The complexity of multiway cuts (extended abstract)
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Fast relative depth computation for an active stereo vision system
Real-Time Imaging
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Crossover, Macromutationand, and Population-Based Search
Proceedings of the 6th International Conference on Genetic Algorithms
Stereo Correspondence by Dynamic Programming on a Tree
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Local Stereo Matching with Segmentation-based Outlier Rejection
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Stereo Vision in Structured Environments by Consistent Semi-Global Matching
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Stereo Matching with Reliable Disparity Propagation
3DIMPVT '11 Proceedings of the 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission
A fast line segment based dense stereo algorithm using tree dynamic programming
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
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. 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).