Open AccessJournal Article
Image edge detection using ant colony optimization
TL;DR: An edge detection technique that is based on ACO is presented, which establishes a pheromone matrix that represents the edge information at each pixel based on the routes formed by the ants dispatched on the image.
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Abstract: 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.
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Citations
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Guided artificial bee colony algorithm
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References
A threshold selection method from gray level histograms
TL;DR: A nonparametric and unsupervised method ofautomatic threshold selection for picture segmentation is presented, whereby an optimal threshold is selected by the discriminant criterion so as to maximize the separability of the resultant classes in gray levels.
44K
Ant system: optimization by a colony of cooperating agents
Marco Dorigo,Vittorio Maniezzo,Alberto Colorni +2 more
- 01 Feb 1996
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Ant colony system: a cooperative learning approach to the traveling salesman problem
TL;DR: The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.
8.3K
Ant Colony Optimization
Ranjith Kumar A,Ranjith Kumar +1 more
- 01 Jan 2011
TL;DR: In this article, a new ACO model that overcomes the difficulties found when working with a huge construction graph is presented. But it is not suitable when the graph size can be a challenge for the computer memory and cannot be completely generated or stored in it.
An ant colony optimization algorithm for image edge detection
Jing Tian,Weiyu Yu,Shengli Xie +2 more
- 01 Jun 2008
TL;DR: The proposed ACO-based edge detection approach is able to establish a pheromone matrix that represents the edge information 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.
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