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231223s2009 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2009.64
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|a eng
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| 100 |
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|a Detry, Renaud
|e verfasserin
|4 aut
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| 245 |
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|a A probabilistic framework for 3D visual object representation
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|c 2009
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 06.10.2009
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|a Date Revised 21.08.2009
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a We present an object representation framework that encodes probabilistic spatial relations between 3D features and organizes these features in a hierarchy. Features at the bottom of the hierarchy are bound to local 3D descriptors. Higher level features recursively encode probabilistic spatial configurations of more elementary features. The hierarchy is implemented in a Markov network. Detection is carried out by a belief propagation algorithm, which infers the pose of high-level features from local evidence and reinforces local evidence from globally consistent knowledge, effectively producing a likelihood for the pose of the object in the detection scene. We also present a simple learning algorithm that autonomously builds hierarchies from local object descriptors. We explain how to use our framework to estimate the pose of a known object in an unknown scene. Experiments demonstrate the robustness of hierarchies to input noise, viewpoint changes, and occlusions
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 700 |
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|a Pugeault, Nicolas
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Piater, Justus H
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 31(2009), 10 vom: 16. Okt., Seite 1790-803
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|x 1939-3539
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|g year:2009
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|u http://dx.doi.org/10.1109/TPAMI.2009.64
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