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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"

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Efficient non-parametric adaptive color modeling using fast Gauss transform

Efficient non-parametric adaptive color modeling using fast ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Elgammal R. Duraiswami L.S. Davis Computer Vision Laboratory University of Maryland College Park MD USA

ISBN: (纸本)0769512720

Modeling the color distribution of a homogeneous region is used extensively for object tracking and recognition applications. The color distribution of an object represents a feature that is robust to partial occlusion, scaling and object deformation. A variety of parametric and non-parametric statistical techniques have been used to model color distributions. In this paper we present a non-parametric color modeling approach based on kernel density estimation as well as a computational framework for efficient density estimation. Theoretically, our approach is general since kernel density estimators can converge to any density shape with sufficient samples. Therefore, this approach is suitable to model the color distribution of regions with patterns and mixture of colors. Since kernel density estimation techniques are computationally expensive, the paper introduces the use of the fast Gauss transform for efficient computation of the color densities. We show that this approach can be used successfully for color-based segmentation of body parts as well as segmentation of many people under occlusion.

关键词： Gaussian processes Histograms Kernel Robustness computer vision Laboratories Educational institutions Shape Clothing Surface fitting

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Anti-sequences: event detection by frame stacking

Anti-sequences: event detection by frame stacking

引用

conference on computer vision and pattern recognition (cvpr)

作者： M. Osadchy D. Keren Y. Gal Department of Computer Science University of Haifa Haifa Israel

ISBN: (纸本)0769512720

This paper presents a natural extension of the newly introduced "anti-face" method to event detection, both in the image and in the feature domains. In the case of the image domain (video sequences) we create spatio temporal templates by stacking the video frames, and the detection is performed on these templates. In order to recognize the motion of features in a video sequence, the spatial locations of the features are modulated in time, thus creating a one-dimensional vector which re resents the event The following applications of anti-sequences are presented 1) Detection of an object under 3D rotations in a video sequence simulated from the COIL database, 2) Visual speech recognition of spoken words, and 3) recognition of symbols sketched with a laser pointer. The resulting detection algorithm is very fast, and is robust enough to work on small images. Also, it is capable of discriminating the desired event-template from arbitrary events, and not only events in a "negative training set".

关键词： Event detection Stacking Video sequences Speech recognition Object detection Motion detection Discrete event simulation Image databases Spatial databases Visual databases

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A new 3-D pattern recognition technique with application to computer aided colonoscopy

A new 3-D pattern recognition technique with application to ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： S.B. Gokturk C. Tomasi Electrical Engineering Department University of Stanford Stanford CA USA Computer Science Department University of Stanford Stanford CA USA

ISBN: (纸本)0769512720

To utilize CT or MRI images for computer aided diagnosis applications, robust features that represent 3D image data need to be constructed and subsequently used by a classification method. We present a computer aided diagnosis system for early diagnosis of colon cancer. The system extracts features via a new 3D pattern processing method and processes them using a support vector machine classifier. Our 3D pattern processing method, called Random Orthogonal Shape Section (ROSS) mimics the radiologist's way of viewing these images and combines information from many random triples of mutually orthogonal sections going through the volume. Another contribution of the paper is a new feedback framework between the classification algorithm and the definition of the features. This framework, called Distinctive Component Analysis combines support vector samples with linear discriminant analysis to map the features of clustered support vectors to a lower dimensional space where the two classes of objects of interest are optimally separated to obtain better features. We show that the combination of these better features with support vector machine classification provides a good recognition rate.

关键词： pattern recognition Application software computer applications Colonoscopy Cancer Support vector machine classification Computed tomography Magnetic resonance imaging Robustness Colonography

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A Bayesian approach to digital matting

A Bayesian approach to digital matting

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yung-Yu Chuang B. Curless D.H. Salesin R. Szeliski Department of Computer Science and Engineering University of Washington Seattle WA USA Microsoft Research Limited Redmond WA USA

ISBN: (纸本)0769512720

This paper proposes a new Bayesian framework for solving the matting problem, i.e. extracting a foreground element from a background image by estimating an opacity for each pixel of the foreground element. Our approach models both the foreground and background color distributions with spatially-varying sets of Gaussians, and assumes a fractional blending of the foreground and background colors to produce the final output. It then uses a maximum-likelihood criterion to estimate the optimal opacity, foreground and background simultaneously. In addition to providing a principled approach to the matting problem, our algorithm effectively handles objects with intricate boundaries, such as hair strands and fur, and provides an improvement over existing techniques for these difficult cases.

关键词： Bayesian methods Pixel Hair Production computer science Gaussian distribution Maximum likelihood estimation Layout Art Humans

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A PDE approach for measuring tissue thickness

A PDE approach for measuring tissue thickness

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Yezzi J.L. Prince School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

ISBN: (纸本)0769512720

We outline an Eulerian framework for computing the thickness of tissues between two simply connected boundaries. Thickness is defined as the length of trajectories which follow a smooth vector field constructed in the region between the boundaries. A pair of partial differential equations (PDEs) are then solved and combined to yield length without requiring the explicit construction of the trajectories. An efficient, stable, and computationally fast solution to these PDEs is found by careful selection of finite differences according to an upwinding condition. The behavior and performance of the method is demonstrated on two simulations and two magnetic resonance imaging data sets in two and three dimensions. These experiments reveal very good performance and show strong potential for application in tissue thickness visualization and quantification.

关键词： Thickness measurement Myocardium Alzheimer's disease Image segmentation Magnetic resonance imaging Data visualization Biomedical imaging Image analysis Cardiac disease Cardiovascular diseases

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Dynamic coupled component analysis

Dynamic coupled component analysis

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conference on computer vision and pattern recognition (cvpr)

作者： F. De la Torre M.J. Black Departament de Comunicacions i Teoria del Senyal Escola d'Enginyeria la Salle Universitat Ramon LLull Barcelona Spain Department of Computer Science Brown University Providence RI USA

ISBN: (纸本)0769512720

We present a method for simultaneously learning linear models of multiple high dimensional data sets and the dependencies between them. For example, we learn asymmetrically coupled linear models for the faces of two different people and show how these models can be used to animate one face given a video sequence of the other. We pose the problem as a form of Asymmetric Coupled Component Analysis (ACCA) in which we simultaneously learn the subspaces for reducing the dimensionality of each dataset while coupling the parameters of the low dimensional representations. Additionally, a dynamic form of ACCA is proposed, that extends this work to model temporal dependencies in the data sets. To account for outliers and missing data, we formulate the problem in a statistically robust estimation framework. We review connections with previous work and illustrate the method with examples of synthesized dancing and the animation of facial avatars.

关键词： Principal component analysis Facial animation Training data computer vision computer science Video sequences Robustness Avatars Face recognition Linear approximation

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Local feature view clustering for 3D object recognition

Local feature view clustering for 3D object recognition

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conference on computer vision and pattern recognition (cvpr)

作者： D.G. Lowe Computer Science Department University of British Columbia Vancouver BC Canada

ISBN: (纸本)0769512720

There have been important recent advances in object recognition through the matching of invariant local image features. However, the existing approaches are based on matching to individual training images. This paper presents a method for combining multiple images of a 3D object into a single model representation. This provides for recognition of 3D objects from any viewpoint, the generalization of models to non-rigid changes, and improved robustness through the combination of features acquired under a range of imaging conditions. The decision of whether to cluster a training image into an existing view representation or to treat it as a new view is based on the geometric accuracy of the match to previous model views. A new probabilistic model is developed to reduce the false positive matches that would otherwise arise due to loosened geometric constraints on matching 3D and non-rigid models. A system has been developed based on these approaches that is able to robustly recognize 3D objects in cluttered natural images in sub-second times.

关键词： Object recognition Image recognition Robustness Solid modeling Image databases Spatial databases computer science Layout Lighting Performance evaluation

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Stereo head calibration from a planar object

Stereo head calibration from a planar object

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conference on computer vision and pattern recognition (cvpr)

作者： H. Malm A. Heyden Centre for Mathematical Sciences Lund University Lund Sweden

ISBN: (纸本)0769512720

A technique for stereo camera calibration from a known planar calibration object is proposed. Both the intrinsic parameters and the relative orientation are calculated. The proposed algorithm uses the two homogeneous linear constraints on the image of the absolute conic arising from the plane homographies for each camera and position. In addition to these, linear constraints on the relation between the images of the absolute conic for the two cameras in the stereo pair are calculated. The derivation of these constraints is made possible by considering the rigidity of the stereo pair. Using all these constraints simultaneously in a linear system gives a stereo calibration technique that is less sensitive to noise, compared to just using the single camera constraints. As a part of the algorithm, a simple way to calculate the infinite homography from two stereo views of a plane is *** performance of the algorithm is shown in experiments on both simulated and real data.

关键词： Calibration Robot vision systems Robot kinematics Transmission line matrix methods Head Linear systems Digital cameras Robot sensing systems Councils Reliability engineering

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Understanding popout through repulsion

Understanding popout through repulsion

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conference on computer vision and pattern recognition (cvpr)

作者： S.X. Yu J. Shi Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)0769512720

Perceptual popout is defined by both feature similarity and local feature contrast. We identify these two measures with attraction and repulsion, and unify the dual processes of association by attraction and segregation by repulsion in a single grouping framework. We generalize normalized cuts to multi-way partitioning with these dual measures. We expand graph partitioning approaches to weight matrices with negative entries, and provide a theoretical basis for solution regularization in such algorithms. We show that attraction, repulsion and regularization each contributes in a unique way to popout. Their roles are demonstrated in various salience detection and visual search scenarios. This work opens up the possibilities of encoding negative correlations in constraint satisfaction problems, where solutions by simple and robust eigendecomposition become possible.

关键词： Image segmentation Brain modeling Image edge detection Filters Cognitive robotics Partitioning algorithms Encoding Robustness Feature extraction Object recognition

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Discrimination of motion based on traces in the space of probability functions over feature relations

Discrimination of motion based on traces in the space of pro...

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conference on computer vision and pattern recognition (cvpr)

作者： S. Sarkar I.R. Vega Computer Science and Engineering Department University of South Florida Tampa FL USA

ISBN: (纸本)0769512720

In this paper we demonstrate that it is possible to discriminate between high level motion types such as walking, jogging, or running based on just the change in the relational statistics among the detected image features, without the need for object models, perfect segmentation, or tracking. Instead of the statistics of the feature attributes themselves, we consider the distribution of the statistics of the relations among the features. We represent the observed distribution of feature relations in an image as a point in a space where the Euclidean distance is related to the Bhattacharya distance between probability functions. Different motion types sweep out different traces in this Space of Probability Functions (SoPF). We demonstrate the effectiveness of this representation on image sequences of human in motion, gathered using a digital video camera. We show that it is not only possible to distinguish between motion types but also to discriminate between persons based on the SoPF traces.

关键词： Probability Statistical distributions Legged locomotion Object detection Motion detection computer vision Image segmentation Tracking Euclidean distance Image sequences

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