Soft edge maps from edge detectors evolved by genetic programming

Wenlong Fu, Mark Johnston, Mengjie Zhang

Abstract: Genetic Programming (GP) has been used for edge detection, but there is no previous work that analyses the outputs from a GP detector before thresholding them to binary edge maps. When the threshold used in a GP system slightly changes, the final edge map from a detector may change a lot. Mapping the outputs of a GP detector to a grayscale space by a linear transformation is not effective. In order to address the problem of the sensitivity to the threshold values, we replace the linear transformation with an S-shaped transformation. We design two new fitness functions so that the outputs from an evolved detector can obtain better edge maps after mapping into a grayscale space. Experimental results show that the S-shaped transformation obtains soft edge maps similar to the fixed threshold and the new fitness functions improve the edge detection accuracy.