Different Techniques Of Edge Detection In Digital Image Processing

Edge detection is a process that detects the presence and location of edges constituted by sharp changes in intensity of an image. Edges define the boundaries between regions in an image, which helps with segmentation and object recognition. Edge detection of an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. The general method of edge detection is to study the changes of a single image pixel in an area, use the variation of the edge neighboring first order or second-order to detect the edge. In this paper after a brief introduction, overview of different edge detection techniques like differential operator method such as sobel operator,prewitt’s technique,Canny technique and morphological edge detection technique are given. 1.Introduction The edge detection methods based on difference operation are used widely in image processing. It could detect the variation of gray levels, but it is sensitive to noise. Edge detection is an important task in image processing. It is a main tool in pattern recognition, image segmentation, and scene analysis. An edge detector is basically a high pass filter that can be applied to extract the edge points in an image. An edge in an image is a contour across which the brightness of the image changes abruptly. In image processing, an edge is often interpreted as one class of singularities. In a function, singularities can be characterized easily as discontinuities where the gradient approaches infinity. However, image data is discrete, so edges in an image often are defined as the local maxima of the gradient[1]-[2]. Edge widely exists between objects and backgrounds, objects and objects, primitives and primitives. The edge of an object is reflected in the discontinuity of the gray. Therefore, the general method of edge detection is to study the changes of a single image pixel in a gray area, use the variation of the edge neighboring first order or second-order to detect the edge. This method is used to refer as local operator edge detection method. Edge detection is mainly the measurement, detection and location of the changes in image gray. Image edge is the most basic features of the image. When we observe the objects, the clearest part we see firstly is edge and line. According to the composition of the edge and line, we can know the object structure. Therefore, edge extraction is an important technique in graphics processing and feature extraction. The basic idea of edge detection is as follows: First, use edge enhancement operator to highlight the local edge of the image. Then, define the pixel "edge strength" and set the threshold to extract the edge point set. However, because of the noise and the blurring image, the edge detected may not be continuous[3]. This paper discuses various techniques for Edge Detection. Edge detection detects outlines of an object and boundaries between objects and the background in the image. Edge is a boundary between two homogeneous regions. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. 2.Different techniques of edge detection 2.1.Differential operator method Differential operator can outstand grey change. There are some points where grey change is bigger. And the value calculated in those points is higher applying derivative operator . So these differential values may be regarded as relevant „edge intensity‟ and gather the points set of the edge through setting thresholds for these differential values[4]. Differential operator is a classic edge detection method, which is based on the gray change of image for each pixel in their areas, using the edge close to the first-order or second order directional derivative to detect the edge. Differential operator edge detection is accomplished by the convolution. The position of first-order derivative in the image from light to dark or from dark to light has a downward or upward step, the changes of the gray value is relatively small in other locations, and the maximum of magnitude corresponds to the location of the edge. Both the theoretical of the application basis on: the feature of first-order differential operator obtains extreme in the step edge for the first-order derivative in image and will be 0 in the roof-like edge; otherwise, the second-order differential operator has the opposite values. First-order operator including: Sobel operator, Prewitt operator, Roberts operator, etc. Second order operator including: LOG operator, Canny operator, etc. FirstPooja Sharma,Gurpreet Singh, Amandeep Kaur / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 3, May-Jun 2013, pp.458-461 459 | P a g e order derivative corresponds to a gradient, firstorder derivative operator is the gradient operator[5]. 2.1.1 .Sobel Edge Detection Operator The Sobel edge detection operation extracts all of edges in an image, regardless of direction. Sobel operation has the advantage of providing both a differencing and smoothing effect. It is implemented as the sum of two directional edge enhancement operations. The resulting image appears as an unidirectional outline of the objects in the original image. Constant brightness regions become black, while changing brightness regions become highlighted. Derivative may be implemented in digital form in several ways. However, the Sobel operators have the advantage of providing both a differencing and a smoothing effect. Because derivatives enhance noise, the smoothing effect is particularly attractive feature of the Sobel operators[7] . Fig.1 The operator consists of a pair of 3×3 convolution kernels as shown in Fig. 1. One kernel is simply the other rotated by 90°. The kernels can be applied separately to the input image, to produce separate measurements of the gradient component in each orientation ( Gx and Gy) The gradient magnitude is given by: Typically, an approximate magnitude is computed using: which is much faster to compute.[6]-[7] 2.1.2.Robert’s cross operator The Roberts Cross operator performs a simple, quick to compute, 2-D spatial gradient measurement on an image. Pixel values at each point in the output represent the estimated absolute magnitude of the spatial gradient of the input image at that point. The operator consists of a pair of 2×2 convolution kernels as shown in Figure. One kernel is simply the other rotated by 90°. This is very similar to the Sobel operator. Fig.2 These kernels are designed to respond maximally to edges running at 45° to the pixel grid, one kernel for each of the two perpendicular orientations. The kernels can be applied separately to the input image, to produce separate measurements of the gradient component in each orientation ( Gx and Gy). These can then be combined together to find the absolute magnitude of the gradient at each point and the orientation of that gradient. The gradient magnitude is given by: although typically, an approximate magnitude is