Abstract
The aim of the image edge detection is to find the points, in a digital image, at which the brightness level changes sharply. Normally they are curved lines called edges. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction. Edge detection may lead to finding the boundaries of objects. It is one of the fundamental steps in image analysis. Edge detection is a hard computational problem. In this paper we apply a multiagent system. The idea comes from ant colony optimization. We use the swarm intelligence of the ants to search the image edges.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Baterina, A.V., Oppus, C.: Image edge detection using ant colony optimization. WSEAS Trans. Sig. Process. 6(8), 58–67 (2010)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)
Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 8(6), 679–697 (1986)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Fidanova, S., Atanasov, K.: Generalized net model for the process of hybrid ant colony optimization. C. R. l’Academie. Bulgare Sci. 62(3), 315–322 (2009)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall Inc., Upper Saddle River (2002)
Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall Inc, Upper Saddle River (1989)
Jevtic, A., Li, B.: Ant algorithm for adaptive edge detection. In: Abrao, T. (ed.) Search Algorithms for Engineering Optimization, Chapter 2, INTECH publisher (2013)
Mlsna, P.A., Rodriguez, J.J.: Gradient and laplacian-type edge detection. In: Bovik, A. (ed.) Handbook of Image and Video Processing, pp. 415–431. Academic Press, San Diego (2000)
Nezamabadi-pour, H., Saryazdi, S., Rashedi, E.: Edge detection using ant algorithm. Soft Comput. 10(7), 623–628 (2006)
Pratt, W.K.: Digital Image Processing, 2nd edn. Wiley, New York (1991)
Tian, J., Yu, W., Xie, S.: An ant colony optimization algorithm for image edge detection. In: IEEE Congress on Evolutionary Computation, pp. 751–756. Hong Kong (2008)
Zhou, P., Ye, W.Q., Wang, Q.: An improved canny algorithm for edge detection. J. Comput. Inf. Syst. 7(5), 1516–1523 (2011)
Zhang, Z., Ma, S., Liu, H., Gong, Y.: An edge detection approach based on directional wavelet transform. J. Comput. Math. Appl. 57(8), 1265–1271 (2009)
Acknowledgments
This work was supported by the Bulgarian National Scientific Fund under the grants DFNI 02/20 “Efficient Parallel Algorithms for Large Scale Computational Problems” and DFNI 02/5 “InterCriteria Analysis. A New Approach to Decision Making” and by EC grant AcomIn.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Fidanova, S., Ilcheva, Z. (2015). Application of Ants Ideas on Image Edge Detection. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2015. Lecture Notes in Computer Science(), vol 9374. Springer, Cham. https://doi.org/10.1007/978-3-319-26520-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-26520-9_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26519-3
Online ISBN: 978-3-319-26520-9
eBook Packages: Computer ScienceComputer Science (R0)