Image Matching with Spatially Variant Contrast and Offset: A Quadratic Programming Approach

  • Alexander Shorin
  • Georgy Gimel’farb
  • Patrice Delmas
  • Jonh Morris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5342)

Abstract

Template matching is widely used in machine vision, digital photogrammetry, and multimedia data mining to search for a target object by similarity between its prototype image (template) and a sensed image of a natural scene containing the target. In the real-world environment, similarity scores are frequently affected by contrast / offset deviations between the template and target signals. Most of the popular least-squares scores presume only simple smooth deviations that can be approximated with a low-order polynomial. This paper proposes an alternative and more general quadratic programming based matching score that extends the conventional least-squares framework onto both smooth and non-smooth signal deviations.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Alexander Shorin
    • 1
  • Georgy Gimel’farb
    • 1
  • Patrice Delmas
    • 1
  • Jonh Morris
    • 1
  1. 1.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand

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