Edge Detection using Moore Neighborhood

2017

Cellular automata can be significantly applied in image processing tasks. In this paper, a novel method to train two dimensional cellular automata for detection of edges in digital images has been proposed and experiments have been carried out for the same. Training of two dimensional cellular automata means selecting the optimum rule set from the given set of rules to perform a particular task. In order to train the cellular automata first, the size of rule set is reduced on the basis of symmetry. Then the sequential floating forward search method for rule selection is used to select the best rule set for edge detection. The misclassification error has been used as an objective function to train the cellular automata for edge detection. The whole experiment has been divided in two parts. First the training was performed for binary images then it is performed for gray scale images. A novel method of thresholding the image by Otsu’s algorithm and then applying the cellular automata r...

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.

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.

— 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.

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, 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.

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.

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.

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.

This study employed the use of Cellular Neural Networks (CNN) for Edge Detection in images due to its high operational speed. The process of edge detection is unavoidable in many image processing tasks such as obstacle detection and satellite picture processing .The conventional edge detector models such as Sobel Operator, Robert Cross, among which Canny is the best, have high computational time. The CNN Model is a class of Differential Equation that has been known to have many application areas and high operational speed. The work investigated four parameters: resolutions, processing time, false alarm rate, and usability for performance evaluation. The CNN Model was modified by using hyperbolic tangent (tanh x) and Von Neumann Neighborhood. The modified CNN Model and enhanced Canny Model were implemented using MATLAB 7.0 running on Pentium III and 128 MB RAM Personal Computer. A series of images served as input for both Canny and modified CNN Model. With several images tested, the overall results indicated that the two models have similar resolutions with average computational time of 1.1078 seconds and 2.293 seconds for CNN-based and Enhanced Canny Model respectively. The hyperbolic entry a 22 of the cloning template A made our work fully controllable since max(tanh x) = +1 and min(tanh x) = -1. A consideration of the set of digital images showed that edge maps which result from Canny Model have adjacent boundaries that tend to merge. The CNN model obviously produced the optimum edge map with edges that are one-pixel wide and unbroken. The false alarm rate of noise variance probability     2  F P for which an edge detector can easily declare an edge pixel given that there is no edge, showed the value 0.8525 for CNN Model and 0.4595 for Canny Model. The CNN parameters can be adjusted and modeled to solve Partial Differential Equations and Maximum likelihood problems for edge detection while Canny Model cannot be easily adjusted for any other functionality. The CNN-based edge detector performed better than the popular canny operator in terms of the computational time required, usability, and false alarm rate.