Ant Colony Optimization
Edge detection using ant algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Edge detection based on the collective intelligence of artificial swarms
ESPOCO'05 Proceedings of the 4th WSEAS International Conference on Electronic, Signal Processing and Control
A preliminary study for multiple ant colony system with new communication strategies
ICCOM'05 Proceedings of the 9th WSEAS International Conference on Communications
Sensitive ant model for combinatorial optimization
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Guided artificial bee colony algorithm
ECC'11 Proceedings of the 5th European conference on European computing conference
A Swarm Intelligence inspired algorithm for contour detection in images
Applied Soft Computing
Robust Kinect-based guidance and positioning of a multidirectional robot by Log-ab recognition
Expert Systems with Applications: An International Journal
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Ant colony optimization (ACO) is a population-based metaheuristic that mimics the foraging behavior of ants to find approximate solutions to difficult optimization problems. It can be used to find good solutions to combinatorial optimization problems that can be transformed into the problem of finding good paths through a weighted construction graph. In this paper, an edge detection technique that is based on ACO is presented. The proposed method establishes a pheromone matrix that represents the edge information at each pixel based on the routes formed by the ants dispatched on the image. The movement of the ants is guided by the local variation in the image's intensity values. The proposed ACO-based edge detection method takes advantage of the improvements introduced in ant colony system, one of the main extensions to the original ant system. Experimental results show the success of the technique in extracting edges from a digital image.