the intrinsic parameters might be used. The proposed algorithm locates in-between of a non-linear optimization and initial linear computation, and provides robust and sufficiently accurate initial values for a bundle adjustment routine. The performance of the algorithm is shown for both simulated and real data, especially in the important case of natural (zero skew and unit aspect ratio) cameras.

关键词： Iterative methods Equations image reconstruction Iterative algorithms Cameras Calibration Switches Robustness Computational modeling H infinity control

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Parametric representations for nonlinear modeling of visual data

Parametric representations for nonlinear modeling of visual ...

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conference on computer Vision and pattern recognition (CVPR)

作者： Ying Zhu D. Comaniciu V. Ramesh S. Schwartz Electrical Engineering Princeton University Princeton NJ USA Imaging & Visualization Department Siemens Corporate Research Inc. Princeton NJ USA

ISBN: (纸本)0769512720

Accurate characterization of data distribution is of significant importance for vision problems. In many situations, multivariate visual data often spread into a nonlinear manifold in the high-dimensional space, which makes traditional linear modeling techniques ineffective. This paper proposes a generic nonlinear modeling scheme based on parametric data representations. We build a compact representation for the visual data using a set of parameterized basis (wavelet) functions, where the parameters are randomized to characterize the nonlinear structure of the data distribution. Meanwhile, a new progressive density approximation scheme is proposed to obtain an accurate estimate of the probability density, which imposes discrimination power on the model. Both synthetic and real image data are used to demonstrate the strength of our modeling scheme.

关键词： Principal component analysis Parametric statistics Data visualization Scattering Linearity Data engineering Manifolds pattern recognition Time measurement Parameter estimation

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A nonparametric statistical comparison of principal component and linear discriminant subspaces for face recognition

A nonparametric statistical comparison of principal componen...

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conference on computer Vision and pattern recognition (CVPR)

作者： J.R. Beveridge K. She B.A. Draper G.H. Givens Computer Science Department Colorado State University Fort Collins CO USA Statistics Department Colorado State University Fort Collins CO USA

ISBN: (纸本)0769512720

The FERET evaluation compared recognition rates for different semi-automated and automated face recognition algorithms. We extend FERET by considering when differences in recognition rates are statistically distinguishable subject to changes in test imagery. Nearest Neighbor classifiers using principal component and linear discriminant subspaces are compared using different choices of distance metric. Probability distributions for algorithm recognition rates and pairwise differences in recognition rates are determined using a permutation methodology. The principal component subspace with Mahalanobis distance is the best combination; using L2 is second best. Choice of distance measure for the linear discriminant subspace matters little, and performance is always worse than the principal components classifier using either Mahalanobis or L1 distance. We make the source code for the algorithms, scoring procedures and Monte Carlo study available in the hopes others will extend this comparison to newer algorithms.

关键词： Nearest neighbor searches Probes Face recognition Monte Carlo methods image recognition computer science Probability distribution Automatic testing Protocols Statistics

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Texture based classification and segmentation using the Laguerre-Gauss transform

Texture based classification and segmentation using the Lagu...

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International Symposium on image and Signal processing.and Analysis

作者： M. Carli A. Neri S. Hatipoglu S.K. Mitra Electrical Engineering Department University of Roma TRE Italy Electrical and Computer Engineering Department University of California Santa Barbara USA

ISBN: (纸本)9539676940

In this paper, we introduce a new tool for the image segmentation problem. For this purpose, we have developed a texture based segmentation and classification strategy based on a representation of the image components by a family of circular harmonic functions (CHF) defining the Laguerre-Gauss transform (LGT). LGT is the polar counterpart of the Hermite transform. By utilizing the outputs of CHF operators at different scales, we use a multiscale segmentation and investigate the performance of CHF texture features.

关键词： image segmentation Gaussian processes Feature extraction Wavelet transforms Testing Spatial databases pattern matching Classification algorithms pattern recognition Polynomials

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Instant dehazing of images using polarization

Instant dehazing of images using polarization

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conference on computer Vision and pattern recognition (CVPR)

作者： Y.Y. Schechner S.G. Narasimhan S.K. Nayar Department of Computer Science Columbia University New York NY USA

ISBN: (纸本)0769512720

We present an approach to easily remove the effects of haze from images. It is based on the fact that usually airlight scattered by atmospheric particles is partially polarized. Polarization filtering alone cannot remove the haze effects, except in restricted situations. Our method, however, works under a wide range of atmospheric and viewing conditions. We analyze the image formation process, taking into account polarization effects of atmospheric scattering. We then invert the process to enable the removal of haze from images. The method can be used with as few as two images taken through a polarizer at different orientations. This method works instantly, without relying on changes of weather conditions. We present experimental results of complete dehazing in far from ideal conditions for polarization filtering. We obtain a great improvement of scene contrast and correction of color. As a by product, the method also yields a range (depth) map of the scene, and information about properties of the atmospheric particles.

关键词： Layout Optical scattering Optical filters Particle scattering Light scattering Optical polarization image analysis Filtering Atmospheric modeling image restoration

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Calibrated, registered images of an extended urban area

Calibrated, registered images of an extended urban area

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conference on computer Vision and pattern recognition (CVPR)

作者： S. Teller M. Antone Z. Bodnar M. Bosse S. Coorg M. Jethwa N. Master MIT Computer Graphics Group USA

ISBN: (纸本)0769512720

We describe a dataset of several thousand calibrated, geo-referenced, high dynamic range color images, acquired under uncontrolled, variable illumination in an outdoor region spanning hundreds of meters. All image, feature, calibration, and geo-referencing data are available at http://***/data. Calibrated imagery is of fundamental interest in a wide variety of applications. We have made this data available in the belief that researchers in computer graphics, computer vision, photogrammetry and digital cartography will find it useful in several ways: as a test set for their own algorithms; as a calibrated image set for applications such as image-based rendering, metric 3D reconstruction, and appearance recovery; and as controlled imagery for integration into existing GIS systems and applications. The Web-based interface to the data provides interactive viewing of high-dynamic-range images and mosaics; extracted edge and point features; intrinsic and extrinsic calibration, along with maps of the ground context in which the images were acquired; the spatial adjacency relationships among images; the epipolar geometry relating adjacent images; compass and absolute scale overlays; and quantitative consistency measures for the calibration data.

关键词： Urban areas Calibration Application software Dynamic range Color Lighting computer graphics computer vision System testing Rendering (computer graphics)

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Matching, reconstructing and grouping 3D lines from multiple views using uncertain projective geometry

Matching, reconstructing and grouping 3D lines from multiple...

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conference on computer Vision and pattern recognition (CVPR)

作者： S. Heuel W. Forstner Institute for Photogrammetry University of Bonn Germany

ISBN: (纸本)0769512720

We present a geometric method for (i) matching 2D line segments from multiple oriented images, (ii) optimally reconstructing 3D line segments and (iii) grouping 3D line segments to corners. The proposed algorithm uses two developments in combining projective geometry and statistics, which are described in this article: (i) the geometric entities points, lines and planes in 2D and 3D and their uncertainty are represented in homogeneous coordinates and new entities may be constructed including their propagated uncertainty. The construction can be performed directly or as an estimation. (ii) relations such as incidence, equality, parallelism and orthogonality between points, lines and planes can be tested statistically based on a given significance level. Using these tools, the resulting algorithm is straightforward and gives reasonable results. It is only based on geometric information and does not use any image intensities, though it can be extended to use other information. The matching of 3D lines does not need any thresholds other than a significance value for the hypotheses tests.

关键词： Testing Uncertainty image reconstruction image segmentation Geometry computer vision Covariance matrix Statistics Data mining Layout

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Segmentation and boundary detection using multiscale intensity measurements

Segmentation and boundary detection using multiscale intensi...

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conference on computer Vision and pattern recognition (CVPR)

作者： E. Sharon A. Brandt R. Basri Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

ISBN: (纸本)0769512720

image segmentation is difficult because objects may differ from their background by any of a variety of properties that can be observed in some, but often not all scales. A further complication is that coarse measurements, applied to the image for detecting these properties, often average over properties of neighboring segments, making it difficult to separate the segments and to reliably detect their boundaries. Below we present a method for segmentation that generates and combines multiscale measurements of intensity contrast, texture differences, and boundary integrity. The method is based on our former algorithm SWA, which efficiently detects segments that optimize a normalized-cut like measure by recursively coarsening a graph reflecting similarities between intensities of neighboring pixels. In this process aggregates of pixels of increasing size are gradually collected to form segments. We intervene in this process by computing properties of the aggregates and modifying the graph to reflect these coarse scale measurements. This allows us to detect regions that differ by fine as well as coarse properties, and to accurately locate their boundaries. Furthermore, by combining intensity differences with measures of boundary integrity across neighboring aggregates we can detect regions separated by weak, yet consistent edges.

关键词： image segmentation Aggregates image edge detection Pixel computer science Optimization methods Object detection Particle measurements Smoothing methods Buildings

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