An edge detection method using outer totalistic cellular automata

A number of Cellular Automata (CA)-based edge detectors have been developed recently due to the simplicity of the model and the potential for simultaneous removal of different types of noise in the process of detection. This paper introduced a novel edge detector using Outer Totalistic Cellular Automata. Its performance has been compared with other recently developed CA-based edge detectors, in addition to some classic methods, through testing images from a public library. Visual and quantitative measurement of similarity with manually marked correct edges confirmed the superiority of the proposed method over conventional and state-of-the-art CA-based edge detectors

IMAGES SEGMENTATION BASED CONTOUR USING EVCA APPROACH, EVOLUTIONARY CELLULAR AUTOMATA

We use an evolutionary process to seek a specialized powerful rule of Cellular Automata (CA) among a set of best rules for extracting edges in a given black-white image. This best set of local rules determines the future state of CA in an asynchronous way. The Genetic Algorithm (GA) is applied to search the best CA rules that can realize better the edge detectio

Image processing using 3-state cellular automata

This paper describes the application of cellular automata (CA) to various image processing tasks such as denoising and feature detection. Whereas our previous work mainly dealt with binary images, the current work operates on intensity images. The increased number of cell states (i.e. pixel intensities) leads to a vast increase in the number of possible rules. Therefore, a reduced intensity representation is used, leading to a three state CA that is more practical. In addition, a modified sequential floating forward search mechanism is developed in order to speed up the selection of good rule sets in the CA training stage. Results are compared with our previous method based on threshold decomposition, and are found to be generally superior. The results demonstrate that the CA is capable of being trained to perform many different tasks, and that the quality of these results is in many cases comparable or better than established specialised algorithms

Self-Organized Specialization and Controlled Emergence in Organic Computing Systems

In this chapter we studied a first approach to generate suitable rule sets for solving classification problems on systems of autonomous, memory constrained components. It was shown that a multi agent system that uses interacting Pittsburgh-style classifier systems can evolve appropiate rule sets. The system evolves specialists for parts of the classification problem and cooperation between them. In this way the components overcome their restricted memory size and are able to solve the entire problem. It was shown that the communication topology between the components strongly influences the average number of components that a request has to pass until it is classified. It was also shown that the introduction of communication costs into the fitness function leads to a more even distribution of knowledge between the components and reduces the communication overhead without influencing the classification performance very much. If the system is used to generate rule sets to solve classification tasks on real hardware systems, communication cost in the training phase can thus lead to a better knowledge distribution and small communication cost. That is, in this way the system will be more robust against the loss of single components and longer reliable in case of limited energy resources

On solving edge detection by emergence

Emergence is the process of deriving some new and coherent structures, patterns and properties in a complex system. Emergent phenomena occur due to interactions (non-linear and distributed) between the elements of a system over time. An important aspect concerning the emergent phenomena is that they are observable on a macroscopic level, whereas they are produced by the interaction of the elements of the system on a microscopic level. In this paper, we attempt to grab some emergence and complexity principles in order to apply them for problem solving. As an application, we consider the edge detection problem a key task in image analysis. Problem solving by emergence consists in discovering the local interaction rules, which will be able to produce a global solution to the problem that the system faces. More clearly, it consists in finding the local rules which will have some awaited and adequate global behavior, to solve a given problem. This approach relies on evolving cellular automata using a genetic algorithm. The aim is to find automatically the rules that allow solving the edge detection problem by emergence. For the sake of simplicity and convenience, the proposed method was tested on a set of binary images,. Very promising results have been obtained