Conditional Variance of Differences: A Robust Similarity Measure for Matching and Registration

  • Atsuto Maki
  • Riccardo Gherardi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)

Abstract

This paper presents a new similarity measure, the sum of conditional variance of differences (SCVD), designed to be insensitive to highly non-linear intensity transformations such as the ones occurring in multi-modal image registration and tracking. It improves on another recently introduced statistical measure, the sum of conditional variances (SCV), which has been reported to outperform comparable information theoretic similarity measures such as mutual information (MI) and cross-cumulative residual entropy (CCRE). We also propose two additional extensions that further increase the robustness of SCV(D) by relaxing the quantisation process and making it symmetric. We demonstrate the benefits of SCVD and improvements on image matching and registration through experiments.

Keywords

Similarity Measure Mutual Information Reference Image Image Registration Conditional Variance 
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

  • Atsuto Maki
    • 1
  • Riccardo Gherardi
    • 1
  1. 1.Cambridge Research LaboratoryToshiba Research EuropeCambridgeUnited Kingdom

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