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Optical Engineering

Genetic-algorithm-based stereo vision with no block partitioning of input images
Author(s): Biao Wang; Ronald Chung; Chun-Lin Shen
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Paper Abstract

Stereo correspondence can be formulated as an optimization problem. In this formulation, however, most of the existing solutions adopt gradient-based approaches, whose performance is dependent on the initialization. This paper presents a genetic-algorithm-based solution that is not gradient-based and thus should have less sensitivity toward the quality of the initialization. A specific coding design is employed that represents each solution candidate for the three-dimensional description of the imaged scene as an individual that embraces numerous chromosomes. Through a set of specially designed genetic operators, a population of such individuals is allowed to evolve to reach a globally optimal or near-optimal solution. Our solution scheme also includes a coarse-to-fine search strategy to reduce the matching ambiguity and the computations needed. Experimental results on synthetic and real images illustrate the performance of the approach.

Paper Details

Date Published: 1 November 2004
PDF: 8 pages
Opt. Eng. 43(11) doi: 10.1117/1.1795818
Published in: Optical Engineering Volume 43, Issue 11
Show Author Affiliations
Biao Wang, Nanjing Univ. of Aeronautics and Astronautics (China)
Ronald Chung, Chinese Univ. of Hong Kong (Hong Kong China)
Chun-Lin Shen, Nanjing Electronic Devices Institute (China)

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