A Novel Method of Edge Detection using Cellular Automata

International journal of engineering research and technology, 2021

In the domain of digital image processing, a number of methods for detecting edges have been proposed till now like Robert, Sobel, Prewitt, Canny, etc. All these methods have some benefits and some drawbacks. Edge detection can be done in more efficient way using the concept of Cellular Automata. Different rules for cellular automata are efficient to find out the edge of bio-medical images also. Outputs obtain on applying cellular automata are better in terms of computation time and clarity. As consequences the detected edges using Cellular Automata are clear and continual. The outcomes of edge detection of bio-medical images can be confirmed with graphical instances.

— Edge is the boundary between an object and the background, and identifies the boundary between overlapping and non-over lapping objects. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. Here fuzzy logic based image processing is used for accurate and noise free edge detection and Cellular Learning Automata (CLA) is used for enhance the previously-detected edges with the help of the repeatable and neighborhood-considering nature of CLA. The different result of edge detection technique is compared with fuzzy edge detected and resulting edge is enhanced using CLA. In this paper, all the algorithms and result are prepared in MATLAB.

Intelligent Analysis of Multimedia Information, 2000

In this paper are presented solutions to develop algorithms for digital image processing focusing particularly on edge detection. Edge detection is one of the most important phases used in computer vision and image processing applications and also in human image understanding. In this chapter, implementation of classical edge detection algorithms it is presented and also implementation of algorithms based on the theory of Cellular Automata (CA). This work is totally related to the idea of understanding the impact of the inherently local information processing of CA on their ability to perform a managed computation at the global level. If a suitable encoding of a digital image is used, in some cases, it is possible to achieve better results in comparison with the solutions obtained by means of conventional approaches. The software application which is able to process images in order to detect edges using both conventional algorithms and CA based ones is written in C# programming language and experimental results are presented for images with different sizes and backgrounds.

International Journal of Computer Applications, 2015

Edge detection is one of the most commonly used operations in image analysis, which is recently receiving much attention. There are various existing traditional edge detectors which are used to estimate edge magnitude and orientation. The concept of Cellular Automata (CA) also has been used for edge detection making the technique more efficient and effective. This paper includes a comparative study of the traditional techniques with the Cellular Automata (CA) technique which is based on rules of Conway’s Game of Life (GoL). The comparative analysis is carried out based on a particular set of assumed parameters which are suitable enough to conclude the best performing edge detector General Terms Edge Detection, Cellular Automata, Game of Life, Traditional Edge Detection Methods.

International Journal of Engineering Development and Research, 2014

In this paper, traditional methods of edge detection and their problems are discussed. After that high performance method for edge detection that is fuzzy logic based image processing is used for accurate and noise free edge detection and Cellular Learning Automata (CLA) is used for enhance the previously-detected edges with the help of the repeatable and neighborhood-considering nature of CLA. In the end, we compare it with popular methods such as Sobel, Canny and Prewitts with enhanced by CLA. Edge is most important visual parameter so if the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter, and shape can be measured. In this paper, all the algorithms and result are prepared in MATLAB.

OALib, 2015

The mathematical model that aims at determining points in an image at which the image brightness suddenly changes is called edge detection. This study aims to propose a new hybrid method for edge detection. This method is based on cellular learning automata (CLA) and stochastic cellular automata (SCA). In the first part of the proposed method, statistic features of the input image are hired to have primary edge detection. In the next step CLA and SCA are employed to amplify pixels situated on edge and castrate those pixels which are part of the image background. The simulation results are conducted to prove proposed method performance and these results suggest that the proposed method is more efficient in finding edges and outperforms the existing edge detection algorithms.

International Journal of Computer Applications, 2012

Cellular Automata can be successfully applied in image processing. In this paper, we propose a new edge detection algorithm, based on cellular automata to extract edges of different types of images, using a totalistic transition rule. The metaheuristic PSO is used to find out the optimal and appropriate transition rules set of cellular automata for edge detection task. This combination increases the efficiency of the algorithm, and ensures its convergence to an optimal edge as shown in various experiments. Comparisons are made with standard methods (Canny) and other algorithms based on Cellular Automata and Genetic Algorithms. Obtained results are promising.

2013

187 All Rights Reserved © 2013 IJARECE  Abstract Edge detection plays an important role in the image analysis and image recognition in the field of Image processing. Edge detection is carried out before noise removal the resultant image is not cleared and it should contain noisy pixels. In order to avoid the problem the paper proposes a method for edge detection after the noise removal. For denoising using the Cellular Automata algorithm so that it performs well compared to other denoising filters. The Fuzzy Logic is used for the detection of edges in the denoised image. So that the detected edges are cleared and the experimental results showed that the edges are located correctly and it performs well.

Series in Computer Vision, 2011

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.

Computer Vision and Image Understanding, 2010

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.