Novel Approach of Edges Detection for Digital Images Based On Hybrid Types of Entropy

Abstract

Edges detection of digital images is used in a various fields of applications ranging from real-time video surveillance and traffic management to medical imaging applications. Most of the classical methods for edge detection are based on the first and second order derivatives of gray levels of the pixels of the original image. These processes give rise to the exponential increment of computational time. This paper shows the new algorithm based on both the Tsallis entropy and the Shannon entropy together for edge detection using split and merge technique. The objective is to find the best edge representation and minimize the computation time. A set of experiments in the domain of edge detection are presented. An edge detection performance compared to the previous classic methods, such as Canny, LOG, and Sobel. Analysis show that the effect of the proposed method is better than those methods in execution time and also is considered as easy implementation

Automated Edge Detection Using Convolutional Neural Network

“…However, errors can be made due to the noise while mask is moved around the image [6]. The class of edge detection using entropy has been widely studied, and many of the paper , for examples [7], [8], [9].…”

New Method Based on Multi-Threshold of Edges Detection in Digital Images

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