Disparity Confidence Measures on Engineered and Outdoor Data

  • Ralf Haeusler
  • Reinhard Klette
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


Confidence measures for stereo analysis are not yet a subject of detailed comparative evaluations. There have been some studies, but still insufficient for estimating the performance of these measures. We comparatively discuss confidence measures whose performance appeared to be ‘promising’ to us, by evaluating their performance on commonly used stereo test data. Those data are either engineered and come with accurate ground truth (for disparities), or they are recorded outdoors and come with approximate ground truth. The performance of confidence measures varies widely between these two types of data. We propose modifications of confidence measures which can improve their performance on outdoor data.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ralf Haeusler
    • 1
  • Reinhard Klette
    • 1
  1. 1.Department of Computer ScienceThe University of AucklandAucklandNew Zealand

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