LEADER 04558nam 22007095 450 001 9910254206203321 005 20200702004535.0 010 $a3-319-23048-4 024 7 $a10.1007/978-3-319-23048-1 035 $a(CKB)3710000000515863 035 $a(EBL)4101849 035 $a(SSID)ssj0001584588 035 $a(PQKBManifestationID)16263107 035 $a(PQKBTitleCode)TC0001584588 035 $a(PQKBWorkID)14864360 035 $a(PQKB)11245588 035 $a(DE-He213)978-3-319-23048-1 035 $a(MiAaPQ)EBC4101849 035 $a(PPN)190520582 035 $a(EXLCZ)993710000000515863 100 $a20151121d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDense Image Correspondences for Computer Vision /$fedited by Tal Hassner, Ce Liu 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (302 p.) 300 $aDescription based upon print version of record. 311 $a3-319-23047-6 320 $aIncludes bibliographical references. 327 $aIntroduction to Dense Optical Flow -- SIFT Flow: Dense Correspondence across Scenes and its Applications -- Dense, Scale-Less Descriptors -- Scale-Space SIFT Flow -- Dense Segmentation-aware Descriptors -- SIFTpack: A Compact Representation for Efficient SIFT Matching -- In Defense of Gradient-Based Alignment on Densely Sampled Sparse Features -- From Images to Depths and Back -- DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling -- Joint Inference in Image Datasets via Dense Correspondence -- Dense Correspondences and Ancient Texts. 330 $aThis book describes the fundamental building-block of many new computer vision systems: dense and robust correspondence estimation. Dense correspondence estimation techniques are now successfully being used to solve a wide range of computer vision problems, very different from the traditional applications such techniques were originally developed to solve. This book introduces the techniques used for establishing correspondences between challenging image pairs, the novel features used to make these techniques robust, and the many problems dense correspondences are now being used to solve. The book provides information to anyone attempting to utilize dense correspondences in order to solve new or existing computer vision problems. The editors describe how to solve many computer vision problems by using dense correspondence estimation. Finally, it surveys resources, code, and data necessary for expediting the development of effective correspondence-based computer vision systems.   ·         Provides in-depth coverage of dense-correspondence estimation ·         Covers both the breadth and depth of new achievements in dense correspondence estimation and their applications ·         Includes information for designing computer vision systems that rely on efficient and robust correspondence estimation  . 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aOptical data processing 606 $aArtificial intelligence 606 $aElectrical engineering 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aOptical data processing. 615 0$aArtificial intelligence. 615 0$aElectrical engineering. 615 14$aSignal, Image and Speech Processing. 615 24$aImage Processing and Computer Vision. 615 24$aArtificial Intelligence. 615 24$aCommunications Engineering, Networks. 676 $a620 702 $aHassner$b Tal$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLiu$b Ce$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910254206203321 996 $aDense Image Correspondences for Computer Vision$91756529 997 $aUNINA