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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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Efficient stereo with multiple windowing

Efficient stereo with multiple windowing

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Fusiello V. Roberto E. Trucco Machine Vision Laboratory Department Informatics University of Udine Italy Department of Computing and Electrical Eng Heriot-Watt University UK

We present a new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions. The algorithm is an adaptive, multi-window scheme using left-right consistency to compute disparity and its associated uncertainty. We demonstrate and discuss performances with both synthetic and real stereo pairs, and show how our results improve on those of closely related techniques for both robustness and efficiency.

关键词： Robustness Pixel Uncertainty Layout Machine vision Laboratories Informatics Image reconstruction Computational geometry Cameras

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3D shape from anisotropic diffusion

3D shape from anisotropic diffusion

引用

conference on computer vision and pattern recognition (cvpr)

作者： P. Favaro S. Osher S. Soatto L. Vese Department of Electrical Engineering Washington University Saint Louis MO USA Department of Applied Mathematics UCLA Los Angeles CA USA Department of Computer Science UCLA Los Angeles CA USA

We cast the problem of inferring the 3D shape of a scene from a collection of defocused images in the framework of anisotropic diffusion. We propose an algorithm that can estimate the shape of a scene by inferring the diffusion coefficient of a heat equation. The method is optimal, as we pose it as the minimization of a certain cost functional based on the input images, and fast. Furthermore, we also extend our algorithm to the case of multiple images, and derive a 3D scene segmentation algorithm that can work in the presence of pictorial camouflage.

关键词： Shape computer society computer vision pattern recognition

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Are External Face Features Useful for Automatic Face Classification?

Are External Face Features Useful for Automatic Face Classif...

引用

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

作者： A. Lapedriza D. Masip J. Vitria Computer Vision Center (CVC) & Department Informàtica Universitat Autònoma Barcelona Bellaterra Spain

In this paper a new experiment using the FRGC database is proposed. The experiment deals with the use of external face features for face classification. Unlike the most part of algorithms that can be found in the literature for classifying faces, we consider the external information located at hair and ears as a reliable source of information. These features have often been discarded due to the difficulty of their extraction and alignment, and the lack of robustness in security related applications. Nevertheless, there are a lot of applications where these considerations are not valid, and the proper processing of external features can be an important additional source of information for classifications tasks. We also propose, following this assumption, a method for extracting external information from face images. The method is based on a top-down reconstructionbased algorithm for extracting the external face features. Once extracted, they are encoded in a second step using the Non Negative Matrix Factorization (NMF) algorithm, yielding an aligned high dimensional feature vector. This method has been used in a gender recognition problem, concluding that the encoded information is useful for classification purposes.

关键词： Face detection Data mining Face recognition Hair Information resources computer vision Humans Spatial databases Ear Robustness

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Efficient nearest neighbor classification using a cascade of approximate similarity measures

Efficient nearest neighbor classification using a cascade of...

引用

conference on computer vision and pattern recognition (cvpr)

作者： V. Athitsos J. Alon S. Sclaroff Computer Science Department Boston University Boston MA USA

This paper proposes a method for efficient nearest neighbor classification in non-Euclidean spaces with computationally expensive similarity/distance measures. Efficient approximations of such measures are obtained using the BoostMap algorithm, which produces embeddings into a real vector space. A modification to the BoostMap algorithm is proposed, which uses an optimization cost that is more appropriate when our goal is classification accuracy as opposed to nearest neighbor retrieval accuracy. Using the modified algorithm, multiple approximate nearest neighbor classifiers are obtained, that provide a wide range of trade-offs between accuracy and efficiency. The approximations are automatically combined to form a cascade classifier, which applies the slower and more accurate approximations only to the hardest cases. The proposed method is experimentally evaluated in the domain of handwritten digit recognition using shape context matching. Results on the MNIST database indicate that a speed-up of two to three orders of magnitude is gained over brute force search, with minimal losses in classification accuracy.

关键词： Nearest neighbor searches Shape measurement Image databases Testing Extraterrestrial measurements Cost function Handwriting recognition Error analysis computer science Optical computing

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Rapid Euclidean distance transform using grayscale morphology decomposition

Rapid Euclidean distance transform using grayscale morpholog...

引用

conference on computer vision and pattern recognition (cvpr)

作者： C.T. Huang O.R. Mitchell Department of Electrical Engineering University of Texas Arlington Arlington TX USA

A fast and exact Euclidean distance transformation using grayscale mathematical morphology is presented. The large structuring element required for this operation is decomposed into iterative applications of simple 3*3 windows. This is possible because the square of the Euclidean distance matrix is easily decomposable. Non-square pixels can also be used in this application.< >

关键词： Euclidean distance Gray-scale Morphology Skeleton Image converters Pixel Shape computer architecture Bismuth Hardware

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Deformable models: canonical parameters for surface representation and multiple view integration

Deformable models: canonical parameters for surface represen...

引用

conference on computer vision and pattern recognition (cvpr)

作者： B.C. Vermuri R. Malladi Dept. of Comput. & Inf. Sci. Florida Univ. Gainesville FL USA Department of Computer & Information Sciences University of Florida Gainesville FL USA

Three-dimensional viewpoint invariance is an important requirement in the representation of surfaces for recognition tasks. Parameterized surfaces possess this desirable property. In general, the parameters in a parametric surface representation can be arbitrarily defined. A canonical, intrinsic parameterization provides a consistent, invariant form for describing surfaces. The goal of this work is to define and construct such a parameterization.< >

关键词： Deformable models Surface reconstruction Surface fitting Shape Face detection computer vision Capacitive sensors Nonlinear equations Differential equations

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Perceptual organization using Bayesian networks

Perceptual organization using Bayesian networks

引用

conference on computer vision and pattern recognition (cvpr)

作者： S. Sarkar K.L. Boyer Signal Analysis and Machine Perception Laboratory Department of Electrical Engineering Ohio State Uinversity Columbus OH USA

It is shown that the formalism of Bayesian networks provides an elegant solution, in a probabilistic framework, to the problem of integrating top-down and bottom-up visual processes as well serving as a knowledge base. The formalism is modified to handle spatial data and thus extends the applicability of Bayesian networks to visual processing. The modified form is called the perceptual inference network (PIN). The theoretical background of a PIN is presented, and its viability is demonstrated in the context of perceptual organization. The PIN imparts an active inferential and integrating nature to perceptual organization.< >

关键词： Bayesian methods computer vision Signal analysis Laboratories Context modeling Machine vision NASA Instruments Resource management Image recognition

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Recovering shape and irradiance maps from rich dense texton fields

Recovering shape and irradiance maps from rich dense texton ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Lobay D.A. Forsyth Computer Science Division University of California Berkeley Berkeley CA USA

We describe a method that recovers an estimate of surface shape and of the irradiance field for a textured surface. The method assumes the surface is viewed in scaled orthography, and we demonstrate the appropriateness of this assumption. Our method uses interest points to obtain the locations of putative texton instances, clusters the textons into types, and then uses an autocalibration method to recover the frontal appearance of each texton model. This yields (a) a dense set of normal estimates, each up to a two-fold ambiguity, (b) a dense set of irradiance estimates and (c) whether each instance is, in fact, an instance of the relevant texton. Because we are able to obtain a very large number of instances of a large number of different textons, this information is obtained at sites very closely spaced in the image. As a result, we need only a simple smoothness constraint to reconstruct a surface model, using EM to resolve the normal ambiguity. We show results on images of real scenes, comparing our reconstructions with those obtained using other methods and demonstrating the accuracy of both the recovered shape and the irradiance estimate.

关键词： Shape Surface texture Surface fitting Surface reconstruction Image reconstruction Yield estimation Layout computer vision computer science Production

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Generalized neighborhoods: a new approach to complex parameter feature extraction

Generalized neighborhoods: a new approach to complex paramet...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Califano R.M. Bolle R.W. Taylor Exploratory Computer Vision Group IBM Thomas J. Watson Research Center Yorktown Heights NY USA

A generalized neighborhood concept is presented which extends the usual techniques for feature extraction using parameter transforms. Generalized neighborhoods allow operators to use the joint information contained in distant portions of the same feature; i.e. to utilize the long-distance correlation present in the image. The generalized neighborhood techniques, by correlating local information over different portions of the image, produce up to two orders of magnitude improvement in accuracy over conventional techniques. The response also becomes more complicated; false features may be detected due to a peculiar form of correlated noise. A general framework, motivated by connectionist networks, is presented which eliminates this behaviour by introducing competitive processes in the parameter spaces. A novel approach to the generation of lateral inhibition links in the networks is proposed which is consistent with generalized neighborhoods. Experiments are provided that show results on range data. Complex surfaces and 3-D surface-intersection curves are reconstructed from the data.< >

关键词： Feature extraction Data mining computer vision Image edge detection Shape Surface reconstruction Image reconstruction Parameter estimation Image segmentation Noise measurement

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Real-time tracking of image regions with changes in geometry and illumination

Real-time tracking of image regions with changes in geometry...

引用

conference on computer vision and pattern recognition (cvpr)

作者： G.D. Hager P.N. Belhumeur Department of Computer Science Yale University New Heaven CT USA Department of Electrical Engineering Yale University New Heaven CT USA

Historically, SSD or correlation-based visual tracking algorithms have been sensitive to changes in illumination and shading across the target region. This paper describes methods for implementing SSD tracking that is both insensitive to illumination variations and computationally efficient. We first describe a vector-space formulation of the tracking problem, showing how to recover geometric deformations. We then show that the same vector space formulation can be used to account for changes in illumination. We combine geometry and illumination into an algorithm that tracks large image regions on live video sequences using no more computation than would be required to trade with no accommodation for illumination changes. We present experimental results which compare the performance of SSD tracking with and without illumination compensation.

关键词： Lighting Target tracking Computational geometry Iterative algorithms Computed tomography Optical computing Optical distortion Motion analysis Motion estimation computer science

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