Abstract
Ant colony optimization is a metaheuristic where a colony of artificial ants cooperate to find good solutions to different optimization problems. Edge detection plays an important role in image processing. It consists in detecting edges or contours in images that allow to extract relevant information. Here, an algorithm based on the ACO metaheuristic for edge detection is proposed. Using heuristic and knowledge information in the construction phase and a repair operator in the improvement phase, a binary image containing detected edges is reached. Our proposal was tested with several images in and without presence of noise. Results are competitive in terms of output images, effectiveness and CPU time.
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Ahmad M, Choi T (1999) Local threshold and Boolean function based edge detection. IEEE Trans Consumer Electr 45(3):674–679
Angelov P, Sadeghi-Tehran P, Ramezani R (2011) An approach to automatic real-time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving Takagi-Sugeno Systems. Int J Intell Syst 26(3):189–205
Arbelaez P, Maire M, Fowlkes Ch, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916
Baterina A, Oppus C (2010) Image edge detection using ant colony optimization. WSEAS Trans Signal Proc 6(2):58–67
Bowyer K, Kranenburg C, Dougherty S (1999) Edge detector evaluation using empirical ROC curves. In: Computer vision and pattern recognition, Colorado, USA
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698
Djemame S, Batouche M (2012) Combining cellular automata and particle swarm optimization for edge detection. Int J Comput Appl 57(14):16–22
Dorigo M, Stützle T (2004) Ant colony optimization, 1st edn. MIT Press, London
Etemad S, White T (2011) An ant-inspired algorithm for detection of image edge features. Appl Soft Comput 11:4883–4893
González R, Woods R (2008) Digital image processing, 3rd edn. Prentice-Hall, New Jersey
Hoang N, Nguyen Q (2018) Metaheuristic optimized edge detection for recognition of concrete wall cracks: a comparative study on the performance of Roberts, Prewitt, Canny, and Sobel Algorithms. Adv Civ Eng 2018:1–16
Jevtić A, Andina D (2010) Adaptive artificial ant colonies for edge detection in digital images. In: 36th annual conference on IEEE Industrial Electronics Society, Glendale, USA
Lakhani K, Minocha B, Gugnani N (2016) Analyzing edge detection techniques for feature extraction in dental radiographs. Perspect Sci 8:395–398
Liantoni F, Kirana K, Muliawati T (2014) Adaptive ant colony optimization based gradient for edge detection. J Comput Sci Inf 7(2):76–82
Li Y, Bai B, Zhang Y (2007) An adaptive inmune genetic algorithm for edge detection. In: Proceedings of the 3rd international conference on intelligent computing, Qindao, China
Liu X, Fang S (2015) A convenient and robust edge detection method based on ant colony optimization. Opt Commun 353:147–157
Martin D, Fowlkes C, Malik J (2004) Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans Pattern Anal Mach Intell 26(5):530–549
Martínez C, Buemi M (2014) ACO algorithm for image edge detection. In: Proceedings of the 6th Chilean conference on pattern recognition, Talca, Chile
Nadernejad E, Sharifzadeh S (2008) Edge detection techniques: evaluations and comparisons. Appl Math Sci 2(31):1507–1520
Neupane B, Aung Z, Woon W (2012) A new image edge detection method using quality-based clustering. In: Proceedings of the 10th international conference on visualization, imaging and image processing, Banff, Canada
Thirumavalavan S, Jayaraman S (2016) An improved teaching–learning based robust edge detection algorithm for noisy images. J Adv Res 7:979–989
Tian J, Yu W, Xie S (2008) An ACO algorithm for image edge detection. In: Proc. of the IEEE congress on evolutionary computation, Hong Kong, China
Yigitbasi E, Baykan N (2013) Edge detection using artificial bee colony algorithm. Int J Inf Electr Eng 3(6):634–638
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Martínez, C.A., Buemi, M.E. Hybrid ACO algorithm for edge detection. Evolving Systems 12, 849–860 (2021). https://doi.org/10.1007/s12530-019-09321-5
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DOI: https://doi.org/10.1007/s12530-019-09321-5