A Divided-and-Conquer Algorithm Based on Guided Selection for Two-View Motion Segmentation

Abstract:

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Motion segmentation for dynamic scene is a fundamental problem in computer vision due to its well-known chicken-and-egg character. The key issue is to estimate both numbers and parameters of motions simultaneously. Different from global clustering method and random sampling scheme, in this paper, we propose a divided-and-conquer algorithm to solve the motion segmentation problem. A guided selection is used to choose the most creditable hypothetical motion as a candidate seed and then make it grow larger. Compared to previous works such as expectation maximization and factorization approaches, there is no need for any pre-knowledge of the number of motions. To global non-parametric clustering method, it is fast because each time we only do cluster process in a partitioned sub-set. Experiments have shown that the proposal method can give a satisfying result for motion segmentation.

Info:

Periodical:

Edited by:

Qi Luo

Pages:

452-458

DOI:

10.4028/www.scientific.net/AMM.20-23.452

Citation:

H. J. Wang et al., "A Divided-and-Conquer Algorithm Based on Guided Selection for Two-View Motion Segmentation", Applied Mechanics and Materials, Vols. 20-23, pp. 452-458, 2010

Online since:

January 2010

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Price:

$35.00

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