Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer
Application of genetic algorithms to stereo matching of images
Pattern Recognition Letters - Special issue on genetic algorithms
Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Cooperative Algorithm for Stereo Matching and Occlusion Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Disparity-Space Images and Large Occlusion Stereo
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
The Rayset and its applications
GRIN'01 No description on Graphics interface 2001
Occlusion Detectable Stereo - Systematic Comparison of Detection Algorithms
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Camera field rendering for static and dynamic scenes
Graphical Models
Hi-index | 0.00 |
In this paper, a new genetic-based stereo algorithm is presented. Our motivation is to improve the accuracy of the disparity map by removing the mismatches caused by both occlusions and false targets. In our approach, the stereo matching problem is considered as an optimization problem. The algorithm first takes advantage of multi-view stereo images to detect occlusions, and therefore, removes mismatches caused by visibility problems. By optimizing the compatibility between corresponding points and the continuity of the disparity map using a genetic algorithm, mismatches caused by false targets are removed. The quadtree structure is used to implement the multi-resolution framework. Since nodes at different level of the quadtree cover different number of pixels, selecting nodes at different levels gives a similar effect as adjusting the window size at different locations of the image. The experimental results show that our approach can generate more accurate disparity maps than two existing approaches. In addition, we introduce a new disparity map evaluation technique, which is developed based on a similar technique employed in the image segmentation area. Comparing with two existing evaluation approaches, the new technique can evaluate the disparity maps generated without additional knowledge of the scene, such as the correct depth information or novel views.