LEADER 04206nam 2200721 a 450 001 9910456890503321 005 20200520144314.0 010 $a1-283-25865-X 010 $a9786613258656 010 $a0-262-29835-X 024 8 $a3634472 035 $a(CKB)2550000000047426 035 $a(EBL)3339283 035 $a(SSID)ssj0000538930 035 $a(PQKBManifestationID)12232587 035 $a(PQKBTitleCode)TC0000538930 035 $a(PQKBWorkID)10568490 035 $a(PQKB)10854892 035 $a(MiAaPQ)EBC3339283 035 $a(OCoLC)750172643$z(OCoLC)754329750$z(OCoLC)816862196$z(OCoLC)961498466$z(OCoLC)962671846$z(OCoLC)988450475$z(OCoLC)991917808$z(OCoLC)1037934790$z(OCoLC)1038677992$z(OCoLC)1053900482$z(OCoLC)1055382967$z(OCoLC)1062871103$z(OCoLC)1081227245 035 $a(OCoLC-P)750172643 035 $a(MaCbMITP)8579 035 $a(Au-PeEL)EBL3339283 035 $a(CaPaEBR)ebr10496272 035 $a(CaONFJC)MIL325865 035 $a(OCoLC)750172643 035 $a(EXLCZ)992550000000047426 100 $a20101203d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aMarkov random fields for vision and image processing$b[electronic resource] /$fedited by Andrew Blake, Pushmeet Kohli, and Carsten Rother 210 $aCambridge, Mass. $cMIT Press$dc2011 215 $a1 online resource (472 p.) 300 $aDescription based upon print version of record. 311 $a0-262-01577-3 320 $aIncludes bibliographical references and index. 327 $aCover ; Contents; 1 Introduction to Markov Random Fields; I Algorithms for Inference of MAP Estimates for MRFs; 2 Basic Graph Cut Algorithms; 3 Optimizing Multilabel MRFs Using Move-Making Algorithms; 4 Optimizing Multilabel MRFs with Convex and Truncated Convex Priors; 5 Loopy Belief Propagation, Mean Field Theory, and Bethe Approximations; 6 Linear Programming and Variants of Belief Propagation; II Applications of MRFs, including Segmentation; 7 Interactive Foreground Extraction; 8 Continuous-Valued MRF for Image Segmentation; 9 Bilayer Segmentation of Video 327 $a10 MRFs for Superresolution and Texture Synthesis11 A Comparative Study of Energy Minimization Methods for MRFs; III Further Topics: Inference, Parameter Learning, and Continuous Models; 12 Convex Relaxation Techniques for Segmentation, Stereo, and Multiview Reconstruction; 13 Learning Parameters in Continuous-Valued Markov Random Fields; 14 Message Passing with Continuous Latent Variables; 15 Learning Large-Margin Random Fields Using Graph Cuts; 16 Analyzing Convex Relaxations for MAP Estimation; 17 MAP Inference by Fast Primal-Dual Linear Programming 327 $a18 Fusion-Move Optimization for MRFs with an Extensive Label SpaceIV Higher-Order MRFs and Global Constraints; 19 Field of Experts; 20 Enforcing Label Consistency Using Higher-Order Potentials; 21 Exact Optimization for Markov Random Fields with Nonlocal Parameters; 22 Graph Cut-Based Image Segmentation with Connectivity Priors; V Advanced Applications of MRFs; 23 Symmetric Stereo Matching for Occlusion Handling; 24 Steerable Random Fields for Image Restoration; 25 Markov Random Fields for Object Detection; 26 SIFT Flow; 27 Unwrap Mosaics; Bibliography; Contributors; Index 330 $aState-of-the-art research on MRFs, successful MRF applications, and advanced topics for future study. 606 $aImage processing$xMathematics 606 $aComputer graphics$xMathematics 606 $aComputer vision$xMathematics 606 $aMarkov random fields 608 $aElectronic books. 615 0$aImage processing$xMathematics. 615 0$aComputer graphics$xMathematics. 615 0$aComputer vision$xMathematics. 615 0$aMarkov random fields. 676 $a006.3/70151 701 $aBlake$b Andrew$f1956-$01049049 701 $aKohli$b Pushmeet$01049050 701 $aRother$b Carsten$01049051 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910456890503321 996 $aMarkov random fields for vision and image processing$92477739 997 $aUNINA