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Technical Program
Paper Detail
| Paper: | TA-L5.6 |
| Session: | Motion Estimation |
| Time: | Tuesday, October 10, 11:40 - 12:00 |
| Presentation: |
Lecture
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| Title: |
A MINIMUM-ENTROPY PROCEDURE FOR ROBUST MOTION ESTIMATION |
| 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. |
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