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JRM Vol.21 No.6 pp. 698-708
doi: 10.20965/jrm.2009.p0698
(2009)

Paper:

Parallel Computation of the Region-Based Level Set Method for Boundary Detection of Moving Objects

Xianfeng Fei*,**, Yasunobu Igarashi***, Makoto Shinkai****,
Masatoshi Ishikawa*****, and Koichi Hashimoto*

*Graduate School of Information Science, Tohoku University, 6-6-01 Aramaki Aza Aoba, Aoba-ku, Sendai 980-8579, Japan

**Electrical Engineering College, Guizhou University, Nanming, Guiyang, Guizhou 550003, China

***Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan

****Corporate Research and Development Group, Sharp Corporation, 1-9-2 Nakase, Mihama-ku, Chiba 261-8520, Japan

*****Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

Received:
April 21, 2009
Accepted:
October 19, 2009
Published:
December 20, 2009
Keywords:
level set method, parallel computing, high-speed camera, high-speed image processing, cell
Abstract

We formulate a parallel, region-based level set model to speed up accurate boundary detection of moving objects in low-contrast images, applying parallelization and discretization to a Chan-Vese (CV) model. We implement the model in a column parallel vision (CPV) system that is one of parallel image processing systems we developed for robot vision. Using a microscopic image of moving paramecia as a sample of a low-contrast image, our model detects moving paramecia boundaries within 2 ms per image. Comparisons of our model to a CV model using the CPV system and a nonparallel PC, we found that our model cuts calculation time for a CV model while obtaining accuracy similar to the CV model in boundary detection of moving objects.

Cite this article as:
Xianfeng Fei, Yasunobu Igarashi, Makoto Shinkai,
Masatoshi Ishikawa, and Koichi Hashimoto, “Parallel Computation of the Region-Based Level Set Method for Boundary Detection of Moving Objects,” J. Robot. Mechatron., Vol.21, No.6, pp. 698-708, 2009.
Data files:
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Last updated on Sep. 24, 2021