Abstract
Most of the classical mathematical methods for edge detection based on the derivative of the pixels of the original image are Gradient operators, Laplacian and Laplacian of Gaussian operators. Gradient based edge detection methods, such as Roberts, Sobel and Prewitts, have used two 2-D linear filters to process vertical edges and horizontal edges separately to approximate first-order derivative of pixel values of the image. The Laplacian edge detection method has used a 2-D linear filter to approximate second-order derivative of pixel values of the image. Major drawback of second-order derivative approach is that the response at and around the isolated pixel is much stronger. In this research study, a novel approach utilizing Shannon entropy other than the evaluation of derivates of the image in detecting edges in gray level images has been proposed. The proposed approach solves this problem at some extent. In the proposed method, we have used a suitable threshold value to segment the image and achieve the binary image. After this the proposed edge detector is introduced to detect and locate the edges in the image. A standard test image is used to compare the results of the proposed edge detector with the Laplacian of Gaussian edge detector operator. In order to validate the results, seven different kinds of test images are considered to examine the versatility of the proposed edge detector. It has been observed that the proposed edge detector works effectively for different gray scale digital images. The results of this study were quite promising.
Highlights
Edge detection has received much attention during the past two decade because of its significant importance in many research areas[1]
The performance of the proposed scheme is evaluated through the simulation results using MATLAB 7 for a set of eight test images and the results of the proposed scheme are compared with the results of well-established edge detection operator on the same set of test images. Such edge detection operator is Laplacian of Gaussian (LOG).Laplacian of a Gaussian (LOG) is chosen for comparison because both approaches are rotation invariant
It has been observed that the proposed method for edge detection works well as compare to LOG
Summary
Edge detection has received much attention during the past two decade because of its significant importance in many research areas[1]. The edge is a prominent feature of an image; it is the front-end processing stage in object recognition and image understanding system. The detection results benefit applications such as image enhancement, recognition, morphing, compression, retrieval, watermarking, hiding, restorationand registration etc[3]. Edge detection concerns localization of abrupt changes in the gray level of an image[4]. Edge detection can be defined as the boundary between two regions separated by two relatively distinct gray level properties[5]. The causes of the region dissimilarity may be due to some factors such as the geometry of the scene, the radio metric characteristics of the surface, the illumination and so on[6]
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