ICIP 2006, Atlanta, GA

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Paper Detail

Session:Motion Estimation
Time:Tuesday, October 10, 11:40 - 12:00
Presentation: Lecture
Topic: Motion Detection and Estimation: Other
Authors: Sylvain Boltz; Laboratoire I3S 
 Eric Wolsztynski; Laboratoire I3S 
 Eric Debreuve; Laboratoire I3S 
 Eric Thierry; Laboratoire I3S 
 Michel Barlaud; Laboratoire I3S 
 Luc Pronzato; Laboratoire I3S 
Abstract: We focus on motion estimation using a block matching approach and suggest using a minimum-entropy criterion. Many entropy-based estimation procedures exist, such as plug-in estimators based on Parzen windowing. We consider here an alternative that is applicable to data of any dimension and that circumvents the critical issues raised by kernel-based methods. To the best of our knowledge, this criterion has not yet been considered for image processing problems. The inherent robustness property of entropy is expected to provide a robust and efficient estimation of the motion vector of a block of a video sequence. In particular, the minimum-entropy estimator should be robust to occlusions and variations of luminance, for which standard approaches like SSD usually meet their limitations.