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020 _a9783540447450
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024 7 _a10.1007/3-540-44745-8
_2doi
050 4 _aQ337.5
050 4 _aTK7882.P3
072 7 _aUYQP
_2bicssc
072 7 _aCOM016000
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082 0 4 _a006.4
_223
245 1 0 _aEnergy Minimization Methods in Computer Vision and Pattern Recognition
_h[electronic resource] :
_bThird International Workshop, EMMCVPR 2001 Sophia Antipolis, France, September 3–5, 2001 Proceedings /
_cedited by Mário Figueiredo, Josiane Zerubia, Anil K. Jain.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2001.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2001.
300 _aX, 652 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v2134
505 0 _aProbabilistic Models and Estimation -- A Double-Loop Algorithm to Minimize the Bethe Free Energy -- A Variational Approach to Maximum a Posteriori Estimation for Image Denoising -- Maximum Likelihood Estimation of the Template of a Rigid Moving Object -- Metric Similarities Learning through Examples: An Application to Shape Retrieval -- A Fast MAP Algorithm for 3D Ultrasound -- Designing the Minimal Structure of Hidden Markov Model by Bisimulation -- Relaxing Symmetric Multiple Windows Stereo Using Markov Random Fields -- Matching Images to Models — Camera Calibration for 3-D Surface Reconstruction -- A Hierarchical Markov Random Field Model for Figure-Ground Segregation -- Articulated Object Tracking via a Genetic Algorithm -- Image Modelling and Synthesis -- Learning Matrix Space Image Representations -- Supervised Texture Segmentation by Maximising Conditional Likelihood -- Designing Moiré Patterns -- Optimization of Paintbrush Rendering of Images by Dynamic MCMC Methods -- Illumination Invariant Recognition of Color Texture Using Correlation and Covariance Functions -- Clustering, Grouping, and Segmentation -- Path Based Pairwise Data Clustering with Application to Texture Segmentation -- A Maximum Likelihood Framework for Grouping and Segmentation -- Image Labeling and Grouping by Minimizing Linear Functionals over Cones -- Grouping with Directed Relationships -- Segmentations of Spatio-Temporal Images by Spatio-Temporal Markov Random Field Model -- Highlight and Shading Invariant Color Image Segmentation Using Simulated Annealing -- Edge Based Probabilistic Relaxation for Sub-pixel Contour Extraction -- Two Variational Models for Multispectral Image Classification -- Optimization and Graphs -- An Experimental Comparison of Min-cut/Max-flow Algorithms for Energy Minimization in Vision -- A Discrete/Continuous Minimization Method in Interferometric Image Processing -- Global Energy Minimization: A Transformation Approach -- Global Feedforward Neural Network Learning for Classification and Regression -- Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics -- Efficiently Computing Weighted Tree Edit Distance Using Relaxation Labeling -- Estimation of Distribution Algorithms: A New Evolutionary Computation Approach for Graph Matching Problems -- A Complementary Pivoting Approach to Graph Matching -- Application of Genetic Algorithms to 3-D Shape Reconstruction in an Active Stereo Vision System -- Shapes, Curves, Surfaces, and Templates -- A Markov Process Using Curvature for Filtering Curve Images -- Geodesic Interpolating Splines -- Averaged Template Matching Equations -- A Continuous Shape Descriptor by Orientation Diffusion -- Multiple Contour Finding and Perceptual Grouping as a Set of Energy Minimizing Paths -- Shape Tracking Using Centroid-Based Methods -- Optical Flow and Image Registration: A New Local Rigidity Approach for Global Minimization -- Spherical Object Reconstruction Using Star-Shaped Simplex Meshes -- Gabor Feature Space Diffusion via the Minimal Weighted Area Method -- 3D Flux Maximizing Flows.
520 _aThis volume consists of the 42 papers presented at the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR2001),whichwasheldatINRIA(InstitutNationaldeRechercheen Informatique et en Automatique) in Sophia Antipolis, France, from September 3 through September 5, 2001. This workshop is the third of a series, which was started with EMMCVPR’97, held in Venice in May 1997, and continued with EMMCVR’99, which took place in York, in July 1999. Minimization problems and optimization methods permeate computer vision (CV), pattern recognition (PR), and many other ?elds of machine intelligence. The aim of the EMMCVPR workshops is to bring together people with research interests in this interdisciplinary topic. Although the subject is traditionally well represented at major international conferences on CV and PR, the EMMCVPR workshops provide a forum where researchers can report their recent work and engage in more informal discussions. We received 70 submissions from 23 countries, which were reviewed by the members of the program committee. Based on the reviews, 24 papers were - cepted for oral presentation and 18 for poster presentation. In this volume, no distinction is made between papers that were presented orally or as posters. The book is organized into ?ve sections, whose topics coincide with the ?ve s- sionsoftheworkshop:“ProbabilisticModelsandEstimation”,“ImageModelling and Synthesis”, “Clustering, Grouping, and Segmentation”, “Optimization and Graphs”, and “Shapes, Curves, Surfaces, and Templates”.
650 0 _aComputer science.
650 0 _aComputer software.
650 0 _aArtificial intelligence.
650 0 _aComputer graphics.
650 0 _aComputer vision.
650 0 _aOptical pattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aPattern Recognition.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aComputer Graphics.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aAlgorithm Analysis and Problem Complexity.
650 2 4 _aComputation by Abstract Devices.
700 1 _aFigueiredo, Mário.
_eeditor.
700 1 _aZerubia, Josiane.
_eeditor.
700 1 _aJain, Anil K.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540425236
786 _dSpringer
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v2134
856 4 0 _uhttp://dx.doi.org/10.1007/3-540-44745-8
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