Hybrid symbiotic genetic optimisation for robust edge-based stereo correspondence

被引:17
|
作者
Goulermas, JY [1 ]
Liatsis, P [1 ]
机构
[1] Univ Manchester, Intelligent Syst & Sensing Lab, Control Syst Ctr, Dept EE & E, Manchester M60 1QD, Lancs, England
关键词
stereo-matching; bipartite graph; genetic; symbiosis; parallel; fuzzy; figural continuity;
D O I
10.1016/S0031-3203(00)00163-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This work proposes a novel algorithm for performing robust feature-based stereo-matching, without the ordering constraint. The calculation of the disparity map is decomposed to a set of disjoint intra-row subproblems, each one having two objectives: the search for a high confidence intra-row matching and the enforcement of figural continuity at the inter-row level. A separate genetic algorithm (GA) is allocated at each epipolar to search the feasible solution space. All GAs evolve parallely in a symbiotic fashion and continuously exchange currently available solution information to enable optimisation of figural continuity, To accelerate the search, we adapt a deterministic solver to seed the GAs and design problem-specific genetic operators for greater efficiency. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:2477 / 2496
页数:20
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