Dynamic depth recovery using belief propagation

In this paper, we study the dynamic stereo problem, i.e. to recover the shape of dynamic scene from multiple synchronized image sequences. To incorporate both spatial and temporal information for depth recovery, we propose a statistical framework that uses pixel process model to encode temporal cohe...

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Bibliographic Details
Main Authors: Yihua Xu, Dongsheng Wang, Heung-yeung, Songde Ma
Format: Conference Proceeding
Language:English
Subjects:
Belief propagation
Computer interfaces
Image motion analysis
Image sequences
Layout
Markov random fields
Robustness
Shape
Stereo vision
User interfaces
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Summary:In this paper, we study the dynamic stereo problem, i.e. to recover the shape of dynamic scene from multiple synchronized image sequences. To incorporate both spatial and temporal information for depth recovery, we propose a statistical framework that uses pixel process model to encode temporal coherence, and Markov random fields (MRFs) for spatial coherence. In this framework, the dynamic depth recovery problem is finally formulated as an optimization problem, and is optimized by using the belief propagation algorithm. Experimental results with the real dynamic scenes illustrate our method's ability of robust shape recovery.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2003.1246979