LEADER 03960oam 2200553 450 001 9910143649303321 005 20210804200200.0 010 $a3-540-46805-6 024 7 $a10.1007/3-540-46805-6 035 $a(CKB)1000000000211130 035 $a(SSID)ssj0000326643 035 $a(PQKBManifestationID)11912764 035 $a(PQKBTitleCode)TC0000326643 035 $a(PQKBWorkID)10297531 035 $a(PQKB)10672008 035 $a(DE-He213)978-3-540-46805-9 035 $a(MiAaPQ)EBC3073057 035 $a(MiAaPQ)EBC6494941 035 $a(PPN)155192353 035 $a(EXLCZ)991000000000211130 100 $a20210804d1999 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 00$aShape, contour, and grouping in computer vision /$fDavid A. Forsyth [and three others] (editors) 205 $a1st ed. 1999. 210 1$aBerlin ;$aHeidelberg :$cSpringer,$d[1999] 210 4$d©1999 215 $a1 online resource (VIII, 350 p.) 225 1 $aLecture Notes in Computer Science,$x0302-9743 ;$v1681 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-66722-9 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aAn Empirical-Statistical Agenda for Recognition -- A Formal-Physical Agenda for Recognition -- Shape -- Shape Models and Object Recognition -- Order Structure, Correspondence, and Shape Based Categories -- Quasi-Invariant Parameterisations and Their Applications in Computer Vision -- Shading -- Representations for Recognition Under Variable Illumination -- Shadows, Shading, and Projective Ambiguity -- Grouping -- Grouping in the Normalized Cut Framework -- Geometric Grouping of Repeated Elements within Images -- Constrained Symmetry for Change Detection -- Grouping Based on Coupled Diffusion Maps -- Representation and Recognition -- Integrating Geometric and Photometric Information for Image Retrieval -- Towards the Integration of Geometric and Appearance-Based Object Recognition -- Recognizing Objects Using Color-Annotated Adjacency Graphs -- A Cooperating Strategy for Objects Recognition -- Statistics, Learning and Recognition -- Model Selection for Two View Geometry:A Review -- Finding Objects by Grouping Primitives -- Object Recognition with Gradient-Based Learning. 330 $aComputer vision has been successful in several important applications recently. Vision techniques can now be used to build very good models of buildings from pictures quickly and easily, to overlay operation planning data on a neuros- geon?s view of a patient, and to recognise some of the gestures a user makes to a computer. Object recognition remains a very di cult problem, however. The key questions to understand in recognition seem to be: (1) how objects should be represented and (2) how to manage the line of reasoning that stretches from image data to object identity. An important part of the process of recognition { perhaps, almost all of it { involves assembling bits of image information into helpful groups. There is a wide variety of possible criteria by which these groups could be established { a set of edge points that has a symmetry could be one useful group; others might be a collection of pixels shaded in a particular way, or a set of pixels with coherent colour or texture. Discussing this process of grouping requires a detailed understanding of the relationship between what is seen in the image and what is actually out there in the world. 410 0$aLecture Notes in Computer Science,$x0302-9743 ;$v1681 606 $aComputer vision 615 0$aComputer vision. 676 $a006.37 702 $aForsyth$b David 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910143649303321 996 $aShape, contour and grouping in computer vision$91492559 997 $aUNINA