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020 _a9783031022425
_9978-3-031-02242-5
024 7 _a10.1007/978-3-031-02242-5
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
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082 0 4 _a620
_223
100 1 _aMordohai, Philippos.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987050
245 1 0 _aTensor Voting
_h[electronic resource] :
_bA Perceptual Organization Approach to Computer Vision and Machine Learning /
_cby Philippos Mordohai, Gérard Medioni.
250 _a1st ed. 2006.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2006.
300 _aIX, 126 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Image, Video, and Multimedia Processing,
_x1559-8144
505 0 _aIntroduction -- Tensor Voting -- Stereo Vision from a Perceptual Organization Perspective -- Tensor Voting in ND -- Dimensionality Estimation, Manifold Learning and Function Approximation -- Boundary Inference -- Figure Completion -- Conclusions.
520 _aThis lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organization methodology applicable in situations that may seem heterogeneous initially. We show how several problems can be posed as the organization of the inputs into salient perceptual structures, which are inferred via tensor voting. The work presented here extends the original tensor voting framework with the addition of boundary inference capabilities; a novel re-formulation of the framework applicable to high-dimensional spaces and the development of algorithms for computer vision and machine learning problems. We show complete analysis for some problems, while we briefly outline our approach for other applications and provide pointers to relevant sources.
650 0 _aEngineering.
_99405
650 0 _aElectrical engineering.
_987052
650 0 _aSignal processing.
_94052
650 1 4 _aTechnology and Engineering.
_987054
650 2 4 _aElectrical and Electronic Engineering.
_987057
650 2 4 _aSignal, Speech and Image Processing.
_931566
700 1 _aMedioni, Gérard.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987059
710 2 _aSpringerLink (Online service)
_987061
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031011146
776 0 8 _iPrinted edition:
_z9783031033704
830 0 _aSynthesis Lectures on Image, Video, and Multimedia Processing,
_x1559-8144
_987063
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02242-5
912 _aZDB-2-SXSC
942 _cEBK
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