Future Generation Computer Systems
A parallel implementation of ant colony optimization
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Ant Colony Optimization
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Edge detection using ant algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Edge detection improvement by ant colony optimization
Pattern Recognition Letters
AntShrink: Ant colony optimization for image shrinkage
Pattern Recognition Letters
An efficient ant-based edge detector
Transactions on computational collective intelligence I
IEEE Computational Intelligence Magazine
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Guest editorial: special section on ant colony optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An evolutionary image matching approach
Applied Soft Computing
Depth image enlargement using an evolutionary approach
Image Communication
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Ant colony optimization (ACO) is an optimization algorithm inspired by the natural collective behavior of ant species. The ACO technique is exploited in this paper to develop a novel image edge detection approach. The proposed approach is able to establish a pheromone matrix that represents the edge presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of ants are driven by the local variation of the image's intensity values. Extensive experimental results are provided to demonstrate the superior performance of the proposed approach.