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999			_c387886 _d387886
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007			cr nn 008mamaa
008			230428s2006 sz | s |||| 0|eng d
020			_a9783031022425
024	7		_a10.1007/978-3-031-02242-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTA1634 _b2006 EB
100	1		_aMordohai, Philippos _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688312
245	1	0	_aTensor Voting : _bA Perceptual Organization Approach to Computer Vision and Machine Learning _cby Philippos Mordohai, Gérard Medioni
250			_a1st edition 2006
264		1	_aCham _bSpringer International Publishing _c2006
300			_a1 recurso en línea (IX, 126 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2006
650		7	_2embne _9159793 _aVisión por ordenador
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aMedioni, Gérard _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688314
776	0	8	_iPrinted edition: _z9783031011146
776	0	8	_iPrinted edition: _z9783031033704
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02242-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI