Cellular automata as a tool for image processing

An overview is given on the use of cellular automata for image processing. We first consider the number of patterns that can exist in a neighbourhood, allowing for invariance to certain transformation. These patterns correspond to possible rules, and several schemes are described for automatically learning an appropriate rule set from training data. Two alternative schemes are given for coping with gray level (rather than binary) images without incurring a huge explosion in the number of possible rules. Finally, examples are provided of training various types of cellular automata with various rule identification schemes to perform several image processing tasks

Training cellular automata for image processing

Abstract — Experiments were carried out to investigate the possibility of training cellular automata (CA) to perform several image processing tasks. Even if only binary images are considered the space of all possible rule sets is still very large, and so the training process is the main bottleneck of such an approach. In this paper the sequential floating forward search method for feature selection was used to select good rule sets for a range of tasks, namely: noise filtering (also applied to gray scale images using threshold decomposition), thinning, and convex hulls. Various objective functions for driving the search were considered. Several modifications to the standard CA formulation were made (the B-rule and 2-cycle CAs) which were found in some cases to improve performance. I