Cellular Automata as a Tool for Image Processing

Fundamenta Informaticae, 2003

This paper presents the theory and application of a high speed, low cost pattern classifier. The proposed classifier is built around a special class of sparse network referred to as Cellular Automata (CA). A specific class of CA, termed as Multiple Attractor Cellular Automata (MACA), has been evolved through Genetic Algorithm (GA) formulation to perform the task of pattern classification. The versatility of the classification scheme is illustrated through its application in three diverse fields -data mining, image compression, and fault diagnosis. Extensive experimental results demonstrate better performance of the proposed scheme over popular classification algorithms in respect of memory overhead and retrieval time with comparable classification accuracy. Hardware architecture of the proposed classifier has been also reported.

Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, 2019

Data classification is a well studied problem where the aim is to identify the categories in the data based on a training set. Various machine learning methods have been utilized for the problem. On the other side, cellular automata have drawn the attention of researchers as the system provides a dynamic and a discrete model for computation. In this study a novel approach is proposed for the classification problem. The method is based on formation of classes in a cellular automata by the interaction of neighborhood cells. Initially, the training data instances are assigned to the cells of a cellular automaton. The state of a cell denotes the class assignment of that point in the instance space. At the beginning of the process, only the cells that have a data instance have class assignments. However, these class assignments are spread to the neighbor cells based on a rule inspired by the heat transfer process in nature. The experiments carried out denote that the model can identify the categories in the data and promising results have been obtained.

International Journal of Information Technology and Computer Science, 2015

The cellular automaton paradigm is very appealing and its inherent simplicity belies its potential complexity. Two dimensional cellular automata are significantly applying to image processing operations. This paper describes the application of cellular automata (CA) to various morphological operations such as thinning and thickening of binary images. The description about the selection of the optimum rule set of two dimensions cellular automata for thinning and thickening of binary images is illustrated by this paper. The selection of the optimum rule set from large search space has been performed on the basis of sequential floating forward search method. The misclassification error between the images obtained by the standard function and the one obtained by cellular automata rule is used as the fitness function. The proposed method is also compared with some standard methods and found suitable for the purpose of morphological operations.

Information Sciences, 1993

This paper is a review of recent published work in the application of automata networks as part of a pattern or image recognition system. The principal requirements were to integrate model-based and data-driven approaches within a connectionist framework and to allow full parallelism. In particular, we construct a network of probabilistic cellular automata (PCAs) for iteratively resolving ambiguities and conflicts in pattern recognition. A natural implementation of inductive inference rules in such a network results in a d~amics that is sensitive to nons~metric couplings (s~aptic weights), unlike that of the more common models inspired by statistical physics (e.g., the Boltzmann machine). This, along with full parallelism, means that object recognition must be achieved through the intermediate-time rather than "infinite''-time behavior of the system. Another, more technology-driven, feature includes using local inferences insofar as possible. The framework is translation-invariant, which is natural for image re~gnition. This leads to a different architecture for describing the model from that used in Bayesian inference networks. ' We discuss only bilinear couplings here. Inference rules are often in the form, for example, that feature Q occurs only in conjunction with both features y and 6 in appropriate positions. The most direct way lo incorporate that these would be with trilinear and higher order couplings. An alternative would bc to have hidden units but keep the couplings bilinear (see [281X

Computers, 2022

Given the grid features of digital images, a direct relation with cellular automata can be established with transition rules based on information of the cells in the grid. This document presents the modeling of an algorithm based on cellular automata for digital images processing. Using an adaptation mechanism, the algorithm allows the elimination of impulsive noise in digital images. Additionally, the comparison of the cellular automata algorithm and median and mean filters is carried out to observe that the adaptive process obtains suitable results for eliminating salt and pepper type-noise. Finally, by means of examples, the result of the algorithm are shown graphically.

Arxiv preprint arXiv: …, 2008

Abstract: - This paper deals with the theory and application of 2-Dimensional, nine-neighborhood, null-boundary, uniform as well as hybrid Cellular Automata (2D CA) linear rules in image processing. These rules are classified into nine groups depending upon ...

Computing Research Repository, 2009

This paper presents a classification of Cellular Automata rules based on its properties at the nth iteration. Elaborate computer program has been designed to get the nth iteration for arbitrary 1-D or 2-D CA rules. Studies indicate that the figures at some particular iteration might be helpful for some specific application. The hardware circuit implementation can be done using opto-electronic components [1-7].

2010

Spatial patterns in an image that shows a visual perception of roughness or softness of the surface is known as the texture of the image. Most of the analysis and description of texture found in the literature is based on statistical or structural properties of this attribute [2]. The field of cellular automata (CA), which has been developed mainly to model the dynamical behavior of systems, is based on the behavior or arrangements of pixel values and their neighborhood which, according to some rules behaves in different manners [2, 8]. In this paper, within the frame of structural approach, a novel method based on the properties of linear cellular automata is proposed to characterize different sort of textures. To this purpose, it is assumed that a binary version of the image under study was generated by a cellular automata technique. By using this model a number of textural primitives are found which allows the production of a characterizing image. In order to verify the feasibility of the proposed method, texture images generated by CA techniques as well as natural images has been used.

AEU - International Journal of Electronics and Communications, 2015

In this paper, we propose a cellular edge detection (CED) algorithm which utilizes cellular automata (CA) and cellular learning automata (CLA). The CED algorithm is an adaptive, intelligent and learnable algorithm for edge detection of binary and grayscale images. Here, we introduce a new CA local rule with adaptive neighborhood type to produce the edge map of image as opposed to CA with fixed neighborhood. The proposed adaptive algorithm uses the von Neumann and Moore neighborhood types. Experimental results demonstrate that the CED algorithm has superior accuracy and performance in contrast to other edge detection methods such as Sobel, Prewitt, Robert, LoG and Canny operators. Moreover, the CED algorithm loses fewer details while extracting image edges compare to other edge detection methods.

Lecture Notes in Computer Science, 2005

As the possibility of combining different classifiers into Multiple Classifier System (MCS) becomes an important direction in machine-learning, difficulties arise in choosing the appropriate classifiers to combine and choosing the way for combining their decisions. Therefore in this paper we present a novel approach -Classificational Cellular Automata (CCA). The basic idea of CCA is to combine different classifiers induced on the basis of various machine-learning methods into MCS in a non-predefined way. After several iterations of applying adequate transaction rules only a set of the most appropriate classifiers for solving a specific problem is preserved.