Algorithm based on Histogram and Entropy for Edge Detection in Gray Level Images

Abstract

Edge detection and feature extraction are widely used in image processing and computer vision applications. Most of the traditional methods for edge detection are based on the first and second order derivatives of gray levels of the pixels of the original image utilizing 2D spatial convolution masks to approximate  the derivative. In this paper we present an algorithm for edge detection in gray level images. The main objective is to solve the previous problem of traditional methods with generate suitable quality of edge detection. Our new algorithm is based on two definitions of entropy: Shannon’s classical concept and a variation called Tsallis entropy. The  novel approach utilizing Subextensive Tsallis entropy rather than the evaluation of derivatives of the image in detecting edges in gray level images has been proposed. Here, we have used a suitable threshold value to segment the image and achieve the binary image. The effectiveness is demonstrated by using many different kinds of test images from the real-world and synthetic images. The results of this study were quite promising.