International Journal of Computer Science and Electronics Engineering (IJCSEE) Volume 1, Issue 4 (2013) ISSN 2320-401X; EISSN 2320-4028 An Analysis of Edge Detectors and Improved Hybrid Technique for Edge Detection of Images Mamta. Juneja, and Parvinder S. Sandhu A. Edge Detection [2] Abstract—This paper provides an analysis of various edge detection techniques viz. sobel, prewitt, robert, laplacian of gaussian It is technique for locating areas with strong intensity and canny in which canny edge detector outperforms all existing edge contrasts. It amounts to locating each edge pixel and detectors. A new hybrid edge detection technique for extracting determining its orientation. Edge detection is one of the most edges, line, boundaries and circles from a given input image is also commonly used operations in image analysis, and there are proposed in this paper. It utilizes canny for extracting edges and probably more algorithms in the literature for enhancing and thereafter output edges are refined by application of hough transform. It has been successful to extract edges, lines, boundaries and circles detecting edges than any other single subject. The reason for of any image and is immune to various noise effects and other this is that edges form the outline of an object. An edge is the disturbances. boundary between an object and the background, and indicates the boundary between overlapping objects. This means that if Keywords—canny, circle detection, edge detection, hough the edges in an image can be identified accurately, all of the transform, laplacian of gaussian, line detection, prewitt, robert, sobel. objects can be located and basic properties such as area, perimeter, and shape can be measured. Since computer vision I. INTRODUCTION involves the identification and classification of objects in an N edge [1] is defined to be a sequence of pixels that image, edge detections is an essential tool. Edge detection is a A border two homogeneous regions of different intensities very important area in the field of Computer Vision, which help in segmentation and object recognition. It Segmentation and object recognition. It is a fundamental of indicates discontinuity in image intensity function. An Edge in low-level image processing and good edges are necessary for an image is a significant local change in the image intensity, higher level processing. usually associated with a discontinuity in either the image Edge detectors are the filters which filter out the useless intensity or the first derivative of the image intensity. information and preserve the useful information in image. Discontinuities in the image intensity can be either Step edge, Three fundamental steps involved in edge detection are: where the image intensity abruptly changes from one value on 1. Image smoothing for noise reduction: this step involve one side of the discontinuity to a different value on the filtering the image for improving the performance of edge opposite side, or Line Edges, where the image intensity detector. abruptly changes value but then returns to the starting value within some short distance. However, Step and Line edges are 2. Detection: This step involves extracting all edge points rare in real images. Because of low frequency components or that are potential candidates to become edge point. the smoothing introduced by most sensing devices, sharp 3. Edge localization: This step involves selecting from the discontinuities rarely exist in real signals. Step edges become candidate edge points only the points that are true members of Ramp Edges and Line Edges become Roof edges, where set of points comprising an edge. intensity changes are not instantaneous but occur over a finite Edge detection techniques are broadly divided in to two distance. Illustrations of these edge shapes are shown in Figure categories gradient edge detection and Laplacian edge 1. detection. Gradient based Edge Detection: This method detects the edge points in the image by using the gradient. The gradient is tool for finding the edge strength and direction at location, f, and is denoted by ∆f , and defined as the vector, Fig. 1 Types of Edges a) Step b) Ramp c) Line d) Roof (1) This vector always points in the direction of greatest rate of Mamta.Juneja is Assistant Professor in University Institute of Engineering change at location (x, y). The magnitude of the vector can be and technology, Panjab Univesity, Chandigarh. E-mail: er_mamta@yahoo. given as, com. Prof. Dr. Parvinder S. Sandhu is with Rayat and Bahra Institute of Engineering and Bio-Technology, Punjab, India. 469 International Journal of Computer Science and Electronics Engineering (IJCSEE) Volume 1, Issue 4 (2013) ISSN 2320-401X; EISSN 2320-4028 Single response to single point: The edge detector must be (2) able to give single response to single edge point. The direction of the gradient vector is given by an angle, C. Edge detectors θ(x, y). Consider the one dimensional signal with an edge The various popular edge detectors used in image shown by jump in intensity as shown in Figure 2 below: processing as described in [1] are as follows: a) Sobel Edge Detector: The Sobel edge detection method is introduced by Sobel in 1970 [1]. In Sobel edge detector, the task of edge detection is fulfilled by performing a 2D spatial gradient convolution operation on an image. This operator uses two convolution masks Gx and Gy. Fig. 2 A Signal Representation (3) If we take the gradient of this signal (which, in one dimension, is just the first derivative with respect to t) we get Here, Gx and Gy are computed as, the shape shown in Figure 3: (4) (5) Where Zi, i=1,2,.,9 are intensity levels of each pixel in the convolution are (6) Finally, the gradient magnitude is thresholded. Sobel Fig. 3 First Derivative of Signal [2] operator is very simple and effective way for finding the edges in image. The sobel operator is used mostly for detecting The first derivative of the one dimensional signal clearly horizontal and vertical edges. However the edges detected by shows maximum is located at the center of the edge in the this sobel operator are thick which may not be suitable for original signal. some applications where the detection of the outer most Laplacian based Edge Detection: This method basically contour of an object is required. searches for zero crossings in the second derivative of the b) Prewitt’s Edge Detector: The Prewitt edge detection is image. If the first derivative is maximum, then its second proposed by Prewitt in 1970) [1] to estimate the magnitude derivative will definitely be zero. This phenomenon gives rise and orientation of an edge. The Prewitt’s edge detector also to alternate method for edge detection which is known as uses the three by three convolution mask, which is little Laplacian. Hence, we can detect the edges by searching for different from that Sobel edge detector. The convolution zero crossings in the 2nd derivative, Figure 4 shows second masks smoothens the image. The convolution mask is: derivative of the signal. (7) The prewitt’s edge detector is mainly used for detecting vertical and horizontal edges in image. c) Robert’s Cross Operator: The Roberts edge detection is Fig. 4 Second Derivative of Signal [2] introduced by Lawrence Roberts in 1965.The Roberts cross operator has 2x2 convolutions mask. The Roberts Cross B. Performance Criterion for Edge Detectors operator performs a simple, quick to compute, 2-D spatial There are problems of false edge detection, missing true gradient measurement on an image. The convolution masks for edges, producing thin or thick lines and problems due to noise. Roberts operator is shown below. Hence, it was necessary to define certain performance criterion for edge detectors. Performance criterion for edge detectors (8) involve following points. The Roberts cross detector has only one disadvantage that it Good detection: good detection means the probability of fails to detect few of the edges. These masks are mainly falsely marking the non-edge points should be as low as designed to respond maximally to edges running at 45° to the possible. pixel grid, one kernel for each of the two perpendicular Good localization: The edge detected should be as close as orientations. to the real edge points. d) Laplacian of Gaussian (LoG): The Laplacian of Gaussian was proposed by Marr in 1982.The Laplacian is a 2- 470 International Journal of Computer Science and Electronics Engineering (IJCSEE) Volume 1, Issue 4 (2013) ISSN 2320-401X; EISSN 2320-4028 D isotropic measure of the 2nd spatial derivative of an image. Minimal Response: Edge in the image should only be marked The Laplacian of an image highlights regions of rapid intensity once, and where possible, image noise should not create false change and is therefore often used for edge detection. edges. Laplacian is applied to the image for smoothing purpose, in E. Comparison of Edge Detectors [5] order to reduce the sensitivity to noise. The Laplacian L(x, y) of an image with pixel intensity Gradient-based edge detection algorithms such as the values I(x, y) is given by: Prewitt filter have a major drawback of being very sensitive to noise. The size of the kernel filter and coefficients are fixed (9) and cannot be adapted to a given image. An adaptive edge- and convolution mask are: detection algorithm is necessary to provide a robust solution that is adaptable to the varying noise levels of these images to help distinguish valid image contents from visual artifacts introduced by noise. The performance of the Canny algorithm - - - - depends heavily on the adjustable parameters, σ, which is the 1 1 1 1 standard deviation for the Gaussian filter, and the threshold - 4 - - 8 - values, ‘T1’ and ‘T2’.