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Top-Points as Interest Points for Image Matching

  • B. Platel
  • E. Balmachnova
  • L. M. J. Florack
  • B. M. ter Haar Romeny
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3951)

Abstract

We consider the use of top-points for object retrieval. These points are based on scale-space and catastrophe theory, and are invariant under gray value scaling and offset as well as scale-Euclidean transformations. The differential properties and noise characteristics of these points are mathematically well understood. It is possible to retrieve the exact location of a top-point from any coarse estimation through a closed-form vector equation which only depends on local derivatives in the estimated point. All these properties make top-points highly suitable as anchor points for invariant matching schemes. By means of a set of repeatability experiments and receiver-operator-curves we demonstrate the performance of top-points and differential invariant features as image descriptors.

Keywords

Feature Vector Receiver Operating Characteristic Curve Critical Path Interest Point Query Image 
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 2006

Authors and Affiliations

  • B. Platel
    • 1
  • E. Balmachnova
    • 1
  • L. M. J. Florack
    • 1
  • B. M. ter Haar Romeny
    • 1
  1. 1.Technische Universiteit EindhovenEindhovenThe Netherlands

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