Characterizing the Performance of Multiple-Image Point-Correspondence Algorithms using Self-Consistency


Leclerc, Y. and Luong, Q.-T. and Fua, P. Characterizing the Performance of Multiple-image Point-correspondence Algorithms using Self-Consistency, in Proceedings of the Vision Algorithms: Theory and Practice Workshop (ICCV99), Corfu, Greece, Sep 1999.


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.

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