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Maximum likelihood motion segmentation using eigendecomposition

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2 Author(s)
Robles-Kelly, A. ; Dept. of Comput. Sci., York Univ., UK ; Hancock, E.R.

This paper presents an iterative maximum likelihood framework for motion segmentation. Our representation of the segmentation problem is based on a similarity matrix for the motion vectors for pairs of pixel blocks. By applying eigendecomposition to the similarity matrix, we develop a maximum likelihood method for grouping the pixel blocks into objects which share a common motion vector. We experiment with the resulting clustering method on a number of real-world motion sequences. Here ground truth data indicates that the method can result in motion classification errors as low as 3%

Published in:

Image Analysis and Processing, 2001. Proceedings. 11th International Conference on

Date of Conference:

26-28 Sep 2001