ISSN 1746-7659, England, UK Journal of Information and Computing Science Vol. 5, No. 1, 2010, pp. 003-010 A Cellular Neural Network- Based Model for Edge Detection Hezekiah Babatunde, Olusegun Folorunso 1 and Adio Akinwale Department of Computer Science, University of Agriculture, Ogun State, Nigeria (Received September 21, 2009, accepted October 8, 2009) Abstract. 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. Keywords: Cellular Neural Networks, Canny Model, Edge Detection, hyperbolic tangent, Von Neumann Neighborhood 1. Introduction In image processing, one of the most effective procedures used in sharpening the images is to improve the contrast. The contrast is improved by increasing the difference across discontinuities of the image components in order to improve the differences. Edge detection algorithms are designed to detect and highlight these continuities. It was first developed for processing satellites pictures and later become well known and widely used in digital image processing [5]. Detecting the edges of an image reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. The two methods of edge detection are gradient and Laplacian [4]. The gradient method detects the edges by looking for the maximum and minimum in the first derivative of the image while the Laplacian method searches for zero crossings in the second derivative of the image to find edges. The common examples of edge detection algorithms are: Sobel, Canny, Laplace, Robert Cross, Prewitt and Sussan Edge Detectors. Roberts Cross, Prewitt and Sobel operators are gradient based edge detectors and they all have kernel operators [2] that calculate the strength of the slope in directories which are orthogonal to each other, commonly vertical and horizontal. Later, the contributions of the different components of the slopes are combined to give the total value of the edge strength. The Prewitt operator [3] measures two components. The vertical edge components is calculated with kernel k x and the horizontal edge component is calculated 1 Corresponding author Tel: +234-8035640707 Email address : folorunsolusegun@yahoo.com Published by World Academic Press, World Academic Union