An ant colony optimization algorithm for image edge detection

@article{Tian2008AnAC,
  title={An ant colony optimization algorithm for image edge detection},
  author={Jing Tian and Weiyu Yu and Shengli Xie},
  journal={2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)},
  year={2008},
  pages={751-756}
}
  • Jing Tian, Weiyu Yu, S. Xie
  • Published 1 June 2008
  • Computer Science
  • 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
Ant colony optimization (ACO) is an optimization algorithm inspired by the natural behavior of ant species that ants deposit pheromone on the ground for foraging. [] Key Method Furthermore, the movements of these ants are driven by the local variation of the imagepsilas intensity values. Experimental results are provided to demonstrate the superior performance of the proposed approach.

Figures from this paper

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TLDR
An edge detection algorithm based on Ant Colony Optimization and Fuzzy Inference System and neural network and the edges from the final pheromone matrix are extracted by using an intelligent thresholding technique provided by training a neural network.
An Enhanced Ant Colony Optimization Based Image Edge
TLDR
An improved ACO based algorithm for image edge detection has been presented and series of simulation experiments demonstrate the feasibility, effectiveness and superior performance of the proposed approach as compared to basic ACO.
A Novel Image Edge Detection Method Based on Multi-Population Ant Colony Optimization
  • Shu Wang
  • Computer Science
    2019 6th International Conference on Information Science and Control Engineering (ICISCE)
  • 2019
TLDR
A multiple-population strategy is utilized to enhance the accuracy of the ACO-based edge detection methods and the experimental results show the effectiveness of the proposed method.
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References

SHOWING 1-10 OF 24 REFERENCES
Edge detection using ant algorithms
TLDR
A new algorithm for edge detection using ant colony search is proposed, represented by a directed graph in which nodes are the pixels of an image, which suggests the effectiveness of the proposed algorithm.
Image Thresholding Using Ant Colony Optimization
TLDR
An approach where one ant is assigned to each pixel of an image and then moves around the image seeking low grayscale regions indicates that an ant-based approach has the potential of becoming an established image thresholding technique.
Edge detection improvement by ant colony optimization
Ant Colony Optimization for Image Regularization Based on a Nonstationary Markov Modeling
TLDR
It is shown that the algorithm applied here in a different way than the classical transposition of the graph color affectation problem outperforms the fixed-form neighborhood used in classical Markov random field regularization techniques.
Ant colony system with local search for Markov random field image segmentation
  • S. Ouadfel, M. Batouche
  • Computer Science
    Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)
  • 2003
TLDR
A new algorithm for image segmentation based on the Markov random field (MRF) and the ant colony optimization (AGO) metaheuristic and it competes with other global stochastic optimization methods like simulated annealing and genetic algorithm.
Hybrid ant colony algorithm for texture classification
We present a novel ant colony algorithm integrating genetic algorithms and simplex algorithms. This method is able to not only speed up searching process for optimal solutions, but also improve the
Special issue on ant colony optimization
TLDR
This special issue has the goal to collect papers on current, relevant work on ACO, and received 19 submissions on topics covering algorithmic developments, applications to continuous and combinatorial optimization problems, and theoretical studies.
Ant Colony Optimization: models and applications
TLDR
Ant Colony Optimization is a metaheuristic that is inspired by the shortest path searching behavior of various ant species that is used to solve a large number of hard combinatorial optimization problems such as the traveling salesman problem, the quadratic assignment problem, and routing in telecommunication networks.
Ant colony system: a cooperative learning approach to the traveling salesman problem
TLDR
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.
Classification With Ant Colony Optimization
TLDR
This paper provides an overview of previous ant-based approaches to the classification task and compares them with state-of-the-art classification techniques, such as C4.5, RIPPER, and support vector machines in a benchmark study, and proposes a new AntMiner+.
...
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