Stereo Matching with Nonlinear Diffusion
International Journal of Computer Vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
A Simple Stereo Algorithm to Recover Precise Object Boundaries and Smooth Surfaces
International Journal of Computer Vision
Prediction Error as a Quality Metric for Motion and Stereo
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Adaptive Support-Weight Approach for Correspondence Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Processing by Semiglobal Matching and Mutual Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Third Eye for Performance Evaluation in Stereo Sequence Analysis
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
High-accuracy stereo depth maps using structured light
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Stereoscopic Scene Flow Computation for 3D Motion Understanding
International Journal of Computer Vision
A Quantitative Evaluation of Confidence Measures for Stereo Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Confidence measures for stereo vision are already popular for some time. Yet, comprehensive results on their performance evaluation are rare. There is still not yet any agreement on answering the question 'what is a good confidence measure'. Very little work has been done for improving discriminativity by exploiting information from combinations of different confidence measures. We present a method to determine an upper bound for performance increase possible by combining given confidence measures. We also provide a solution for a fusion of measures resulting in improved confidence accuracy on popular stereo benchmark data.