International Journal of Computer Applications (0975 – 8887) Volume 61– No.3, January 2013 26 Edge Detection using Moore Neighborhood Pratibha Sharma Student, Deptt. of Computer Science, MITS, Lakshmangarh Manoj Diwakar Asstt. Professor, Deptt. of Computer Science, MITS, Lakshmangarh Niranjan Lal Asstt. Professor, Deptt. of Computer Science, MITS, Lakshmangarh ABSTRACT Edge detection is a fundamental tool in image processing. Several edge detectors have been proposed in literature for enhancing and detecting edges in images. Image Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. In this paper, the application of two-dimensional cellular automata using Moore Neighborhood has been proposed for edge detection. The idea is simple but effective technique for edge detection. Edge basically occurs where there is significant change in intensity. The principle of the algorithm used is to increase the difference between those pixels where intensity values change significantly. So by using this concept, detected edges are wider and clear. The given algorithm can be applied to gray scale and monochrome images. General Term Edge Detection Keywords Cellular Automata, Moore Neighborhood 1. INTRODUCTION Edge detection is the most fundamental and important tool for feature detection and extraction. Edge detectors aims at identifying points in digital image at which the image brightness changes abruptly or sharply. Edges occur when there is significant change in the local intensities of an image. Edges typically occur on the boundary between two different regions in an image. The goal of edge detection is to produce a line drawing which shows the edges in an image. By using edge detectors, important features can be extracted from images of an image like corners, line, curves etc. The detection results benefit applications such as image enhancement, recognition, morphing, restoration, registration, compression, retrieval, hiding etc. Now the intensity changes are caused by geometrical or non geometrical events. Geometric events include the object boundary (discontinuity in depth and/or surface color and texture) and surface boundary (discontinuity in surface orientation and/or surface color and texture). Non Geometric events include the specularity (direct reflection of light, such as a mirror) and shadows (from other objects or from the same object). Much of the work has been done for edge detection. Most of the algorithms used for edge detection have been broadly classified in 2 categories: Gradient based algorithms and Laplacian based algorithms. The gradient based algorithm detects the edges by looking for the maximum and minimum in the first derivative of the image. Roberts, Prewitt, Sobel are base on Gradient method. The Laplacian method searches for zero crossings in the second derivative of the image to find edges. A variety of algorithms have been proposed for analyzing image intensity variation, including statistical methods [1–5], difference methods [6–8] and curve fitting methods[9–13]. The early days of works on edge detection were done by Sobel and Roberts [14]. Their detection methods are based on simple intensity gradient operators. Later on, much of the research works have been devoted to the development of detectors with good detection performance as well as good localization performances. Edge detection in noisy environment can be treated as an optimal linear filter design problem [15–19]. Canny [16] formulated edge detection as an optimization problem and defined an optimal filter, which can be efficiently approximated by the first derivative of Gaussian function in the one-dimensional case. Canny’s filter was further extended to recursive filters [18], which provide a more efficient way for image noise filtering and edge detection. Other edge detection methods include differentiation based edge detection using logarithmic image processing (LIP) models, contrast-based methods, relaxation labeling techniques and anisotropic diffusion. In fact, these methods can be combined to achieve better performance. For instance, the second directional derivative edge detector proposed by Haralick [10] can be regarded as a hybrid of the differentiation method and the statistical hypothesis testing method, which leads to better performance in a noisy environment. Classical methods like Sobel, Prewitt, Robert, Prewitt, Marr- Hildreth and Canny some disadvantages also. For example, Sobel, Prewitt and Laplacian based methods are sensitive to noise and are less accurate. Marr-Hildreth algorithm malfunctions at corners and Canny algorithm takes more computation time and still results are not accurate. So a method is required that provides better clarity, performs well in noisy images and also, it should not detect any false edges. In the proposed algorithm, simple mathematics has been applied for achieving the requirement of better clarity and good performance in noisy images. The principle used in the algorithm is based on finding mean and maximum again and again on every pixel and apply Moore neighborhood concept of Cellular Automata. The result of the algorithm will be compared with the existing common edge detectors and it will be shown that the results obtained by using Cellular Automata are much better. 2. CELLULAR AUTOMATA A Cellular Automata is a discrete model studied in computability theory, mathematics, physics, complexity science, theoretical biology and microstructure modeling. It consists of a regular grid of cells, each one in one of a finite number of states, such as "On" and "Off. The grid can be in any finite number of dimensions. Generally one dimensional and two dimensional Cellular Automata are used. For each cell, a set of cells called its neighborhood (usually including the cell itself) is defined relative to the specified cell. For example, the neighborhood of a cell might be defined as the