Edge Detection Using Distinct Particle Swarm Optimization
Naveen Singh Dagar1, Pawan Kumar Dahiya2

1Naveen Singh Dagar, Research Scholar at Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Sonepat, Haryana, India.
2Pawan Kumar Dahiya, Assistant Professor in Electronics & Communication Engineering Department in Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Sonepat, Haryana, India.

Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1050-1057 | Volume-8 Issue-9, July 2019 | Retrieval Number: H7259068819/19©BEIESP | DOI: 10.35940/ijitee.H7259.078919

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Edge detection is long-established in computer perception approach such as object detection, shape matching, medical image classification etc. For this reason many edge detectors like, Sobel, Robert, Prewitt, Canny etc. has been progressed to increase the effectiveness of the edge pixels. All these approaches work fine on images having minimum variation in intensity. Therefore, a new objective function based distinct particle swarm optimization (DPSO) is proposed in this paper to identify unbroken edges in an image. The conventional edge detectors such as “Canny” & computational intelligent techniques like ACO, GA and PSO are compared with proposed algorithm. Precision, Recall & F-Score is used as performance parameters for these edge detection techniques. The ground truth images are taken as reference edge images and all the edge images acquired by different edge detection systems are contrasted with reference edge image with ascertain the Precision, Recall and F-Score. The techniques are tested on 500 test images from the “BSD500” datasets. The empirical results presented by the proposed algorithm performance better than other edge detection techniques in the images. The proposed method observes edges more accurately and smoothly than other edge detection techniques such as “Canny, ACO, GA and PSO” in different images.
Keyword: Image Processing, Edge Detection, Distinct Particle Swarm Optimization, BSD500, ACO, GA, PSO, F-Score.

Scope of the Article: Signal and Image Processing