Friday 26th of April 2024
 

A New Algorithm Based Entropic Threshold for Edge Detection in Images


Mohamed A. El-Sayed

Edge detection is one of the most critical tasks in automatic image analysis. There exists no universal edge detection method which works well under all conditions. This paper shows the new approach based on the one of the most efficient techniques for edge detection, which is entropy-based thresholding. The main advantages of the proposed method are its robustness and its flexibility. We present experimental results for this method, and compare results of the algorithm against several leading edge detection methods, such as Canny, LOG, and Sobel. Experimental results demonstrate that the proposed method achieves better result than some classic methods and the quality of the edge detector of the output images is robust and decrease the computation time.

Keywords: Segmentation, Edge detection, Clustering, Entropy, Thresholding, Measures of information

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ABOUT THE AUTHOR

Mohamed A. El-Sayed
Mohamed A. El-Sayed received his B.Sc. from Faculty of Science (Maths & CS in June 1994) at Minia University - Egypt. His M.Sc. from Faculty of Science (Maths\\CS) at South Valley University in 2002. Ph.D. degrees in Computer Science from Faculty of Science at Minia University in 2007. His research interests include image processing, computer graphic and graphs drawing. He has published several international journals and Conference papers in the above area. He is working in Mathematics department, Faculty of Science, Fayoum University, Egypt. Currently, he is an Assistant professor of Computer Science at Faculty of Computers and Information Science , Taif Univesity, KSA.


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