Unsupervised Edge Detector based on Evolved Cellular Automata

Extensive research has been performed in image processing to find the best edge detector, from the gradient-based operators to evolved Cellular Automata (CA). Some of these detectors have weak points, such as disconnected edges, the incapacity of detecting the branching edges or the need of a ground truth that is not always available. To overcome these issues, we propose a CA-based edge detector adapted to the particularities of the image. The adaption means to identify the best CA rule, which is an optimization problem solved by a Genetic Algorithm (GA). The GA requires a fitness function and we propose to use an unsupervised fitness based on edge dissimilarity. The performed numerical experiments are meant to evaluate the proposed approach and to emphasize that some of the weak points of a well-known detector (Canny) can be overcome by our method.