Quality Assessment of Non-dense Image Correspondences

  • Anita Sellent
  • Jochen Wingbermühle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7584)


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.


Correspondence Algorithm Endpoint Error Histogram Distance Stereo Disparity Dense Correspondence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Anita Sellent
    • 1
  • Jochen Wingbermühle
    • 1
  1. 1.CV Research LabRobert Bosch GmbHHildesheimGermany

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