Seybold Report ISSN: 1533-9211
Dr.V.Sheshathri*
Assistant Professor, Department of Computer Applications, Erode Arts and Science College,
Erode-638 009, Tamilnadu, India, E-mail: sheshathrieac@gmail.com
Dr.M.Sumathi
Assistant Professor, Department of Computer Science, Kamban College of Arts and Science,
Coimbatore- 641669, Tamilnadu, India. E-mail: sumathiabiaswin@gmail.com
N.Shunmuganathan
Ph.D Research Scholar, Department of Computer Science, Erode Arts and Science College,
Erode-638 009, Tamilnadu, India, E-mail: shunmuganathan@outlook.com
Vol 17, No 10 ( 2022 ) | Licensing: CC 4.0 | Pg no:2188-2198 | Published on: 31-10-2022
Abstract
Edge information the most major image edge and it incorporates the helpful information for image recognition. It has provided valuable and important for people to recognize the target and interpret the image. This paper is combine two methods together and performs image edge detection. This method conducts ant colony optimization (ACO) and particle swarm optimization (PSO) on the image and transforms its sub-optimal solution to the distribution of initial pheromone. It performs ACO and shows the edge information of the image. An image edge detection method based on Hybrid Ant Colony and Particle Swarm Optimization (ACPSO) is proposed in this paper to handle the following defects: the traditional image edge detection methods are not good at image edge detection, they are trapped into local optimum easily, they can lose balance between two mechanisms: random and positive feedback easily and they are too slow in convergence speed. Edge detection method plays an important role in image processing. It consists in detecting edges or contours in images which allows extracting information from them. A method requests to develop that can identify edges clearly in a noisy image. The ACPSO method is applied to find image edge detection. The proposed ACPSO method presents better edges then traditional method.
Keywords:
Edge Detection, ACO, PSO, ACPSO, Positive Feedback, Pheromone