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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"

共 6858 条记录，以下是4781-4790 订阅

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Ridge's corner detection and correspondence

Ridge's corner detection and correspondence

引用

conference on computer vision and pattern recognition (cvpr)

作者： F. Shilat M. Werman Y. Gdalyahn Institute of Computer Science Hebrew University of Jerusalem Jerusalem Israel Hebrew University of Jerusalem Jerusalem Jerusalem IL

Traditionally, corners are found along step edges. In this paper we present an alternative approach-corners along ridges/troughs and local minima points. These features seem to be more reliable for tracking. A new app... 详细信息

关键词： Image edge detection computer vision Layout Calibration computer science Navigation Tensile stress Image sequences Motion estimation Detectors

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Graphical models for graph matching

Graphical models for graph matching

引用

conference on computer vision and pattern recognition (cvpr)

作者： T.S. Caetano T. Caelli D.A.C. Barone Instituto de Informática Universidade Federal do Rio Grande do Sul Porto Alegre Rio Grande do Sul Brazil Department of Computing Science University of Alberta Edmonton AB Canada

This paper explores a formulation for attributed graph matching as an inference problem over a hidden Markov random field. We approximate the fully connected model with simpler models in which optimal inference is feasible, and contrast them to the well-known probabilistic relaxation method, which can operate over the complete model but does not assure global optimality. The approach is well suited for applications in which there is redundancy in the binary attributes of the graph, such as in the matching of straight line segments. Results demonstrate that, in this application, the proposed models have superior robustness over probabilistic relaxation under additive noise conditions.

关键词： Graphical models Hidden Markov models Least squares approximation pattern recognition Context modeling Dynamic programming Layout Relaxation methods Noise robustness Additive noise

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A model for dynamic shape and its applications

A model for dynamic shape and its applications

引用

conference on computer vision and pattern recognition (cvpr)

作者： Che-Bin Liu N. Ahuja Beckman Institute University of Illinois Urbana-Champaign Urbana-Champaign IL USA

Variation in object shape is an important visual cue for deformable object recognition and classification. In this paper, we present an approach to model gradual changes in the 2-D shape of an object. We represent 2-D region shape in terms of the spatial frequency content of the region contour using Fourier coefficients. The temporal changes in these coefficients are used as the temporal signatures of the shape changes. Specifically, we use autoregressive model of the coefficient series. We demonstrate the efficacy of the model on several applications. First, we use the model parameters as discriminating features for object recognition and classification. Second, we show the use of the model for synthesis of dynamic shape using the model learned from a given image sequence. Third, we show that, with its capability of predicting shape, the model can be used to predict contours of moving regions, which can be used as initial estimates for the contour, based tracking methods.

关键词： Shape Predictive models Object recognition Fires Video sequences Image sequences computer vision Fourier transforms Algorithm design and analysis Parameter estimation

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Multi-stage target recognition using modular vector quantizers and multilayer perceptrons

Multi-stage target recognition using modular vector quantize...

引用

conference on computer vision and pattern recognition (cvpr)

作者： L.A. Chan N.M. Nasrabadi V. Mirelli Department of Electrical and Computer Engineering State University of New York University at Buffalo Buffalo NY USA US Army Research Laboratory Fort Belvoir VA USA

An automatic target recognition (ATR) classifier is proposed that uses modularly cascaded vector quantizers (VQs) and multilayer perceptrons (MLPs). A dedicated VQ codebook is constructed for each target class at a specific range of aspects, which is trained with the K-means algorithm and a modified learning vector quantization (LVQ) algorithm. Each final codebook is expected to give the lowest mean squared error (MSE) for its correct target class at a given range of aspects. These MSEs are then processed by an array of window MLPs and a target MLP consecutively. In the spatial domain, target recognition rates of 90.3 and 65.3 percent are achieved for moderately and highly cluttered test sets, respectively. Using the wavelet decomposition with an adaptive and independent codebook per sub-band, the VQs alone have produced recognition rates of 98.7 and 69.0 percent on more challenging training and test sets, respectively.

关键词： Target recognition Multilayer perceptrons Vector quantization Books Pixel Error correction codes Automatic testing System testing Area measurement

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Statistical feature fusion for gait-based human recognition

Statistical feature fusion for gait-based human recognition

引用

conference on computer vision and pattern recognition (cvpr)

作者： Ju Han B. Bhanu Center for Research in Intelligent Systems University of California Riverside CA USA

This paper presents a novel approach for human recognition by combining statistical gait features from real and synthetic templates. Real templates are directly computed from training silhouette sequences, while synthetic templates are generated from training sequences by simulating silhouette distortion. A statistical feature extraction approach is used for learning effective features from real and synthetic templates. Features learned from real templates characterize human walking properties provided in training sequences, and features learned from synthetic templates predict gait properties under other conditions. A feature fusion strategy is therefore applied at the decision level to improve recognition performance. We apply the proposed approach to USF HumanID Database. Experimental results demonstrate that the proposed fusion approach not only achieves better performance than individual approaches, but also provides large improvement in performance with respect to the baseline algorithm.

关键词： Humans Biometrics Biological system modeling Legged locomotion Fingerprint recognition Character recognition pattern recognition Frequency measurement Phase measurement Intelligent systems

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Generic recognition through qualitative reasoning about 3-D shape and object function

Generic recognition through qualitative reasoning about 3-D ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： L. Stark K. Bowyer Department of Computer Science and Engineering University of South Florida Tampa FL USA

The work which demonstrates the feasibility of a different approach to 3-D object recognition is described. The authors construct a definition of a generic object category, such as a chair, in terms of the function required of the object. This definition is based on qualitative reasoning about 3-D shape, and does not imply any particular geometric or structural model for an object. Thus, this approach has the potential to lead to recognition systems of much greater generality than current CAD-based or model-based approaches.< >

关键词： Shape Solid modeling Object recognition Geometry computer science Knowledge engineering computer vision

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Robust face detection with multi-class boosting

Robust face detection with multi-class boosting

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yen-Yu Lin Tyng-Luh Liu Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (纸本)0769523722

With the aim to design a general learning framework for detecting faces of various poses or under different lighting conditions, we are motivated to formulate the task as a classification problem over data of multiple classes. Specifically, our approach focuses on a new multi-class boosting algorithm, called MBHboost, and its integration with a cascade structure for effectively performing face detection. There are three main advantages of using MBHboost: 1) each MBH weak learner is derived by sharing a good projection direction such that each class of data has its own decision boundary; 2) the proposed boosting algorithm is established based on an optimal criterion for multi-class classification; and 3) since MBHboost is flexible with respect to the number of classes, it turns out that it is possible to use only one single boosted cascade for the multi-class detection. All these properties give rise to a robust system to detect faces efficiently and accurately.

关键词： Robustness Face detection Boosting Detectors Neural networks Information science Multi-layer neural network Multilayer perceptrons Testing computer vision

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Optimal sub-shape models by minimum description length

Optimal sub-shape models by minimum description length

引用

conference on computer vision and pattern recognition (cvpr)

作者： G. Langs P. Peloschek H. Bischof Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Department of Clinical Radiology Vienna Medical University Vienna Austria

Active shape models are powerful and widely used tool to interpret complex image data. By building models of shape variation they enable search algorithms to use a priori knowledge in an efficient and gainful way. However, due to the linearity of PCA, non-linearities like rotations or independently moving sub-parts in the data can deteriorate the resulting model considerably. Although non-linear extensions of active shape models have been proposed and application specific solutions have been used, they still need a certain amount of user interaction during model building. In this paper the task of building/choosing optimal models is tackled in a more generic information theoretic fashion. In particular, we propose an algorithm based on the minimum description length principle to find an optimal subdivision of the data into sub-parts, each adequate for linear modeling. This results in an overall more compact model configuration. Which in turn leads to a better model in terms of modes of variations. The proposed method is evaluated on synthetic data, medical images and hand contours.

关键词： Active shape model Biomedical imaging Principal component analysis computer vision Linearity Polynomials pattern recognition computer graphics Radiology Image processing

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Hierarchical region based stereo matching

Hierarchical region based stereo matching

引用

conference on computer vision and pattern recognition (cvpr)

作者： L. Cohen L. Vinet P.T. Sander A. Gagalowicz Domaine de Voluceau-Rocquencourt Le Chesnay France Domaine de Voluceau-Rocquencourt INRIA Le Chesnay France

Stereo matching is the process of determining correspondences between entities in related images. Often, this is treated as two quite independent subprocesses, namely, segmentation, followed by matching. These processes are treated as naturally related, in that partial matching is fed back to the segmentation and both proceed simultaneously in a cooperative fashion. Regions are considered as the primitives to be matched, since many of the shortcomings inherent in approaches based on points or lines can be overcome by taking more developed entities. The implementation is based on maintaining a hierarchy of segmented regions in each image, corresponding to analysis at differing scales. The selection of a particular segmentation in each image at a scale appropriate to each region is validated with reference to the optimal matching region in the other image. Examples of these methods, applied to a synthetic image (incorporating color) and to natural office scenes, are presented.< >

关键词： Image segmentation Image edge detection Machine vision Biological systems Shape Stereo vision Orbital robotics Visual perception Detectors Feeds

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Single Camera Stereo for Mobile Robot Surveillance

Single Camera Stereo for Mobile Robot Surveillance

引用

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

作者： J. McBride M. Snorrason T. Goodsell R. Eaton M.R. Stevens Charles River Analytics 슠Inc. Cambridge MA USA

Mobile robot designers frequently look to computer vision to solve navigation, obstacle avoidance, and object detection problems such as those encountered in parking lot surveillance. Stereo reconstruction is a useful technique in this domain. The advantage of a single-camera stereo method versus a stereo rig is the flexibility to change the baseline distance to best match each scenario. This directly increases the robustness of the stereo algorithm and increases the effective range of the system. The challenge comes from accurately rectifying the images into an ideal stereo pair. Structure from motion (SFM) can be used to compute the camera motion between the two images, but its accuracy is limited and small errors can cause rectified images to be misaligned. We present a single-camera stereo system that incorporates a Levenberg-Marquardt minimization of rectification parameters to bring the rectified images into alignment.

关键词： Robot vision systems Cameras Mobile robots Surveillance Stereo vision computer vision Navigation Object detection Stereo image processing Image reconstruction

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