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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Stenger, Björn
|e verfasserin
|4 aut
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|a Model-based hand tracking using a hierarchical Bayesian filter
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|c 2006
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 20.09.2006
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|a Date Revised 15.11.2006
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|a published: Print
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|a Citation Status MEDLINE
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|a This paper sets out a tracking framework, which is applied to the recovery of three-dimensional hand motion from an image sequence. The method handles the issues of initialization, tracking, and recovery in a unified way. In a single input image with no prior information of the hand pose, the algorithm is equivalent to a hierarchical detection scheme, where unlikely pose candidates are rapidly discarded. In image sequences, a dynamic model is used to guide the search and approximate the optimal filtering equations. A dynamic model is given by transition probabilities between regions in parameter space and is learned from training data obtained by capturing articulated motion. The algorithm is evaluated on a number of image sequences, which include hand motion with self-occlusion in front of a cluttered background
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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 Thayananthan, Arasanathan
|e verfasserin
|4 aut
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| 700 |
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|a Torr, Philip H S
|e verfasserin
|4 aut
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| 700 |
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|a Cipolla, Roberto
|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 28(2006), 9 vom: 23. Sept., Seite 1372-84
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:28
|g year:2006
|g number:9
|g day:23
|g month:09
|g pages:1372-84
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