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Accurate detection and characterization of corner points using circular statistics and fuzzy clustering

  • M. E. Díaz
  • G. Ayala
  • J. Albert
  • F. J. Ferri
  • J. Domingo
Statistical Pattern Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)

Abstract

Accurate detection and characterization of corner points in grey level images is considered as a pattern recognition problem. The method considers circular statistic tests to detect 2D features. A fuzzy clustering algorithm is applied to the edge orientations near the prospective corners to detect and classify them. The method is based on formulating hypotheses about the distribution of these orientations around an edge, corner or other 2-D feature. The method may provide accurate estimates of the direction of the edges that converge in a corner, along with their confidence intervals. Experimental results show the method to be robust enough against noise and contrast changes. Fuzzy membership improves the results of the algorithm and both versions (crisp and fuzzy) give better results than other previously proposed corner detectors.

Keywords

Fuzzy Cluster Corner Point Grey Level Image Mise Distribution Gradient Orientation 
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 1998

Authors and Affiliations

  • M. E. Díaz
    • 1
  • G. Ayala
    • 1
  • J. Albert
    • 1
  • F. J. Ferri
    • 1
  • J. Domingo
    • 1
  1. 1.Institut de RobòticaUniversitat de ValènciaBurjassotSpain

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