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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"

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A minimum-variance adaptive surface mesh

A minimum-variance adaptive surface mesh

引用

conference on computer vision and pattern recognition (cvpr)

作者： R.C. Wilson E.R. Hancock Department of Computer Science University of York York UK

The main contribution of the paper is to describe a new class of the adaptive mesh. The mesh uses both split and merge operations to adapt itself to the structure of volumetric data-points. The adaptive behaviour is controlled by the variance of the data-point positions about maximum-likelihood quadric patches. The authors show that the density of control points on the mesh is regulated by the curvature of the underlying surface. Finally, they illustrate the effectiveness of the method on both real-world and simulated data-sets.

关键词： Surface fitting Topology Surface reconstruction Spline computer science Programmable control Adaptive control Finite element methods Lagrangian functions Equations

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On the representation of occluded shapes

On the representation of occluded shapes

引用

conference on computer vision and pattern recognition (cvpr)

作者： T. Darrel A. Pentland Media Lab. MIT Cambridge MA USA Vision and Modeling Group Massachusetts Institute of Technology

Most approaches to simultaneously recovering both model parameters and segmentation have relied on an edge field to represent segmentation. This restriction, and the implementations it leads to, have fundamental limitations in representing occluded stimuli. The authors develop a framework that overcomes these limitations by using multiple layers of explicit support to represent segmentation. Results from an initial implementation demonstrate that this method can segment images containing occluded objects.< >

关键词： Shape Switches Image segmentation Parameter estimation Smoothing methods Finite difference methods Layout Capacitive sensors Very large scale integration Voltage control

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Temporal Segmentation and Activity Classification from First-person Sensing

Temporal Segmentation and Activity Classification from First...

引用

ieee computer society conference on computer vision and pattern recognition Workshops

作者： Ekaterina H. Spriggs Fernando De La Torre Martial Hebert Carnegie Mellon University

ISBN: (纸本)9781424439942

Temporal segmentation of human motion into actions is central to the understanding and building of computational models of human motion and activity recognition. Several issues contribute to the challenge of temporal segmentation and classification of human motion. These include the large variability in the temporal scale and periodicity of human actions, the complexity of representing articulated motion, and the exponential nature of all possible movement combinations. We provide initial results from investigating two distinct problems - classification of the overall task being performed, and the more difficult problem of classifying individual frames over time into specific actions. We explore first-person sensing through a wearable camera and Inertial Measurement Units (IMUs) for temporally segmenting human motion into actions and performing activity classification in the context of cooking and recipe preparation in a natural environment. We present baseline results for supervised and unsupervised temporal segmentation, and recipe recognition in the CMU-Multimodal activity database (CMU-MMAC).

关键词： Humans Cameras Databases computer vision Hidden Markov models computer graphics Motion analysis Computational modeling Measurement units Motion detection

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Recovering shape and reflectance model of non-lambertian objects from multiple views

Recovering shape and reflectance model of non-lambertian obj...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Tianli Yu Ning Xu N. Ahuja Electrical and Computer Engineering Department University of Illinois Urbana-Champaign USA

This paper proposes an algorithm to simultaneously estimate both the 3D shape and parameters of a surface reflectance model from multiple views of an object made of a single material. The algorithm is based on a multiple view shape from shading method. A triangular mesh represents the shape of the object. The Phong reflectance model is used to model the surface reflectance. We iteratively find the shape and reflectance parameters that best fit all input images. Subdividing triangles in the mesh into smaller ones gradually refines the estimates of shape and reflectance model. The estimation takes into account both self-occlusion and self-shadowing. Analysis shows that the accuracy of reflectance estimation is limited by the triangle size in the shape model. We also propose to use Richardson extrapolation to overcome this and further refine the reflectance model estimate. The estimated 3D shape and reflectance model can be used to render the same object from different viewing directions and under different lighting conditions. Experimental results on both synthetic and real objects are given.

关键词： Shape Reflectivity Image reconstruction Photometry Stereo vision Rendering (computer graphics) Layout Surface reconstruction computer graphics computer vision

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Shape-based recognition of wiry objects

Shape-based recognition of wiry objects

引用

conference on computer vision and pattern recognition (cvpr)

作者： O. Carmichael M. Hebert The Robotics Institute Carnegie Mellon University Pittsburgh PA USA

We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge cues. We first use example images of the desired object in typical backgrounds to train a classifier cascade which determines whether edge pixels in an image belong to an instance of the object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels. The features used for this classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of complex objects in cluttered indoor scenes under arbitrary out-of-image-plane rotation.

关键词： Image edge detection Pixel Shape Face detection Filtering Layout Lamps Object detection Image recognition pattern recognition

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Accurate Tracking of Monotonically Advancing Fronts

Accurate Tracking of Monotonically Advancing Fronts

引用

conference on computer vision and pattern recognition (cvpr)

作者： M.S. Hassouna A.A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY USA

A wide range of computer vision applications such as distance field computation, shape from shading, and shape representation require an accurate solution of a particular Hamilton-Jacobi (HJ) equation, known as the Eikonal equation. Although the fast marching method (FMM) is the most stable and consistent method among existing techniques for solving such equation, it suffers from large numerical error along diagonal directions as well as its computational complexity is not optimal. In this paper, we propose an improved version of the FMMthat is both highly accurate and computationally efficient for Cartesian domains. The new method is called the multi-stencils fast marching (MSFM), which computes the solution at each grid point by solving the Eikonal equation along several stencils and then picks the solution that satisfies the fast marching causality relationship. The stencils are centered at each grid point x and cover its entire nearest neighbors. In 2D space, 2 stencils cover the 8-neighbors of x, while in 3D space, 6 stencils cover its 26-neighbors. For those stencils that are not aligned with the natural coordinate system, the Eikonal equation is derived using directional derivatives and then solved using a higher order finite difference scheme.

关键词： computer vision Shape Finite difference methods Application software Computational complexity Nonlinear equations Partial differential equations Image processing Laboratories Grid computing

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Bayesian multi-camera surveillance

Bayesian multi-camera surveillance

引用

conference on computer vision and pattern recognition (cvpr)

作者： V. Kettnaker R. Zabih Computer Science Department Cornell University USA

The task of multicamera surveillance is to reconstruct the paths taken by all moving objects that are temporally visible from multiple non-overlapping cameras. We present a Bayesian formalization of this task, where the optimal solution is the set of object paths with the highest posterior probability given the observed data. We show how to efficiently approximate the maximum a posteriori solution by linear programming and present initial experimental results.

关键词： Bayesian methods Surveillance Monitoring Cameras computer science Road transportation Topology Streaming media Traffic control Statistics

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Local blur estimation and super-resolution

Local blur estimation and super-resolution

引用

conference on computer vision and pattern recognition (cvpr)

作者： Ming-Chao Chiang T.E. Boult Department of Computer Science Columbia University New York NY USA Department of EECS Lehigh University Bethlehem PA USA

Until now, all super-resolution algorithms have presumed that the images were taken under the same illumination conditions. This paper introduces a new approach to super-resolution, based on edge models and a local blur estimate, which circumvents these difficulties. The paper presents the theory and the experimental results using the new approach.

关键词： Image resolution Image edge detection Image sequences Image restoration computer science High-resolution imaging Frequency Sensor phenomena and characterization US Department of Defense Lighting control

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Large-scale Learning with SVM and Convolutional for Generic Object Categorization

Large-scale Learning with SVM and Convolutional for Generic ...

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conference on computer vision and pattern recognition (cvpr)

作者： Fu Jie Huang Y. LeCun Courant Institute of Mathematical Sciences New York University New York NY USA

The detection and recognition of generic object categories with invariance to viewpoint, illumination, and clutter requires the combination of a feature extractor and a classifier. We show that architectures such as convolutional networks are good at learning invariant features, but not always optimal for classification, while Support Vector Machines are good at producing decision surfaces from wellbehaved feature vectors, but cannot learn complicated invariances. We present a hybrid system where a convolutional network is trained to detect and recognize generic objects, and a Gaussian-kernel SVM is trained from the features learned by the convolutional network. Results are given on a large generic object recognition task with six categories (human figures, four-legged animals, airplanes, trucks, cars, and "none of the above"), with multiple instances of each object category under various poses, illuminations, and backgrounds. On the test set, which contains different object instances than the training set, an SVM alone yields a 43.3% error rate, a convolutional net alone yields 7.2% and an SVM on top of features produced by the convolutional net yields 5.9%.

关键词： Large-scale systems Support vector machines Support vector machine classification Object detection Lighting Feature extraction Machine learning Gaussian processes Object recognition Humans

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Feature selection for reliable tracking using template matching

Feature selection for reliable tracking using template match...

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conference on computer vision and pattern recognition (cvpr)

作者： T. Kaneko O. Hori Multimedia Laboratory Corporate R&D Center Toshiba Corporation Kawasaki Japan

A new feature selection method for reliable tracking is presented. In this paper, it is assumed that features are tracked by template matching where small regions around the features are defined as templates. The proposed method selects features based on the upper bound of the average template matching error. This selection criterion is directly related to the reliability of tracking and hence, the performance is better than that of other feature detectors. Experimental results are presented to confirm the efficiency of the proposed method.

关键词： computer vision Upper bound Detectors Laboratories Research and development computer errors Image sequences Application software Humans Autocorrelation

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