  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.
