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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6676 条记录，以下是4911-4920 订阅

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A probabilistic framework for graph clustering

A probabilistic framework for graph clustering

引用

conference on computer vision and pattern recognition (cvpr)

作者： Bin Luo A. Robles-Kelly A. Torsello R.C. Wilson E.R. Hancock Department of Computer Science University of York York UK

ISBN: (纸本)0769512720

The paper describes a probabilistic framework for graph clustering. We commence from a set of pairwise distances between graph structures. From this set of distances, we use a mixture model to characterize the pairwise affinity of the different graphs. We present an EM-like algorithm for clustering the graphs by iteratively updating the elements of the affinity matrix. In the M-step we apply eigendcomposition to the affinity matrix to locate the principal clusters. In the M-step we update the affinity probabilities. We apply the resulting unsupervised clustering algorithm to two practical problems. The first of these involves locating shape-categories using shock trees extracted from 2D silhouettes. The second problem involves finding the view structure of a polyhedral object using the Delaunay triangulation of corner features.

关键词： Clustering algorithms Iterative algorithms Tree graphs computer science Electric shock Machine learning Unsupervised learning Knowledge engineering pattern recognition computer vision

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Detection and tracking of shopping groups in stores

Detection and tracking of shopping groups in stores

引用

conference on computer vision and pattern recognition (cvpr)

作者： I. Haritaoglu M. Flickner IBM Almaden Research Center San Jose CA USA

ISBN: (纸本)0769512720

We describe a monocular real-time computer vision system that identifies shopping groups by detecting and tracking multiple people as they wait in a checkout line or service counter. Our system segments each frame into foreground regions which contains multiple people. Foreground regions are further segmented into individuals using a temporal segmentation of foreground and motion cues. Once a person is detected, an appearance model based on color and edge density in conjunction with a mean-shift tracker is used to recover the person's trajectory. People are grouped together as a shopping group by analyzing interbody distances. The system also monitors the cashier's activities to determine when shopping transactions start and end. Experimental results demonstrate the robustness and real-time performance of the algorithm.

关键词： Real time systems Cameras computer vision Counting circuits Trajectory Surveillance Layout Robustness Humans Information security

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Camera trajectory estimation using inertial sensor measurements and structure from motion results

Camera trajectory estimation using inertial sensor measureme...

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conference on computer vision and pattern recognition (cvpr)

作者： S.-H. Jung C.J. Taylor GRASP Laboratory CIS Department University of Pennsylvania Philadelphia PA USA

ISBN: (纸本)0769512720

This paper describes an approach to estimating the trajectory of a moving camera based on the measurements acquired with an inertial sensor and estimates obtained by applying a structure from motion algorithm to a small set of keyframes in the video sequence. The problem is formulated as an offline trajectory fitting task rather than an online integration problem. This approach avoids many of the issues usually associated with inertial estimation schemes. One of the main advantages of the proposed technique is that it can be applied in situations where approaches based on feature tracking would have significant difficulties. Results obtained by applying the procedure to extended sequences acquired with both conventional and omnidirectional cameras are presented.

关键词： Cameras Motion estimation Motion measurement Video sequences Sensor phenomena and characterization Stereo vision Layout Trajectory Laboratories Computational Intelligence society

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引用

conference on computer vision and pattern recognition (cvpr)

作者： C. Stauffer E. Grimson Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA

ISBN: (纸本)0769512720

This paper investigates applications of a new representation for images, the similarity template. A similarity template is a probabilistic representation of the similarity of pixels in an image patch. It has application to detection of a class of objects, because it is reasonably invariant to the color of a particular object. Further, it enables the decomposition of a class of objects into component parts over which robust statistics of color can be approximated. These regions can be used to create a factored color model that is useful for recognition. Detection results are shown on a system that learns to detect a class of objects (pedestrians) in static scenes based on examples of the object provided automatically by a tracking system. Applications of the factored color model to image indexing and anomaly detection are pursued on a database of images of pedestrians.

关键词： Object detection Face detection Vehicle detection Pixel Statistics Layout Image recognition Color Image edge detection Gray-scale

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Local analysis for 3D reconstruction of specular surfaces

Local analysis for 3D reconstruction of specular surfaces

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conference on computer vision and pattern recognition (cvpr)

作者： S. Savarese P. Perona California Institute of Technology Pasadena CA USA

ISBN: (纸本)0769512720

We explore the geometry linking the shape of a curved mirror surface to the distortions it produces on a scene it reflects. Our analysis is local and differential. We assume a simple calibrated scene composed of lines passing through a point. We demonstrate that local information about the geometry of the surface may be recovered up to the second order from either the orientation and curvature of the images of two intersecting lines, or from the orientation of the images of three or more intersecting lines. An explicit solution for calculating shape and position of spherical mirror surfaces is given.

关键词： Surface reconstruction Shape Mirrors Image reconstruction Layout Information geometry Optical reflection pattern analysis Cameras Postal services

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Linear image coding for regression and classification using the tensor-rank principle

Linear image coding for regression and classification using ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Shashua A. Levin School of Computer Science and Engineering Hebrew University Jerusalem Israel

ISBN: (纸本)0769512720

Given a collection of images (matrices) representing a "class" of objects we present a method for extracting the commonalities of the image space directly from the matrix representations (rather than from the vectorized representation which one would normally do in a PCA approach, for example). The general idea is to consider the collection of matrices as a tensor and to look for an approximation of its tensor-rank. The tensor-rank approximation is designed such that the SVD decomposition emerges in the special case where all the input matrices are the repeatition of a single matrix. We evaluate the coding technique both in terms of regression, i.e., the efficiency of the technique for functional approximation, and classification. We find that for regression the tensor-rank coding, as a dimensionality reduction technique, significantly outperforms other techniques like PCA. As for classification, the tensor-rank coding is at is best when the number of training examples is very small.

关键词： Image coding Principal component analysis Matrix decomposition computer science Face recognition Image recognition Independent component analysis Tensile stress Decorrelation computer vision

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Dimension recognition and geometry reconstruction in vectorization of engineering drawings

Dimension recognition and geometry reconstruction in vectori...

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conference on computer vision and pattern recognition (cvpr)

作者： Feng Su Jiqiang Song Chiew-Lan Tai Shijie Cai State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China

ISBN: (纸本)0769512720

This paper presents a novel approach for recognizing and interpreting dimensions in engineering drawings. It starts by detecting potential dimension frames, each comprising only the line and text components of a dimension, then verifies them by detecting the dimension symbols. By removing the prerequisite of symbol recognition from detection of dimension sets, our method is capable of handling low quality drawings. We also propose a reconstruction algorithm for rebuilding the drawing entities based on the recognized dimension annotations. A coordinate grid structure is introduced to represent and analyze two-dimensional spatial constraints between entities; this simplifies and unifies the process of rectifying deviations of entity dimensions induced during scanning and vectorization.

关键词： Engineering drawings Graphics Image converters Reconstruction algorithms Length measurement Goniometers Computational geometry Laboratories computer science Image reconstruction

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Feature reduction and hierarchy of classifiers for fast object detection in video images

Feature reduction and hierarchy of classifiers for fast obje...

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conference on computer vision and pattern recognition (cvpr)

作者： B. Heisele T. Serre S. Mukherjee T. Poggio Center for Biological and Computational Learning M I T Lincoln Laboratory Cambridge MA USA

ISBN: (纸本)0769512720

We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform feature reduction by choosing relevant image features according to a measure derived from statistical learning theory. In a second step we build a hierarchy of classifiers. On the bottom level, a simple and fast classifier analyzes the whole image and rejects large parts of the background On the top level, a slower but more accurate classifier performs the final detection. Experiments with a face detection system show that combining feature reduction with hierarchical classification leads to a speed-up by a factor of 170 with similar classification performance.

关键词： Object detection Face detection Support vector machines Support vector machine classification Filters computer vision Image analysis Classification algorithms Biology computing Research and development

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On representing edge structure for model matching

On representing edge structure for model matching

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conference on computer vision and pattern recognition (cvpr)

作者： T.F. Cootes C.J. Taylor Department Imaging Science and Biomedical Engineering University of Manchester Institute of Science and Technology Manchester UK

ISBN: (纸本)0769512720

We show how a novel, non-linear representation of edge structure can be used to improve the performance of model matching algorithms and object verification/recognition tasks. Rather than represent the image structure using intensity values or gradients, we use a measure which indicates the orientation of structures at each pixel, together with an indication of how reliable the orientation estimate is. Orientations in flat, noisy regions tend to be penalised whereas those near strong edges are favoured. We demonstrate that this representation leads to more accurate and reliable matching between models and new images, and leads to better recognition/verification of faces in an access control task.

关键词： Pixel Active appearance model Face recognition Image recognition Vectors Biomedical imaging Biomedical engineering Access control Buildings Brightness

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Unsupervised face recognition from image sequences based on clustering with attraction and repulsion

Unsupervised face recognition from image sequences based on ...

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conference on computer vision and pattern recognition (cvpr)

作者： B. Raytchev H. Murase NTT Communication Science Laboratories Atsugi Kanagawa Japan

ISBN: (纸本)0769512720

We propose a new method for unsupervised face recognition from time-varying sequences of face images obtained in real-world environments. Two types of forces, attraction and repulsion, operate across the spatio-temporal facial manifolds, to autonomously organize the data without relying on any category-specific information provided in advance. Experiments with real-world data gathered over a period of several months and including both frontal and side-view faces were used to evaluate the method and encouraging results were obtained The proposed method can be used in video surveillance systems or for content-based information retrieval.

关键词： Face recognition Image sequences Humans Biology computing Video surveillance Information retrieval Content based retrieval Unsupervised learning Supervised learning Lighting

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