A New Algorithm Based Entropic Threshold for Edge Detection in Images

TL;DR

This paper introduces a novel entropy-based thresholding algorithm for edge detection that outperforms traditional methods like Canny, LOG, and Sobel in robustness, quality, and computational efficiency.

Contribution

The paper presents a new entropic thresholding approach for edge detection, demonstrating improved accuracy and efficiency over existing methods.

Findings

Outperforms Canny, LOG, and Sobel in accuracy

Provides more robust edge detection results

Reduces computation time significantly

Abstract

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.