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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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Local discriminant embedding and its variants

Local discriminant embedding and its variants

引用

conference on computer vision and pattern recognition (cvpr)

作者： Hwann-Tzong Chen Huang-Wei Chang Tyng-Luh Liu Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (纸本)0769523722

We present a new approach, called local discriminant embedding (LDE), to manifold learning and pattern classification. In our framework, the neighbor and class relations of data are used to construct the embedding for classification problems. The proposed algorithm learns the embedding for the submanifold of each class by solving an optimization problem. After being embedded into a low-dimensional subspace, data points of the same class maintain their intrinsic neighbor relations, whereas neighboring points of different classes no longer stick to one another. Via embedding, new test data are thus more reliably classified by the nearest neighbor rule, owing to the locally discriminating nature. We also describe two useful variants: two-dimensional LDE and kernel LDE. Comprehensive comparisons and extensive experiments on face recognition are included to demonstrate the effectiveness of our method.

关键词： Testing Training data Linear discriminant analysis Principal component analysis pattern classification Kernel Nearest neighbor searches Face recognition Information science Maintenance

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Real-time connectivity constrained depth map computation using programmable graphics hardware

Real-time connectivity constrained depth map computation usi...

引用

conference on computer vision and pattern recognition (cvpr)

作者： N. Cornells L. Van Gool Katholieke Univ. Leuven Belgium K. U. Leuven ESAT-PSI Leuven-Heverlee Belgium

This paper presents a passive depth map computation algorithm which runs entirely on programmable 3D graphics hardware at real-time speed. Previous implementations relied on the use of either mipmapping or correlation windows to increase the robustness of the calculated depth map. These methods, however, are inherently unable to compute the depths for fine 3D structures. To overcome this obstacle we introduce a pixel-based method which incorporates connectivity information between neighboring pixels, while maintaining the performance boost gained by implementing the algorithm on a highly parallelized architecture such as graphics hardware.

关键词： Hardware Robustness Iterative algorithms computer architecture Moore's Law Software performance Cameras Frequency estimation computer graphics Supercomputers

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Random subspaces and subsampling for 2-D face recognition

Random subspaces and subsampling for 2-D face recognition

引用

conference on computer vision and pattern recognition (cvpr)

作者： N.V. Chawla K.W. Bowyer Department of Computer Science and Engineering University of Notre Dame Notre Dame IN USA

Random subspaces are a popular ensemble construction technique that improves the accuracy of weak classifiers. It has been shown, in different domains, that random subspaces combined with weak classifiers such as decision trees and nearest neighbor classifiers can provide an improvement in accuracy. In this paper, we apply the random subspace methodology to the 2-D face recognition task. The main goal of the paper is to see if the random subspace methodology can do as well, if not better, than the single classifier constructed on the tuned face space. We also propose the use of a validation set for tuning the face space, to avoid bias in the accuracy estimation. In addition, we also compare the random subspace methodology to an ensemble of subsamples of image data. This work shows that a random subspaces ensemble can outperform a well-tuned single classifier for a typical 2-D face recognition problem. The random subspaces approach has the added advantage of requiring less careful tweaking.

关键词： Face recognition Nearest neighbor searches Principal component analysis Filtering Testing Classification tree analysis computer science Decision trees Pixel pattern recognition

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An Evaluation of the Suitability of FPGAs for Embedded vision Systems

An Evaluation of the Suitability of FPGAs for Embedded Visio...

引用

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

作者： W.J. MacLean Department of Electrical & Computer Engineering University of Toronto Toronto ONT Canada

Reconfigurable hardware, in the form of Field Programmable Gate Arrays (FPGAs), is becoming increasingly attractive for digital signal processing problems, including image processing and computer vision tasks. The ability to exploit the parallelism often found in these problems, as well as the ability to support different modes of operation on a single hardware substrate, gives these devices a particular advantage over fixed architecture devices such as serial CPUs and DSPs. Further, development times are substantially shorter than dedicated hardware in the form of Application Specific ICs (ASICs), and small changes to a design can be prototyped in a matter of hours. On the other hand, designing with FPGAs still requires expertise beyond that found in many vision labs today. This paper looks at the advantages and disadvantages of FPGA technology, its suitability for image processing and computer vision tasks, and attempts to suggest some directions for the future.

关键词： Field programmable gate arrays Machine vision computer vision Hardware Digital signal processing Signal processing algorithms Stereo vision Sensor arrays Image processing computer architecture

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Coupled PDEs for non-rigid registration and segmentation

Coupled PDEs for non-rigid registration and segmentation

引用

conference on computer vision and pattern recognition (cvpr)

作者： G. Unal G. Slabaugh Intelligent Vision and Reasoning Department Siemens Corporate Research Inc. Princeton NJ USA

In this paper we present coupled partial differential equations (PDEs) for the problem of joint segmentation and registration. The registration component of the method estimates a deformation field between boundaries of two structures. The desired coupling comes from two PDEs that estimate a common surface through segmentation and its non-rigid registration with a target image. The solutions of these two PDEs both decrease the total energy of the surface, and therefore aid each other in finding a locally optimal solution. Our technique differs from recently popular joint segmentation and registration algorithms, all of which assume a rigid transformation among shapes. We present both the theory and results that demonstrate the effectiveness of the approach.

关键词： Image segmentation Shape Biomedical imaging Medical diagnostic imaging Application software Partial differential equations Joints Anatomical structure Deformable models Image motion analysis

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Fields of Experts: a framework for learning image priors

Fields of Experts: a framework for learning image priors

引用

conference on computer vision and pattern recognition (cvpr)

作者： S. Roth M.J. Black Department of Computer Science Brown University Providence RI USA

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 nonlinear 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： Nonlinear filters Statistics Machine vision Markov random fields Image denoising Training data Image databases Image coding PSNR computer science

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MER-DIMES: a planetary landing application of computer vision

MER-DIMES: a planetary landing application of computer visio...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yang Cheng A. Johnson L. Matthies Jet Propulsion Laboratory California Institute of Technology USA

During the Mars Exploration Rover (MER) landings, the descent image motion estimation system (DIMES) was used for horizontal velocity estimation. The DIMES algorithm combined measurements from a descent camera, a radar altimeter, and an inertial measurement unit. To deal with large changes in scale and orientation between descent images, the algorithm used altitude and attitude measurements to rectify images to a level ground plane. Feature selection and tracking were employed in the rectified images to compute the horizontal motion between images. Differences of consecutive motion estimates were then compared to inertial measurements to verify correct feature tracking. DIMES combined sensor data from multiple sources in a novel way to create a low-cost, robust, and computationally efficient velocity estimation solution, and DIMES was the first use of computer vision to control a spacecraft during planetary landing. This paper presents the detailed implementation of the DIMES algorithm and the results from the two landings on Mars.

关键词： Machine vision Motion analysis Space vehicles Multisensor systems Estimation

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Efficient multiclass object detection by a hierarchy of classifiers

Efficient multiclass object detection by a hierarchy of clas...

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conference on computer vision and pattern recognition (cvpr)

作者： Xiaodong Fan The Department of Electrical and Compute Engineering Johns Hopkins University-Applied Physics Laboratory Baltimore MD USA

ISBN: (纸本)0769523722

We consider detecting object instances from multiple classes on grayscale images. Traditional approaches learn a classifier for each class separately and apply each of them in an exhaustive search over positions and scales. We achieve an efficient detection by organizing the search coarse-to-fine based on a hierarchical partitioning of the entire hypothesis space, the set of all possible object instances, so that groups of hypotheses can be pruned simultaneously without evaluating each one individually. In this paper, we develop an algorithm to jointly learn the hierarchy along with a classifier at each node by exploring the common parts shared among a group of object instances at all levels in the hierarchy. We also show how the confusions of the initial coarse-to-fine search can be resolved by comparing pairs of conflicting detections using cheap binary classifiers. The whole process is illustrated by detecting and recognizing handwritten digits.

关键词： Object detection computer vision Gray-scale Organizing Partitioning algorithms Handwriting recognition Image edge detection Detectors Testing Robustness

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Two-view multibody structure-and-motion with outliers

Two-view multibody structure-and-motion with outliers

引用

conference on computer vision and pattern recognition (cvpr)

作者： K. Schindler D. Suter Electrical and Computer Systems Engineering Monash University Clayton VIC Australia

Multi-body structure-and-motion (MSaM) is the problem to establish the multiple-view geometry of several views of a 3D scene taken at different times, where the scene consists of multiple rigid objects moving relative to each other. We examine the case of two views. The setting is the following: given are a set of corresponding image points in two images, which originate from an unknown number of moving scene objects, each giving rise to a motion model. Furthermore, the measurement noise is unknown, and there are a number of gross errors, which are outliers to all models. The task to find an optimal set of motion models for the measurements is solved through Monte-Carlo sampling, careful statistical analysis of the data and simultaneous selection of multiple motion models.

关键词： Layout Transmission line matrix methods Motion analysis Statistical analysis Cameras Solid modeling Motion estimation Systems engineering and theory Australia Computational geometry

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Towards a Practical Stereo vision Sensor

Towards a Practical Stereo Vision Sensor

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

作者： M. Bansal A. Jain T. Camus A. Das Sarnoff Corporation Princeton NJ USA

Development of a practical stereo vision sensor for real-world applications must account for the variability of high-volume production processes and the impact of unknown environmental conditions during its operation. One critical factor of stereo depth estimation performance is the relative alignment of the stereo camera pair. While imperfectly aligned stereo cameras may be rectified in the image domain, there are some errors introduced by both the calibration recovery and image rectification processes. Finally, additional uncalibrated misalignments, for example due to thermal or mechanical deformation in a harsh automotive environment, may occur which will further deteriorate stereo depth estimation. This paper describes an experimental framework for determining these limits using image processing algorithms, operating on graphically synthesized imagery, with performance envelope validation on real stereo image data.

关键词： Stereo vision Cameras Calibration Automotive engineering Layout Production Thermal sensors Robustness computer vision Image processing

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