Genetic programming for edge detection: a Gaussian-based approach

Wenlong Fu, Mark Johnston, Mengjie Zhang

Abstract: Gaussian-based filtering techniques have been popularly applied to edge detection. However, how to effectively tune parameters of Gaussian filters and how to effectively combine different Gaussian filters are still open issues. In this study, a new genetic programming (GP) approach is proposed to automatically tune parameters of Gaussian filters and automatically combine different types of Gaussian filters to extract edge features. In general, it is time-consuming for GP to evolve edge detectors using a large training image dataset. To efficiently evolve edge detectors from a large training image dataset, we propose sampling techniques (randomly selecting training images) to evolve Gaussian-based edge detectors. A sampling technique only using part of a set of images obtains similar performance to the training data using all of these images. The evolved edge detectors from the proposed sampling technique perform better than the Gaussian gradient and rotation invariant surround suppression. Based on the analysis of GP evolving edge detectors, it is suggested that combining Gaussian filters should be based on different types of Gaussian filters, and the Gaussian gradient should be considered as a major filter in these combinations.