Abstract
Edge detection is an important component in many computer vision tasks since edges convey information about the objects in an image. This paper presents a comparative analysis of the proposed edge detector with respect to one of the state-of-the-art methods, the Canny edge detector. Our edge detection model involves the supervised optimization of a cellular automaton rule with particle swarm optimization. Using this scheme we obtain transferable rules that can be applied on multiple images with similar properties. We test the two methods on clean and noisy images and the proposed method outperforms Canny on average on our data set containing a variety of edges.
Citare
Dumitru, D., Andreica, A., Balint, Z., Diosan, L., Robustness analysis of transferable cellular automata rules optimized for edge detection, KES 2020, , 713-722