Genetic programming for edge detection: A global approach

Edge detection is an important task in computer vision. This paper describes a global approach to edge detection using genetic programming (GP). Unlike most traditional edge detection methods which use local window filters, this approach directly uses an entire image as input and classifies pixels directly as edges or non-edges without preprocessing or postprocessing. Shifting operations and common standard operators are used to form the function set. Precision, recall and true negative rate are used to construct the fitness functions. This approach is examined and compared with the Laplacian and Sobel edge detectors on three sets of images providing edge detection problems of varying difficulty. The results suggest that the detectors evolved by GP outperform the Laplacian detector and compete with the Sobel detector in most cases.