Alignment by Maximization of Mutual Information
International Journal of Computer Vision
A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Non-parametric Local Transforms for Computing Visual Correspondence
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Stereo Processing by Semiglobal Matching and Mutual Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Stereo Matching Costs on Images with Radiometric Differences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cooperative Stereo Matching with Color-Based Adaptive Local Support
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Comparison of dense stereo using CUDA
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
| Hi-index | 0.00 |
Most recent stereo algorithms are designed to perform well on close range stereo datasets with relatively small baselines and good radiometric conditions. In this paper, different matching costs on the Semi-Global Matching algorithm are evaluated and compared using aerial image sequences and satellite images with ground truth. The influence of various cost functions on the stereo matching performance using datasets with different baseline lengths and natural radiometric changes is evaluated. A novel matching cost merging Mutual Information and Census is introduced and shows the highest robustness and accuracy. Our study indicates that using an adaptively weighted combination of Mutual Information and Census as matching cost can improve the peformance of stereo matching for airborne image sequences and satellite images.