Welcome to Open Science
Contact Us
Home Books Journals Submission Open Science Join Us News Unsubscribe Page
Object Recognition Using Artificial Fish Swarm Algorithm on Fourier Descriptors
Current Issue
Volume 2, 2015
Issue 5 (September)
Pages: 105-110   |   Vol. 2, No. 5, September 2015   |   Follow on         
Paper in PDF Downloads: 90   Since Sep. 19, 2015 Views: 851   Since Sep. 19, 2015
Hamid Ali Abed AL-Asadi, Computers Sciences Department, Education for Pure Science College, University of Basra, Basra, Iraq.
Majida Ali Abed, College of Computers Sciences & Mathematics, University of Tikrit, Tikrit, Iraq.
In this paper, we present an Artificial Fish Swarm Algorithm is a class of an evolutionary optimization technique with three types of classifier combinations using different geometrics’ shape for the recognition of the plant leaves. Fish Swarm Algorithm is applied on Fourier descriptors to get optimum weights that maximize the recognition rate. Fourier descriptors are invariant to rotation, translation or scaling. These optimum Fourier descriptors are then used in process of recognition. The obtained results achieve a recognition rate of 98.75% for Log of Euclidean Distance classifier. Results show that our proposed system advances object recognition with highly effective.
Object Recognition, Artificial Fish Swarm Algorithm (AFSA), Fourier Transform and Classifier
P. Azad, T. Gockel, and R. Dillmann. Computer Vision - Principles and Practice. Elektor International Media BV, 2008.
D. A. Forsyth and J. Ponce. Computer Vision: A Modern Approach. Prentice Hall, US ed edition, August 2002.
S. M. Smith and J. M. Brady. Susan—a new approach to low level image processing. Int. J. Comput. Vision, 23(1):45–78, 1997.
SERBY, D., KOLLER-MEIER, S., AND GOOL, L. V. 2004. Probabilistic object tracking using multiple features. In IEEE International Conference of Pattern Recognition (ICPR). 184–187.
P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, "Overview of the Face Recognition Grand Challenge," Proc. Computer Vision and Pattern Recognition Conference, San Diego, 2005.
BREGLER, C., HERTZMANN, A., AND BIERMANN, H. 2000. Recovering nonrigid 3d shape from image streams. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 690–696.
LIYUAN, L. AND MAYLOR, L. 2002. Integrating intensity and texture differences for robust change detection. IEEE Trans. Image Process. 11, 2, 105–112.
Hutter, Marcus (2005). Universal Artificial Intelligence. Berlin: Springer. ISBN 978-3-540-22139-5.
Luger, George; Stubblefield, William (2004). Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th ed.). Benjamin/Cummings. ISBN 0-8053-4780-1.
Zhuang X., Mastorakis N. E., "Image processing with the artificial swarm intelligence", in WSEAS Transactions on Computers, Issue 4, Vol.4, pp. 333-341, ISSN 1109-2750, April 2005.
Bonabeau, E., Dorigo, M., Theraulaz, G., Swarm intelligence: from natural to artificial systems, Santa Fe Institute in the Sciences of Complexity, Oxford Univ. Press, Oxford, 1999.
H. A. Perlin, H. S. Lopes, and T. M. Centeno, Particle Swarm Optimization for Object Recognition in Computer Vision," New Frontiers in Applied Arti_cial Intelligence 5027, 11 (2008).
Karaboga, Dervis (2010). "Artificial bee colony algorithm". Scholarpedia 5 (3): 6915.
Srinivas. M and Patnaik. L, "Adaptive probabilities of crossover and mutation in genetic algorithms," IEEE Transactions on System, Man and Cybernetics, vol.24, no.4, pp.656–667, 1994.
Ramos V., Almeida F.: Artificial Ant Colonies in Digital Image Habitats - A Mass Behaviour Effect Study on Pattern Recognition. In: Proc. ANTS 2000 - 2nd Int. Works. on Ant Algorithms (From Ant Colonies to Artificial Ants), 2000, pp. 113-124.
Hamid Ali Abed AL-Asadi, Hussein Ali Al_Iedane and Nadra J. Ali AL-Saad, “Object recognition in Image using Hybrid (DRA-CSO) Architecture”, International Journal of Engineering Research & Technology (IJERT), Vol. 4 Issue 02, 2015.
ZHANG D S, LU G J. Shape-based image retrieval using generic Fourier descriptor [J]. Signal Processing: Image Communication, 2002, 17(10): 825−848.
Kunyyui I, Lepisto L, Rauhamaa J, Visa A. Multiscale Fourier descriptors for defect image retrieval [J]. Pattern Recognition Letters, 2006, 27(2): 123−132.
R. M. Haralick and L. G. Shapiro. Computer and Robot Vision. Addision-Wesley Longman Publishing, 1992.
D. Lowe (2004). "Distinctive Image Features from Scale-Invariant Keypoints". International Journal of Computer Vision 60 (2): 91.
Chen, S.: A new vision system and the Fourier descriptors method by group representations. In: IEEE CDC Conference, Las Vegas, USA (1985).
Jiang M, Zhu K. Multiobjective Optimization by Artificial Fish Swarm Algorithm. IEEE Transactions. 2011.
Fran, S.L.; Valder, S., Jr. Fish Swarm Optimization Algorithm Applied to Engineering system design. Lat. Am. J. Solids Struct. 2014, 11, 143–156.
Open Science Scholarly Journals
Open Science is a peer-reviewed platform, the journals of which cover a wide range of academic disciplines and serve the world's research and scholarly communities. Upon acceptance, Open Science Journals will be immediately and permanently free for everyone to read and download.
Office Address:
228 Park Ave., S#45956, New York, NY 10003
Phone: +(001)(347)535 0661
Copyright © 2013-, Open Science Publishers - All Rights Reserved