An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis

An edge detection is important for its reliability and security that delivers a better understanding of object recognition in applications of computer vision such as pedestrian detection, face detection, and video surveillance. This paper introduced two fundamental limitations encountered in edge detection: edge connectivity and edge thickness, those have been used by various developments in the state-of-the-art. An optimal selection of the threshold for effectual edge detection has constantly been a key challenge in computer vision. Therefore, a robust edge detection algorithm using multiple threshold approach (B-Edge) is proposed to cover both the limitations. The majorly used canny edge operator focuses on two thresholds selections and still witnesses few gaps for optimal results. To handle the loopholes of canny edge operator our method selects the simulated triple thresholds that targets to the prime issues of edge detection: image contrast, effective edge pixels selection, errors handling and similarity to the ground truth. The qualitative and quantitative experimental evaluation demonstrates that our edge detection method outperforms competing algorithms for mentioned issues. The proposed approach endeavors an improvement for both grayscale and colored images. fails using local information around edge terminals for effective edge linking.