A Hybrid Genetic Approach for Stereo Matching

被引：0

作者：

Kiperwasser, Eliyahu ^{[1
]}

David, Omid ^{[1
]}

Netanyahu, Nathan S. ^{[1
]}

机构：

[1] Bar Ilan Univ, Dept Comp Sci, IL-52900 Ramat Gan, Israel

来源：

GECCO'13: PROCEEDINGS OF THE 2013 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2013年

关键词：

Computer Vision; Genetic Algorithm; Stereo Matching;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present a genetic algorithm (GA)-based approach for the stereo matching problem. More precisely, the approach presented is a combination of a simple dynamic programming algorithm, commonly used for stereo matching, with a practical GA-based optimization scheme. The performance of our scheme was evaluated on standard test data of the Middlebury benchmark [1]. Specifically, the number of incorrect disparities on these data decreases by approximately 20% in comparison to the original approach (without the use of a GA).

引用

页码：1325 / 1331

页数：7

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