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020 _a9783540468059
_9978-3-540-46805-9
024 7 _a10.1007/3-540-46805-6
_2doi
050 4 _aTA1634
072 7 _aUYQV
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYQV
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082 0 4 _a006.37
_223
245 1 0 _aShape, Contour and Grouping in Computer Vision
_h[electronic resource] /
_cedited by David A. Forsyth, Joseph L. Mundy, Vito di Gesu, Roberto Cipolla.
250 _a1st ed. 1999.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c1999.
300 _aVIII, 350 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,
_x1611-3349 ;
_v1681
505 0 _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.
520 _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.
650 0 _aComputer vision.
650 0 _aPattern recognition systems.
650 0 _aComputer graphics.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Vision.
650 2 4 _aAutomated Pattern Recognition.
650 2 4 _aComputer Graphics.
650 2 4 _aArtificial Intelligence.
700 1 _aForsyth, David A.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMundy, Joseph L.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aGesu, Vito di.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aCipolla, Roberto.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540667223
776 0 8 _iPrinted edition:
_z9783662209899
830 0 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v1681
856 4 0 _uhttps://doi.org/10.1007/3-540-46805-6
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912 _aZDB-2-SXCS
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