Abstract
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.
This work was sponsored in part by the Defense Advanced Research Projects Agency under contract F33615-97-C-1023 monitored by Wright Laboratory. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency, the United States Government, or SRI International.
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Leclerc, Y.G., Luong, QT., Fua, P. (2000). Characterizing the Performance of Multiple-Image Point-Correspondence Algorithms Using Self-Consistency. In: Triggs, B., Zisserman, A., Szeliski, R. (eds) Vision Algorithms: Theory and Practice. IWVA 1999. Lecture Notes in Computer Science, vol 1883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44480-7_3
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DOI: https://doi.org/10.1007/3-540-44480-7_3
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