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Image Edge Detection using Modified Ant Colony Optimization Algorithm based on Weighted Heuristics

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 68 - Number 15
Year of Publication: 2013
Puneet Rai
Maitreyee Dutta

Puneet Rai and Maitreyee Dutta. Article: Image Edge Detection using Modified Ant Colony Optimization Algorithm based on Weighted Heuristics. International Journal of Computer Applications 68(15):5-9, April 2013. Full text available. BibTeX

	author = {Puneet Rai and Maitreyee Dutta},
	title = {Article: Image Edge Detection using Modified Ant Colony Optimization Algorithm based on Weighted Heuristics},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {68},
	number = {15},
	pages = {5-9},
	month = {April},
	note = {Full text available}


Ant Colony Optimization (ACO) is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image's intensity value. This paper proposes an improved method based on heuristic which assigns weight to the neighborhood. Experimental results are provided to support the superior performance of the proposed approach.


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