Complex Correlation Statistic for Dense Stereoscopic Matching

  • Jan Čech
  • Radim Šára
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

A traditional solution of area-based stereo uses some kind of windowed pixel intensity correlation. This approach suffers from discretization artifacts which corrupt the correlation value. We introduce a new correlation statistic, which is completely invariant to image sampling, moreover it naturally provides a position of the correlation maximum between pixels. Hereby we can obtain sub-pixel disparity directly from sampling invariant and highly discriminable measurements without any postprocessing of the discrete disparity map. The key idea behind is to represent the image point neighbourhood in a different way, as a response to a bank of Gabor filters. The images are convolved with the filter bank and the complex correlation statistic (CCS) is evaluated from the responses without iterations.

Keywords

Root Mean Square Error Root Mean Square Stereo Match Correlation Table Gabor Response 
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 2005

Authors and Affiliations

  • Jan Čech
    • 1
  • Radim Šára
    • 1
  1. 1.Center for Machine PerceptionCzech Technical UniversityPragueCzech Republic

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