Circle detection on images using learning automata

Studies in Computational Intelligence

Volumn 427, Issue , 2013, Pages 545-570

  Cuevas, Erik ^a   Wario, Fernando ^a   Zaldivar, Daniel ^a   Pérez Cisneros, Marco ^a  

^a UNIVERSITY OF GUADALAJARA   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84867519243     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-29694-9_21     Document Type: Article

References (30)

1. Atherton, T.J., Kerbyson, D.J.: Using phase to represent radius in the coherent circle Hough transform. In: Proc. IEE Colloquium on the Hough Transform, IEE, London (1993)
2. Ayala-Ramirez, V., Garcia-Capulin, C.H., Perez-Garcia, A., Sanchez-Yanez, R.E.: Circle detection on images using genetic algorithms. Pattern Recognition Letters 27, 652-657 (2006)
3. Beygi, H., Meybodi, M.R.: A new action-set learning automaton for function optimization. Int. J. Franklin Inst. 343, 27-47 (2006)
4. Beigyab, H., Meybodibc, M.R.: A learning automata-based algorithm for determination of the number of hidden units for three-layer neural networks. International Journal of Systems Science 40(1), 101-118 (2009)
5. Chen, T.-C., Chung, K.-L.: An efficient Randomized Algorithm for detecting Circles. Computer Vision and Image Understanding 83, 172-191 (2001)
6. Cheng, H.D., Yanhui, G., Yingtao, Z.: A novel Hough transform based on eliminating particle swarm optimization and its applications. Pattern Recognition 42, 1959-1969 (2009)
7. Cuevas, E., Zaldivar, D., Perez-Cisneros, M.: Seeking multi-thresholds for image segmentation with Learning Automata. Machine Vision and Applications (2010), doi: 10.1007/s00138-010-0249-0
8. Dasgupta, S., Das, S., Biswas, A., Abraham, A.: Automatic circle detection on digital images with an adaptive bacterial foraging algorithm. Soft Computing 14, 1151-1164 (2010)
9. Da Fontoura Costa, L., Marcondes Cesar Jr., R.: Shape Analysis and Classification. CRC Press, Boca Raton (2001)
10. Han, J.H., Koczy, L.T., Poston, T.: Fuzzy Hough transform. In: Proc. 2nd Int. Conf. on Fuzzy Systems, vol. 2, pp. 803-808 (1993)
11. Howell, M., Gordon, T.: Continuous action reinforcement learning automata and their application to adaptive digital filter design. Engineering Applications of Artificial Intelligence 14, 549-561 (2001)
12. Ikonen, E., Najim, K.: Online optimization of replacement policies using learning automata. International Journal of Systems Science 39(3), 237-249 (2008)
13. Lu,W., Tan, J.L.: Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT). Pattern Recognition 41(4), 1268-1279 (2008)
14. Lutton, E., Martinez, P.: A genetic algorithm for the detection 2-D geometric primitives on images. In: Proc. of the 12th Int. Conf. on Pattern Recognition, vol. 1, pp. 526-528 (1994)
15. Muammar, H., Nixon, M.: Approaches to extending the Hough transform. In: Proc. Int. Conf. on Acoustics, Speech and Signal Processing ICASSP, vol. 3, pp. 1556-1559 (1989)
16. Najim, K., Poznyak, A.S.: Learning Automata-Theory and Applications. Pergamon Press, Oxford (1994)
17. Narendra, K.S., Thathachar, M.A.L.: Learning Automata: an Introduction. Prentice-Hall, London (1989)
18. Roth, G., Levine, M.D.: Geometric primitive extraction using a genetic algorithm. IEEE Trans. Pattern Anal. Machine Intell. 16(9), 901-905 (1994)
19. Seyed-Hamid, Z.: Learning automata based classifier. Pattern Recognition Letters 29, 40-48 (2008)
20. Shaked, D., Yaron, O., Kiryati, N.: Deriving stopping rules for the probabilistic Hough transform by sequential analysis. Comput. Vision Image Understanding 63, 512-526 (1996)
21. Thathachar, M.A.L., Sastry, P.S.: Varieties of learning automata: An overview. IEEE Trans. Systems. Man Cybernet. Part B: Cybernet 32, 711-722 (2002)
22. Tsetlin,M.L.: Automaton Theory andModeling of Biological Systems. Academic Press, New York (1973)
23. Turing, A.M.: On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society 42, 230-265 (1936)
24. Van-Aken, J.R.: Efficient ellipse-drawing algorithm. IEEE Comp, Graphics Applic. 4(9), 24-35 (1984)
25. Wu, Q.H.: Learning coordinated control of power systems using inter-connected learning automata. Int. J. Electr. Power Energy Syst. 17, 91-99 (1995)
26. Xu, L., Oja, E., Kultanen, P.: A new curve detection method: Randomized Hough transform (RHT). Pattern Recognition Lett. 11(5), 331-338 (1990)
27. Yao, J., Kharma, N., Grogono, P.: Fast robust GA-based ellipse detection. In: Proc. 17th Int. Conf. on Pattern Recognition, ICPR 2004, Cambridge, UK, pp. 859-862 (2004)
28. Yuen, S., Ma, C.: Genetic algorithm with competitive image labelling and least square. Pattern Recognition 33, 1949-1966 (2000)
29. Zeng, X., Zhou, J., Vasseur, C.: A strategy for controlling non-linear systems using a learning automaton. Automatica 36, 1517-1524 (2000)
30. Zeng, X., Liu, Z.: A learning automaton based algorithm for optimization of continuous complex function. Information Sciences 174, 165-175 (2005)