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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

共 6545 条记录，以下是191-200 订阅

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A sparse object category model for efficient learning and exhaustive recognition

A sparse object category model for efficient learning and ex...

引用

2005 ieee computer society conference on computer vision and pattern recognition, cvpr 2005

作者： Fergus, R. Perona, A. Zisserman, A. Dept. of Engineering Science University of Oxford Parks Road Oxford OX1 3PJ United Kingdom Dept. of Electrical Engineering California Institute of Technology MC 136-93 Pasadena CA 91125 United States

ISBN: (纸本)0769523722

We present a "parts and structure" model for object category recognition that can be learnt efficiently and in a semisupervised manner: the model is learnt from example images containing category instances, without requiring segmentation from background clutter. The model is a sparse representation of the object, and consists of a star topology configuration of parts modeling the output of a variety of feature detectors. The optimal choice of feature types (whose repertoire includes interest points, curves and regions) is made automatically. In recognition, the model may be applied efficiently in an exhaustive manner, bypassing the need for feature detectors, to give the globally optimal match within a query image. The approach is demonstrated on a wide variety of categories, and delivers both successful classification and localization of the object within the image. © 2005 ieee.

关键词： Object recognition

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Evaluating image retrieval

Evaluating image retrieval

引用

2005 ieee computer society conference on computer vision and pattern recognition, cvpr 2005

作者： Shirahatti, Nikhil V. Barnard, Kobus Electrical and Computer Engineering University of Arizona United States Department of Computer Science University of Arizona United States

ISBN: (纸本)0769523722

We present a comprehensive strategy for evaluating image retrieval algorithms. Because automated image retrieval is only meaningful in its service to people, performance characterization must be grounded in human evaluation. Thus we have collected a large data set of human evaluations of retrieval results, both for query by image example and query by text. The data is independent of any particular image retrieval algorithm and can be used to evaluate and compare many such algorithms without further data collection. The data and calibration software are available on-line (http://***/research/data). We develop and validate methods for generating sensible evaluation data, calibrating for disparate evaluators, mapping image retrieval system scores to the human evaluation results, and comparing retrieval systems. We demonstrate the process by providing grounded comparison results for several algorithms. © 2005 ieee.

关键词： Image retrieval

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Texture features and learning similarity

Texture features and learning similarity

引用

1966 ieee computer society conference on computer vision and pattern recognition

作者： Ma, WY Manjunath, BS UNIV CALIF SANTA BARBARA DEPT ELECT & COMP ENGNSANTA BARBARACA 93106

ISBN: (纸本)0818672587

This paper addresses two important issues related to texture pattern retrieval: feature extraction and similarity search. A Gabor feature representation for textured images is proposed, and its performance in pattern retrieval is evaluated on a large texture image database. These features compare favorably with other existing texture representations. A simple hybrid neural network algorithm is used to learn the similarity by simple clustering in the texture feature space. With learning similarity, the performance of similar pattern retrieval improves significantly. An important aspect of this work is its application to real image data. Texture feature extraction with similarity learning is used to search through large aerial photographs. Feature clustering enables efficient search of the database as our experimental results indicate.

关键词： computer vision

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Facial expression recognition and its degree estimation

Facial expression recognition and its degree estimation

引用

1997 ieee computer society conference on computer vision and pattern recognition (cvpr 97)

作者： Kimura, S Yachida, M Lab of Image Information Science and Technology Osaka Japan

ISBN: (纸本)0780342364

The purpose of this study is not only to recognize some kind of facial expressions which is associated with human emotion but also to estimate its degree. Our method is based on the idea that facial expression recognition can be achieved by extracting a variation from expressionless face with considering face area as a whole pattern. For the purpose of extracting subtle changes in the face such as the degree of expressions, it is necessary to eliminate the individuality appearing in the facial image. Using a elastic net model, a variation of facial expression is represented as motion vectors of the deformed Net from a facial edge image. Then, applying K-L expansion, the change of facial expression represented as the motion vectors of nodes is mapped into low dimensional eigen space, and estimation is achieved by projecting input images on to the Emotion Space. In this paper we have constructed three kinds of expression models: happiness, anger, surprise, curd experimental results are evaluated.

关键词： Object recognition

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A generative model of human hair for hair sketching

A generative model of human hair for hair sketching

引用

2005 ieee computer society conference on computer vision and pattern recognition, cvpr 2005

作者： Chen, Hong Zhu, Song Chun Departments of Statistics and Computer Science University of California Los Angeles United States

ISBN: (纸本)0769523722

Human hair is a very complex visual pattern whose representation is rarely studied in the vision literature despite its important role in human recognition. In this paper, we propose a generative model for hair representation and hair sketching, which is far more compact than the physically based models in graphics. We decompose a color hair image into three bands: a color band (a) (by Luv transform), a low frequency band (b)for lighting variations, and a high frequency band (c)for the hair pattern. Then we propose a three level generative model for the hair image (c). In this model, image (c) is generated by a vector field (d) that represents hair orientation, gradient strength, and directions;and this vector field is in turn generated by a hair sketch layer (e). We identify five types of primitives for the hair sketch each specifying the orientations of the vector field on the two sides of the sketch. With the five-layer representation (a-e) computed, we can reconstruct vivid hair images and generate hair sketches. We test our algorithm on a large data set of hairs and some results are reported in the experiments. © 2005 ieee.

关键词： pattern recognition systems

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Fields of experts: A framework for learning image priors

Fields of experts: A framework for learning image priors

引用

2005 ieee computer society conference on computer vision and pattern recognition, cvpr 2005

作者： Roth, Stefan Black, Michael J. Department of Computer Science Brown University Providence RI United States

ISBN: (纸本)0769523722

We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The approach extends traditional Markov Random Field (MRF) models by learning potential functions over extended pixel neighborhoods. Field potentials are modeled using a Products-of-Experts framework that exploits non-linear functions of many linear filter responses. In contrast to previous MRF approaches all parameters, including the linear filters themselves, are learned from training data. We demonstrate the capabilities of this Field of Experts model with two example applications, image denoising and image inpainting, which are implemented using a simple, approximate inference scheme. While the model is trained on a generic image database and is not tuned toward a specific application, we obtain results that compete with and even outperform specialized techniques.

关键词： Image analysis

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Human activity recognition in thermal infrared imagery

Human activity recognition in thermal infrared imagery

引用

2005 ieee computer society conference on computer vision and pattern recognition, cvpr 2005 - Workshops

作者： Han, Ju Bhanu, Bir Center for Research in Intelligent Systems University of California RiversideCA92521 United States

ISBN: (纸本)0769526608

In this paper, we investigate human repetitive activity properties from thermal infrared imagery, where human motion can be easily detected from the background regardless of lighting conditions and colors of the human surfaces and backgrounds. We employ an efficient spatiotemporal representation for human repetitive activity recognition, which represents human motion sequence in a single image while preserving some temporal information. A statistical approach is used to extract features for activity recognition. Experimental results show that the proposed approach achieves good performance for human repetitive activity recognition. © 2005 ieee computer society. All rights reserved.

关键词： Infrared radiation

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Multi-scale classification of 3-D objects

Multi-scale classification of 3-D objects

引用

1997 ieee computer society conference on computer vision and pattern recognition (cvpr 97)

作者： Zhang, DM Hebert, M Carnegie Mellon Univ Pittsburgh United States

ISBN: (纸本)0780342364

We describe an approach to the classification of 3-D objects using a multi-scale representation. This approach starts with a smoothing algorithm for representing objects at different scales. Smoothing is applied in curvature space directly, thus avoiding the usual shrinkage problems and allowing for efficient implementations. A 3-D similarity measure that integrates the representations of the objects at multiple scales is introduced Given a library of models, objects that are similar based an this multi-scale measure are grouped together into classes. Thtr objects that are in the same class ave combined into a single prototype object. Finally the prototypes are used for hierarchical recognition by first comparing the scene representation to the prototypes and then matching it only to the objects in the most likely class rather than to the entire library of models. Beyond its application to object recognition, this approach provides an attractive implementation of the intuitive nations of scale and approximate similarity for 3-D shapes.

关键词： Object recognition

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Accurate and efficient stereo processing by semi-global matching and mutual information

Accurate and efficient stereo processing by semi-global matc...

引用

conference on computer vision and pattern recognition

作者： Hirschmüller, H DLR German Aerosp Ctr Inst Robot & Mechatron Oberpfafenhofen D-82230 Wessling Germany

ISBN: (纸本)0769523722

This paper considers the objectives of accurate stereo matching, especially at object boundaries, robustness against recording or illumination changes and efficiency of the calculation. These objectives lead to the proposed Semi-Global Matching method that performs pixelwise matching based on Mutual Information and the approximation of a global smoothness constraint. Occlusions are detected and disparities determined with sub-pixel accuracy. Additionally, an extension for multi-baseline stereo images is presented. There are two novel contributions. Firstly, a hierarchical calculation of Mutual Information based matching is shown, which is almost as fast as intensity based matching. Secondly, an approximation of a global cost calculation is proposed that can be performed in a time that is linear to the number of pixels and disparities. The implementation requires just l second on typical images.

关键词： Mutual information Costs Lighting Image reconstruction Reflection Pixel Stereo vision Robustness Belief propagation Geometry

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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

作者： Chum, O Matas, J Czech Tech Univ Dept Cybernet Ctr Machine Percept CR-16635 Prague Czech Republic

ISBN: (纸本)0769523722

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： pattern matching

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