An Improved Edge Detection algorithm using Cellular Automata

Abstract These Edges of an image are considered a type of crucial information that can be extracted by applying detectors with different methodology. Most of the classical mathematical methods for edge detection based on the derivative of the pixels of the original image are Gradient operators, Laplacian and Laplacian of Gaussian operators. Gradient based edge detection methods, such as Roberts, Sobel and Prewitts, have used two 2-D or 3-D linear filters to process vertical edges and horizontal edges separately to approximate first-order derivative of pixel values of the image. The Laplacian edge detection method has used a 3-D linear filter to approximate second-order derivative of pixel values of the image. Major drawback of second-order derivative approach is that the response at and around the isolated pixel is much stronger. In this paper, a novel technique based on Cellular Automata other than the evaluation of derivates of the image in detecting edges in gray level images has been proposed. The proposed approach solves this problem at some extent. In the proposed method, we have used a set of rules to detect and locate the edges in the image. It has been observed that the proposed edge detector works effectively for different gray scale digital images. The results of this study were quite promising. Keywords: Edge, Edge detection, Cellular Automata, Gradient operators