Scale- and rotation-robust genetic programming-based corner detectors

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. © 2010 Springer-Verlag Berlin Heidelberg.