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Novel Statistical Approaches to the Quantitative Combination of Multiple Edge Detectors

  • Stamatia Giannarou
  • Tania Stathaki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4141)

Abstract

This paper aims at describing a new framework which allows for the quantitative combination of different edge detectors based on the correspondence between the outcomes of a preselected set of operators. This is inspired from the problem that despite the enormous amount of literature on edge detection techniques, there is no single one that performs well in every possible image context. The so called Kappa Statistics are employed in a novel fashion to enable a sound performance evaluation of the edge maps emerged from different parameter specifications. The proposed method is unique in the sense that the balance between the false detections (False Positives and False Negatives) is explicitly assessed in advanced and incorporated in the estimation of the optimum threshold. Results of this technique are demonstrated and compared to individual edge detection methods.

Keywords

Edge Detection Weight Kappa Edge Image Edge Pixel True 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 2006

Authors and Affiliations

  • Stamatia Giannarou
    • 1
  • Tania Stathaki
    • 1
  1. 1.Communications and Signal Processing GroupImperial College LondonLondonUK

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