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Statistically Valid Graph Representations of Scale-Space Geometry

  • Tomoya Sakai
  • Atsushi Imiya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)

Abstract

This paper presents a statistical scale-selection criterion for graph representations derived from differential geometric features of a greyscale image in a Gaussian scale space. The image gradient in scale space derives hierarchical and topological relationships among the bright and dark components in the image. These relationships can be represented as a tree and a skeleton-like graph, respectively. Since the image at small scales contains invalid geometric features due to noise and numerical errors, a validation scheme is required for the detected features. The presented scale-selection criterion allows us to identify the valid features used for the graph representations with statistical confidence.

Keywords

Singular Point Probability Density Function Saddle Point Noise Image Scale Space 
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 2008

Authors and Affiliations

  • Tomoya Sakai
    • 1
  • Atsushi Imiya
    • 1
  1. 1.Institute of Media and Information TechnologyChiba UniversityJapan

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