English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Edge detection is an important field in image processing. Edges characterizeobject boundaries and are therefore useful for segmentation, registration,feature extraction, and identification of objects in a scene. In this paper, anapproach utilizing an improvement of Baljit and Amar method which uses Shannonentropy other than the evaluation of derivatives of the image in detectingedges in gray level images has been proposed. The proposed method can reducethe CPU time required for the edge detection process and the quality of theedge detector of the output images is robust. A standard test images, thereal-world and synthetic images are used to compare the results of the proposededge detector with the Baljit and Amar edge detector method. In order tovalidate the results, the run time of the proposed method and the perviousmethod are presented. It has been observed that the proposed edge detectorworks effectively for different gray scale digital images. The performanceevaluation of the proposed technique in terms of the measured CPU time and thequality of edge detector method are presented. Experimental results demonstratethat the proposed method achieve better result than the relevant classicmethod.

New Edge Detection Technique based on the Shannon Entropy in Gray Level Images