Detecting Regions from Single Scale Edges

  • Konstantinos Rapantzikos
  • Yannis Avrithis
  • Stefanos Kollias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6553)


We believe that the potential of edges in local feature detection has not been fully exploited and therefore propose a detector that starts from single scale edges and produces reliable and interpretable blob-like regions and groups of regions of arbitrary shape. The detector is based on merging local maxima of the distance transform guided by the gradient strength of the surrounding edges. Repeatability and matching score are evaluated and compared to state-of-the-art detectors on standard benchmarks. Furthermore, we demonstrate the potential application of our method to wide-baseline matching and feature detection in sequences involving human activity.


Maximally Stable Extremal Region Salient Region Detector Edge Fragment Euclidean Distance Transform Spurious Edge 
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

  • Konstantinos Rapantzikos
    • 1
  • Yannis Avrithis
    • 1
  • Stefanos Kollias
    • 1
  1. 1.School of Electrical and Computer EngineeringNational Technical University of AthensGreece

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