GPGPU Implementation of a Genetic Algorithm for Stereo Refinement

Author
Keywords
Abstract
During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU) has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement. The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance.
Year of Publication
2015
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
3
Issue
Special Issue on Digital Economy
Number
2
Number of Pages
69-76
Date Published
03/2015
ISSN Number
1989-1660
Citation Key
URL
http://www.ijimai.org/JOURNAL/sites/default/files/files/2015/02/ijimai20153_2_9.pdf
DOI
10.9781/ijimai.2015.329