IARJSET - Science, Engineering and Technology

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Edge detection is an important technique in image processing .In this paper, a new approach to edge detection (Min Constructor - Gaussian Operator) is presented. Here the result of some traditional edge detection methods such as Sobel,Prewitt and Robert operator are compared with this new approach. Edge detection algorithm is essentially a process of detection of this discontinues in an image.The nature of intensity variation points to application of derivative operators for detecting edges.Application of derivative operator on intensity image produce another image ,usually called gradient image as it reveals the rate of intensity variation .The image is then made to undergo thresholding and /or edge linking in order to yield contours.There are many ways to perform edge detection. However, the majority of different methods may be grouped into two categories, Gradient and Laplacian. The gradient method detects the edges by looking for the maximum and minimum in the first derivative of the image. The Laplacian method searches for zero crossings in the second derivative of the image to find edges. An edge has the one-dimensional shape of a ramp and calculating the derivative of the image can highlight its location. The goal of edge detection is to mark the points in a digital image at which the luminous intensity changes sharply. Sharp changes in image properties usually reflect important events and changes in properties of the world. These include discontinuities in depth, discontinuities in surface orientation, changes in material properties and Variations in scene illumination. Edge detection is a research field within image processing and computer vision, in particular within the area of feature extraction. There are many methods for edge detection(1), but most of them can be grouped into two categories, search-based and zero-crossing based. The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction. The zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Laplacian or the zero-crossings of a non-linear differential expression, as will be described in the section on differential edge