LEADER 06488nam 22007575 450 001 996465545403316 005 20200704231333.0 010 $a3-540-69042-5 024 7 $a10.1007/3-540-62909-2 035 $a(CKB)1000000000234635 035 $a(SSID)ssj0000322905 035 $a(PQKBManifestationID)11244396 035 $a(PQKBTitleCode)TC0000322905 035 $a(PQKBWorkID)10296331 035 $a(PQKB)11439516 035 $a(DE-He213)978-3-540-69042-9 035 $a(PPN)155207016 035 $a(EXLCZ)991000000000234635 100 $a20121227d1997 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aEnergy Minimization Methods in Computer Vision and Pattern Recognition$b[electronic resource] $eInternational Workshop EMMCVPR'97, Venice, Italy, May 21-23, 1997, Proceedings /$fedited by Marcello Pelillo, Edwin R. Hancock 205 $a1st ed. 1997. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d1997. 215 $a1 online resource (XII, 556 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v1223 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-62909-2 327 $aReliable computation and related games -- Characterizing the distribution of completion shapes with corners using a mixture of random processes -- Adaptive parametrically deformable contours -- Kona: A multi-junction detector using minimum description length principle -- Restoration of SAR images using recovery of discontinuities and non-linear optimization -- Geometrically deformable templates for shape-based segmentation and tracking in cardiac MR images -- Image segmentation via energy minimization on partitions with connected components -- Restoration of severely blurred high range images using stochastic and deterministic relaxation algorithms in compound gauss Markov random fields -- Maximum likelihood estimation of Markov Random Field parameters using Markov Chain Monte Carlo algorithms -- Noniterative manipulation of discrete energy-based models for image analysis -- Unsupervised image segmentation using Markov Random Field models -- Adaptive anisotropic parameter estimation in the weak membrane model -- Twenty questions, focus of attention, and A*: A theoretical comparison of optimization strategies -- Deterministic annealing for unsupervised texture segmentation -- Self annealing: Unifying deterministic annealing and relaxation labeling -- Multidimensional scaling by deterministic annealing -- Deterministic search strategies for relational graph matching -- Object localization using color, texture and shape -- Visual deconstruction: Recognizing articulated objects -- Optimization problems in statistical object recognition -- Object recognition using stochastic optimization -- Genetic algorithms for ambiguous labelling problems -- Toward global solution to MAP image estimation: Using Common structure of local solutions -- Figure-ground separation: A case study in energy minimization via evolutionary computing -- Probabilistic relaxation: Potential, relationships and open problems -- A region-level motion-based graph representation and labeling for tracking a spatial image partition -- An expectation-maximisation approach to graph matching -- An energy minimization method for matching and comparing structured object representations -- Consistent modeling of terrain and drainage using deformable models -- Integration of confidence information by Markov Random Fields for reconstruction of underwater 3D acoustic images -- Unsupervised segmentation applied on sonar images -- SAR image registration and segmentation using an estimated DEM -- Deformable templates for tracking and analysis of intravascular ultrasound sequences -- Motion correspondence through energy minimization. 330 $aThis book constitutes the refereed proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR'97, held in Venice, Italy, in May 1997. The book presents 29 revised full papers selected from a total of 62 submissions. Also included are four full invited papers and a keynote paper by leading researchers. The volume is organized in sections on contours and deformable models, Markov random fields, deterministic methods, object recognition, evolutionary search, structural models, and applications. The volume is the first comprehensive documentation of the application of energy minimization techniques in the areas of compiler vision and pattern recognition. 410 0$aLecture Notes in Computer Science,$x0302-9743 ;$v1223 606 $aArtificial intelligence 606 $aPattern recognition 606 $aOptical data processing 606 $aComputer graphics 606 $aAlgorithms 606 $aComputers 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aComputer Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22013 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 615 0$aArtificial intelligence. 615 0$aPattern recognition. 615 0$aOptical data processing. 615 0$aComputer graphics. 615 0$aAlgorithms. 615 0$aComputers. 615 14$aArtificial Intelligence. 615 24$aPattern Recognition. 615 24$aImage Processing and Computer Vision. 615 24$aComputer Graphics. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aComputation by Abstract Devices. 676 $a006.4/2 702 $aPelillo$b Marcello$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHancock$b Edwin R$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aEMMCVPR'97 906 $aBOOK 912 $a996465545403316 996 $aEnergy Minimization Methods in Computer Vision and Pattern Recognition$9772370 997 $aUNISA