Paper Title:
A Divided-and-Conquer Algorithm Based on Guided Selection for Two-View Motion Segmentation
  Abstract

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, K. Chen, Y. Zhou, Y. Zhang, H. B. Guan, "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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