Non-dense image correspondence estimation algorithms are known for their speed, robustness and accuracy. However, current evaluation methods evaluate correspondences point-wise and consider only correspondences that are actually estimated. They cannot evaluate the fact that some algorithms might leave important scene correspondences undetected - correspondences which might be vital for succeeding applications. Additionally, often the reference correspondences for real world scenes are also sparse. Outliers that do not hit a reference measurement can remain undetected with the current, point-wise evaluation methods. To assess the quality of correspondence fields we propose a histogram based evaluation metric that does not rely on point-wise comparison and is therefore robust to sparsity in estimate as well as reference. © 2012 Springer-Verlag.
CITATION STYLE
Sellent, A., & Wingbermühle, J. (2012). Quality assessment of non-dense image correspondences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7584 LNCS, pp. 114–123). Springer Verlag. https://doi.org/10.1007/978-3-642-33868-7_12
Mendeley helps you to discover research relevant for your work.