title: Low-level feature extraction for edge detection using genetic programming.
abstract: Edge detection is a subjective task. Traditionally, a moving window approach is used, but the window size in edge detection is a tradeoff between localization accuracy and noise rejection. An automatic technique for searching a discriminated pixel's neighbors to construct new edge detectors is appealing to satisfy different tasks. In this paper, we propose a genetic programming (GP) system to automatically search pixels (a discriminated pixel and its neighbors) to construct new low-level subjective edge detectors for detecting edges in natural images, and analyze the pixels selected by the GP edge detectors. Automatically searching pixels avoids the problem of blurring edges from a large window and noise influence from a small window. Linear and second-order filters are constructed from the pixels with high occurrences in these GP edge detectors. The experiment results show that the proposed GP system has good performance. A comparison between the filters with the pixels selected by GP and all pixels in a fixed window indicates that the set of pixels selected by GP is compact but sufficiently rich to construct good edge detectors.
authors.forename: Wenlong
authors.initials: W
authors.lastname: Fu
authors.forename: Mark
authors.initials: M
authors.lastname: Johnston
authors.forename: Mengjie
authors.initials: M
authors.lastname: Zhang
mesh_terms: D000818:Animals; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007254:Information Science; D008957:Models, Genetic