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Scale- and Rotation-Robust Genetic Programming-Based Corner Detectors

  • Kisung Seo
  • Youngkyun Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

This paper introduces GP- (Genetic Programming-) based robust corner detectors for scaled and rotated images. Previous Harris, SUSAN and FAST corner detectors are highly efficient for well-defined corners, but frequently mis-detect as corners the corner–like edges which are often generated in rotated images. It is very difficult to avoid incorrectly detecting as corners many edges which have characteristics similar to corners. In this paper, we have focused on this challenging problem and proposed using Genetic Programming to do automated generation of corner detectors that work robustly on scaled and rotated images. Various terminal sets are presented and tested to capture the key properties of corners. Combining intensity-related information, several mask sizes, and amount of contiguity of neighboring pixels of similar intensity, allows a well-devised terminal set to be proposed. This method is then compared to three existing corner detectors on test images and shows superior results.

Keywords

corner detector genetic programming automated generation scale and rotation robust 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kisung Seo
    • 1
  • Youngkyun Kim
    • 1
  1. 1.Dept. of Electronic EngineeringSeokyeong University SeoulKorea

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