Stereo vision for planetary rovers: stochastic modeling to near real-time implementation
International Journal of Computer Vision
Error propagation in machine vision
Machine Vision and Applications
Object-centered surface reconstruction: combining multi-image stereo and shading
International Journal of Computer Vision
Performance characterization of fundamental matrix estimation under image degradation
Machine Vision and Applications - Special issue on performance evaluation
Performance Assessment Through Bootstrap
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterizing the uncertainty of the fundamental matrix
Computer Vision and Image Understanding
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prediction Error as a Quality Metric for Motion and Stereo
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Real-World stereo-analysis evaluation
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
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A new approach to characterizing the performance of point-correspondence algorithms is presented. Instead of relying on any "ground truth', it uses the self-consistency of the outputs of an algorithm independently applied to different sets of views of a static scene. It allows one to evaluate algorithms for a given class of scenes, as well as to estimate the accuracy of every element of the output of the algorithm for a given set of views. Experiments to demonstrate the usefulness of the methodology are presented.