1 November 2004 Genetic-algorithm-based stereo vision with no block partitioning of input images
Author Affiliations +
Optical Engineering, 43(11), (2004). doi:10.1117/1.1795818
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.
Biao Wang, Ronald Chung, Chun-Lin Shen, "Genetic-algorithm-based stereo vision with no block partitioning of input images," Optical Engineering 43(11), (1 November 2004). http://dx.doi.org/10.1117/1.1795818


Binary data

Image resolution

3D image processing


Image processing

Optical engineering


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