Abstract
This paper presents a new edge detector based on a cellular automata model. A uniform cellular automaton rule using a von Neumann neighborhood is proposed for carrying out edge detection on binary and gray-scaled images. A computational model and characterization of the state space of the rule are analyzed using a finite state machine. The work shows that a cellular automata-based model often provides an optimum edge map on binary images, and on average is better than the compared edge operators for gray-scaled images.
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