  Edge detection is an important field in image processing. Edges characterize
object boundaries and are therefore useful for segmentation, registration,
feature extraction, and identification of objects in a scene. In this paper, an
approach utilizing an improvement of Baljit and Amar method which uses Shannon
entropy other than the evaluation of derivatives of the image in detecting
edges in gray level images has been proposed. The proposed method can reduce
the CPU time required for the edge detection process and the quality of the
edge detector of the output images is robust. A standard test images, the
real-world and synthetic images are used to compare the results of the proposed
edge detector with the Baljit and Amar edge detector method. In order to
validate the results, the run time of the proposed method and the pervious
method are presented. It has been observed that the proposed edge detector
works effectively for different gray scale digital images. The performance
evaluation of the proposed technique in terms of the measured CPU time and the
quality of edge detector method are presented. Experimental results demonstrate
that the proposed method achieve better result than the relevant classic
method.
