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WαSH: Weighted α-Shapes for Local Feature Detection

  • Christos Varytimidis
  • Konstantinos Rapantzikos
  • Yannis Avrithis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7573)

Abstract

Depending on the application, local feature detectors should comply with properties that are often contradictory, e.g. distinctiveness vs. robustness. Providing a good balance is a standing problem in the field. In this direction, we propose a novel approach for local feature detection starting from sampled edges. The detector is based on shape stability measures across the weighted α-filtration, a computational geometry construction that captures the shape of a non-uniform set of points. The extracted features are blob-like and include non-extremal regions as well as regions determined by cavities of boundary shape. Overall, the approach provides distinctive regions, while achieving high robustness in terms of repeatability and matching score, as well as competitive performance in a large scale image retrieval application.

Keywords

Scale Invariant Feature Transform Query Time Weighted Point Regular Triangulation Maximally Stable Extremal Region 
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

  • Christos Varytimidis
    • 1
  • Konstantinos Rapantzikos
    • 1
  • Yannis Avrithis
    • 1
  1. 1.National Technical University of AthensGreece

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