000 | 03541nam a22005175i 4500 | ||
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001 | 978-3-031-02242-5 | ||
003 | DE-He213 | ||
005 | 20240730165016.0 | ||
007 | cr nn 008mamaa | ||
008 | 220601s2006 sz | s |||| 0|eng d | ||
020 |
_a9783031022425 _9978-3-031-02242-5 |
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024 | 7 |
_a10.1007/978-3-031-02242-5 _2doi |
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050 | 4 | _aT1-995 | |
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_aTBC _2bicssc |
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_a620 _223 |
100 | 1 |
_aMordohai, Philippos. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _987050 |
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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. |
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300 |
_aIX, 126 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aSynthesis Lectures on Image, Video, and Multimedia Processing, _x1559-8144 |
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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 |
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650 | 0 |
_aElectrical engineering. _987052 |
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650 | 0 |
_aSignal processing. _94052 |
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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 |
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710 | 2 |
_aSpringerLink (Online service) _987061 |
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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 |
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