Optimization in Edge Detection using Ant Colony Optimization
Poonam Kasare1, Jyoti Kulkarni2, Rajankumar Bichkar3
1Poonam Kasare, PG student, Dept. of E &TC, Pimpri Chinchwad College of Engineering, Pune, India.
2Jyoti S. Kulkarni, Research Scholar, Dept. of E &TC, G.H. Raisoni College of Engineering and Management, Pune, India And Asst. Prof., Dept. of E & TC, Pimpri Chinchwad College of Engineering, Pune, India.
3Rajankumar S. Bichkar, Principal, Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, India.
Manuscript received on 13 August 2019. | Revised Manuscript received on 18 August 2019. | Manuscript published on 30 September 2019. | PP: 8167-8170 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6134098319/2019©BEIESP | DOI: 10.35940/ijrte.C6134.098319
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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: Image processing is now emerged in different fields like medical, security and surveillance, remote sensing & satellite applications and much more. Image processing includes different operations such as feature extraction, object detection and recognition, X-ray scanning etc. All such operations required edge detection to get better quality image. Edge detection is performed to distinguish different objects in an image by finding the boundaries or edges between them. Edges are used to isolate particular objects from their background as well as to recognize or classify objects. In this paper, comparison of various edge detection techniques such as Sobel, Prewitt, Roberts, Canny, LoG and Ant Colony Optimization Algorithm is given. Ant colony Optimization(ACO) use parallelism which reduces the computation time as size of an image increases.
Keywords: ACO, Canny, Pheromone.
Scope of the Article: Simulation Optimization and Risk Management