Comparison of Adaptive Ant Colony Optimization for Image Edge Detection of Leaves Bone Structure

Febri Liantoni, Rifki Indra Perwira, Daniel Silli Bataona


Leaf bone structure has a characteristic that can be used as a reference in digital image processing. One form of digital image processing is image edge detection. Edge detection is the process of extracting edge information from an image. In this research, Adaptive Ant Colony Optimization algorithm is proposed for edge image detection of leaf bone structure. The Adaptive Ant Colony Optimization method is a modification of Ant Colony Optimization, in which the initial an ant dissemination process is no longer random, but it is done by a pixel placement process that allows for an edge based on the value of the image gradient. As a comparison also performed edge detection using Robert and Sobel method. Based on the experiments performed, Adaptive Ant Colony Optimization algorithm is capable of producing more detailed image edge detection and has thicker borders than others.


Keywords: edge detection, ant colony optimization, robert, sobel

Full Text:



S. Wu, F. Bao, E. Xu, Y.-X. Wang, Y.-F. Chang, and C.-L. Shiang, “A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network,” in ISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology, 2007.

C. Kalyoncu and Ö. Toygar, “Geometric leaf classification,” Computer Vision and Image Understanding, vol. 133, pp. 102–109, Apr. 2015.

F. Liantoni, C. K. Kartika, and H. M. Tri, “Adaptive Ant Colony Optimization based Gradient for Edge Detection,” Journal of Computer Science, vol. 7, no. 2, pp. 78–84, 2014.

B. Anna and C. Oppus, “Image Edge Detection Using Ant Colony Optimization,” International Journal of Circuits, Systems and Signal Processing, vol. 4, no. 2, pp. 24–33, 2010.

R. Gonzales and R. Wood, Digital Image Processing. Addison Wesley, 1992.

S. Agarwal, “A Review Paper Of Edge Detection Using Ant Colony Optimization,” International Journal of Latest Research in Science and Technology, no. 1, pp. 120–123, 2012.

M. Dorigo, M. Birattari, and T. Stutzle, “Ant Colony Optimization: Artificial Ants as a Computational Intelligence Technique,” IEEE Computational Intelligence Magazine, 2006.

J. Tian, Y. Weiyu, and X. Shengli, “An Ant Colony Optimization Algorithm For Image Edge Detection,” IEEE Congress on Evolutionary Computation, pp. 751–756, 2008.

D.-S. Lu and C.-C. Chen, “Edge detection improvement by ant colony optimization,” Pattern Recognition Letters, vol. 29, no. 4, pp. 416–425, Mar. 2008.

F. Liantoni and L. Agus Hermanto, “Adaptive Ant Colony Optimization on Mango Classification Using K-Nearest Neighbor and Support Vector Machine,” Journal of Information Systems Engineering and Business Intelligence, vol. 3, p. 75, Oct. 2017.

S. Theodoridis and K. Koutroumbas, “Pattern Recognition,” in Pattern Recognition (Fourth Edition), Boston: Academic Press, 2009.

S. Theodoridis and K. Koutroumbas, “Pattern Recognition and Neural Networks,” in Machine Learning and Its Applications, 1999, pp. 169–195.

J. Ning, Q. Zhang, C. Zhang, and B. Zhang, “A best-path-updating information-guided ant colony optimization algorithm,” Information Sciences, vol. 433–434, pp. 142–162, Apr. 2018.

O. P. Verma, M. Hanmandlu, S. Kumar, and Dhruv, “A Novel Fuzzy Ant System For Edge Detection,” IEEE International Conference on Computer and Information Science, pp. 228–233, 2010.

DOI: 10.24003/emitter.v6i2.306


  • There are currently no refbacks.

Copyright (c) 2018 EMITTER International Journal of Engineering Technology

EMITTER Journal Editorial Office


Politeknik Elektronika Negeri Surabaya

Jl. Raya ITS - Kampus PENS Sukolilo Surabaya 60111, INDONESIA   Telp : +62 31 594 7280   Fax : +62 31 594 6114