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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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Physically-based edge labeling

Physically-based edge labeling

引用

conference on computer vision and pattern recognition (cvpr)

作者： T.E. Boult L.B. Wolff Department of Computer Science Columbia University New York NY USA Department of Computer Science Johns Hopkins University Baltimore MD USA

The authors present a physically based approach, using polarization, to distinguish three types of image edges; limb edges, specular edges, and albedo/physical edges. Assuming general imaging conditions and smooth dielectric surfaces, a labeling scheme which enables one to distinguish among these edge types has been developed. The method is demonstrated on laboratory images.< >

关键词： Labeling Image edge detection Polarization Dielectrics Optical reflection Light sources Reflectivity computer science computer vision Application software

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Solving Markov Random Fields using Second Order Cone Programming Relaxations

Solving Markov Random Fields using Second Order Cone Program...

引用

conference on computer vision and pattern recognition (cvpr)

作者： M.P. Kumar P.H.S. Torr A. Zisserman Oxford Brookes University UK Dept. of Computing Oxford Brookes University UK Dept. of Engineering Science University of Oxford UK

This paper presents a generic method for solvingMarkov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP). Though in general solving MRFs is NP-hard, we propose a second order cone programming relaxation scheme which solves a closely related (convex) approximation. In terms of computational efficiency, our method significantly outperforms the semidefinite relaxations previously used whilst providing equally (or even more) accurate results. Unlike popular inference schemes such as Belief Propagation and Graph Cuts, convergence is guaranteed within a small number of iterations. Furthermore, we also present a method for greatly reducing the runtime and increasing the accuracy of our approach for a large and useful class of MRFs. We compare our approach with the state-of-the-art methods for subgraph matching and object recognition and demonstrate significant improvements.

关键词： Markov random fields Quadratic programming Belief propagation Inference algorithms Convergence computer vision Tree graphs Computational efficiency Runtime Object recognition

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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 View Reconstruction of Curved Surfaces

Single View Reconstruction of Curved Surfaces

引用

conference on computer vision and pattern recognition (cvpr)

作者： M. Prasad A. Fitzgibbon University of Oxford U.K.

Recent advances in single-view reconstruction (SVR) have been in modelling power (curved 2.5D surfaces) and automation (automatic photo pop-up). We extend SVR along both of these directions. We increase modelling power in several ways: (i) We represent general 3D surfaces, rather than 2.5D Monge patches; (ii) We describe a closed-form method to reconstruct a smooth surface from its image apparent contour, including multilocal singularities ("kidney-bean" self-occlusions); (iii) We show how to incorporate user-specified data such as surface normals, interpolation and approximation constraints; (iv) We show how this algorithm can be adapted to deal with surfaces of arbitrary genus. We also show how the modelling process can be automated for simple object shapes and views, using a-priori object class information. We demonstrate these advances on natural images drawn from a number of object classes.

关键词： Surface reconstruction Image reconstruction Layout Automation Shape Image segmentation computer vision Surface fitting Painting Topology

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On the distribution of saliency

On the distribution of saliency

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Berengolts M. Lindenbaum Computer Science Department Technion-Israel Institute of Technology Haifa Israel

The calculation of salient structures is one of the early and basic ideas of perceptual organization in computer vision. Saliency algorithms typically mark edge-points with some saliency measure, growing with the length and the smoothness of the curve on which this edge-point lies. We consider a generalization [M. Lindenbaum and A. Berengolts, 2000] of the Ullman-Shaashua saliency measure (1998) and aim to analyze the saliency measure in a probabilistic context: regarding the basic grouping information (grouping cues) as random variables, we use ergodicity and asymptotic analysis to derive the saliency distribution associated with the main curves ("figure") and with the rest of the image ("background"). We further consider finite-length curves and analyze their saliency values. We observed several discrepancies between the observed distributions and the predictions we supply, discuss their sources and propose a way to account for them. Then, based on the derived distributions we show how to set threshold on the saliency for deciding optimally between figure and background, how to choose cues which are usable for saliency, and how to estimate bounds on the saliency performance.

关键词： Voting computer science computer vision Length measurement Image analysis Information analysis Random variables Humans Tensile stress Clustering algorithms

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Gauging relational consistency and correcting structural errors

Gauging relational consistency and correcting structural err...

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 aim of this paper is to provide a comparative evaluation of a number of contrasting approaches to relational matching. Unique to this study is the way in which we show how a diverse family of algorithms relate to one-another using a common Bayesian framework. Broadly speaking there are two main aspects to this study. Firstly we focus on the issue of how relational inexactness may be quantified. We illustrate that several popular relational distance measures can be recovered as specific limiting cases of the same Bayesian consistency measure. The second aspect of our comparison concerns the way in which structural inexactness is controlled. We investigate three different realisations of the matching process which draw on contrasting control models. The main conclusion of our study is that the active process of graph-editing outperforms the alternatives in terms of its ability to effectively control a large population of contaminating clutter.

关键词： Error correction Bayesian methods Pollution measurement Filtering computer errors computer science Measurement uncertainty Image segmentation Associative memory Turning

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Object detection using contrast based scale-space

Object detection using contrast based scale-space

引用

conference on computer vision and pattern recognition (cvpr)

作者： V.A. Topkar A.K. Sood B. Kjell Department of Computer Science George Mason University Fairfax VA USA

The authors propose four scale-space object detection algorithms for separating objects from the background. These algorithms do not need thresholding at any of the scales. The different algorithms are applicable to images with different noise and clutter characteristics. Statistical analysis of the four algorithms is conducted for noisy and cluttered backgrounds.< >

关键词： Object detection Background noise Additive noise Colored noise Contracts computer science Statistical analysis Laplace equations Filters Pulse generation

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A Framework for Feature Selection for Background Subtraction

A Framework for Feature Selection for Background Subtraction

引用

conference on computer vision and pattern recognition (cvpr)

作者： T. Parag A. Elgammal A. Mittal Department of Computer Science Rutgers University Piscataway NJ USA Real-time Vision & Modeling Department Siemens AG Corporate Research and Development Princeton NJ USA

Background subtraction is a widely used paradigm to detect moving objects in video taken from a static camera and is used for various important applications such as video surveillance, human motion analysis, etc. Various statistical approaches have been proposed for modeling a given scene background. However, there is no theoretical framework for choosing which features to use to model different regions of the scene background. In this paper we introduce a novel framework for feature selection for background modeling and subtraction. A boosting algorithm, namely RealBoost, is used to choose the best combination of features at each pixel. Given the probability estimates from a pool of features calculated by Kernel Density Estimate (KDE) over a certain time period, the algorithm selects the most useful ones to discriminate foreground objects from the scene background. The results show that the proposed framework successfully selects appropriate features for different parts of the image.

关键词： Layout Object detection Motion detection Cameras Video surveillance Humans Motion analysis Boosting Probability Kernel

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Radiometric alignment of image sequences

Radiometric alignment of image sequences

引用

conference on computer vision and pattern recognition (cvpr)

作者： Seon Joo Kim M. Pollefeys Department of Computer Science North Carolina State University Chapel Hill NC USA

Color values in an image are related to image irradiance by a nonlinear function called radiometric response function. Since this function depends on the aperture and the shutter speed, image intensity of a same object may vary during the acquisition of an image sequence due to auto exposure feature of the camera. While this is desirable to make optimal use of the limited dynamic range of most cameras, this causes problems for a number of applications in computer vision. In this paper we propose a method for estimating the radiometric response function and apply it to radiometrically align images so that the color values are consistent for all images of a sequence. Our approach computes the response function, exposure and white balance changes between images (up to some ambiguity) for a moving camera without any prior knowledge about exposures. We show the performance of our algorithm by estimating the response function from synthetic images and also from real world data, using it to radiometrically align the images.

关键词： Radiometry Image sequences Layout Dynamic range computer vision Brightness Shape measurement Application software Digital cameras Iterative algorithms

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