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Research papers

Circle detection on images based on the Clonal Selection Algorithm (CSA)

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Pages 34-44 | Received 19 Feb 2012, Accepted 13 Jun 2014, Published online: 27 Jun 2014

References

  • Brabazon A and O’Neill M. Biologically Inspired Algorithms for financial Modelling, 2006 (Springer, Berlin).
  • Chih-Chih L. A novel image segmentation approach based on particle swarm optimization. IEICE Trans. Fundam. Electron. Commun. Comput. Sci., 2006, 89, 324–327.
  • Le Hégarat-Mascle S, Hégarat-Mascle L, Kallel A and Descombes X. Ant colony optimization for image regularization based on a nonstationary markov modeling. IEEE Trans. Image Process., 2007, 16, 865–878.
  • Hammouche K, Diaf M and Siarry P. Amultilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation. Comput. Vis. Image Understand., 2008, 109, 163–175.
  • Baştürk A and Günay E. Efficient edge detection in digital images using a cellular neural network optimized by differential evolution algorithm. Expert Syst. Appl., 2009, 36, 2645–2650.
  • da Fontoura Costa L and Marcondes Cesar R Jr. Shape Analysis and Classification, 2001 (CRC Press, Boca Raton, FL).
  • Peura M and Iivarinen J. In Advances in Visual Form Analysis (Ed. Arcelli C, Cordella L P and di Baja G S), 1997, pp.443–451 (World Scientific, Singapore).
  • Yuen H, Princen J, Illingworth J and Kittler J. Comparative study of Hough transform methods for circle finding. Image Vis. Comput., 1990, 8, 71–77.
  • Iivarinen J, Peura M, Sarela J and Visa A. Comparison of combined shape descriptors for irregular objects, Proc. 8th British Machine Vision Conf., Cochester, UK, September 1997, University of Essex, pp. 430–439.
  • Jones G, Princen J, Illingworth J and Kittler J. Robust estimation of shape parameters, Proc. British Machine Vision Conf., Oxford, UK, September 1990, BMVA, pp. 43–48.
  • Fischer M and Bolles R. Random sample consensus: a paradigm to model fitting with applications to image analysis and automated cartography. Commun. ACM, 1981, 24, 381–395.
  • Bongiovanni G and Crescenzi P. Parallel simulated annealing for shape detection. Comput. Vis. Image Understand., 1995, 61, 60–69.
  • Roth G and Levine MD. Geometric primitive extraction using a genetic algorithm. IEEE Trans. Pattern Anal. Mach. Intell., 1994, 16, 901–905.
  • Muammar H and Nixon M. Approaches to extending the Hough transform, Proc. Int. Conf. on Acoustics, speech and signal processing: ICASSP-89, Glasgow, UK, May 1989, IEEE, Vol. 3, pp. 1556–1559.
  • Atherton TJ and Kerbyson DJ. Using phase to represent radius in the coherent circle Hough transform, Proc. IEE Coll. on the Hough transform, London, UK, May 1993, IEE. 269–278.
  • Shaked D, Yaron O and Kiryati N. Deriving stopping rules for the probabilistic Hough transform by sequential analysis. Comput. Vis. Image Understand., 1996, 63, 512–526.
  • Fischer M and Bolles R. Random sample consensus: a paradigm to model fitting with applications to image analysis and automated cartography. Commun. ACM, 1981, 24, 381–395.
  • Xu L, Oja E and Kultanen P. A new curve detection method: randomized Hough transform (RHT). Pattern Recogn. Lett., 1990, 11, 331–338.
  • Han JH, Koczy LT and Poston T. Fuzzy Hough transform, Proc. 2nd IEEE Int. Conf. on Fuzzy systems, San Francisco, CA, USA, March 1993, IEEE, Vol. 2, pp. 803–808.
  • Lu W and Tan JL. Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT). Pattern Recogn., 2008, 41, 1268–1279.
  • Roth G and Levine MD. Geometric primitive extraction using a genetic algorithm. IEEE Trans. Pattern Anal. Mach. Intell., 1994, 16, 901–905.
  • Lutton E and Martinez P. A genetic algorithm for the detection 2-D geometric primitives on images, Proc. of the 12th Int. Conf. on Pattern recognition, Jerusalem, Israel, October 1994, IEEE, Vol. 1, pp. 526–528.
  • Yao J, Kharma N and Grogono P. Fast robust GA-based ellipse detection, Proc. 17th Int. Conf. on Pattern recognition: ICPR-04, Cambridge, UK, August 2004, IEEE, Vol. 2, pp. 859–862.
  • Yuen S and Ma C. Genetic algorithm with competitive image labelling and least square. Pattern Recogn., 2000, 33, 1949–1966.
  • Ayala-Ramirez V, Garcia-Capulin CH, Perez-Garcia A and Sanchez-Yanez RE. Circle detection on images using genetic algorithms. Pattern Recogn. Lett., 2006, 27, 652–657.
  • Dasgupta S, Das S, Biswas A and Abraham A. Automatic circle detection on digital images whit an adaptive bacterial foraging algorithm. Soft Comput., 2009, 14, 1151–1164.
  • Rosin PL. Further five point fit ellipse fitting, Proc. 8th British Machine Vision Conf., Cochester, UK, September 1997, University of Essex, pp.290–299.
  • Goldsby GA, Kindt TJ, Kuby J and Osborne BA. Immunology, 2003, 5th edition (Freeman, New York).
  • de Castro LN and Timmis J. Artificial Immune Systems: A New Computational Intelligence Approach, 2002 (Springer, London).
  • Dasgupta D. Advances in artificial immune systems. IEEE Comput. Intell. Mag., 2006, 1, 40–49.
  • Wang X, Gao XZ and Ovaska SJ. Artificial immune optimization methods and applications — a survey, Proc. IEEE Int. Conf. on Systems, man, and cybernetics, The Hague, The Netherlands, October 2004, IEEE, pp.3415–3420.
  • de Castro LN and von Zuben FJ. Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput., 2002, 6, 239–251.
  • Ada GL and Nossal G. The clonal selection theory. Sci. Am., 1987, 257, 50–57.
  • Ma HP, Simon D, Fei MR and Chen ZX. On the equivalences and differences of evolutionary algorithms. Eng. Appl. Artif. Intell., 2013, 26, 2397–2407.
  • Eberhart RC and Shi YH. Comparison between genetic algorithms and particle swarm optimization. Lect. Notes Comput. Sci., 1998, 1447, 611–616.
  • Biswas A, Das S, Abraham A and Dasgupta S. Stability analysis of the reproduction operator in bacterial foraging optimization. Theor. Comput. Sci., 2010, 411, 2127–2139.
  • Ülker ED and Ülker S. Comparisons study for clonal selection algorithm and genetic algorithm. Int. J. Comput. Sci. Inf. Technol., 2012, 4, 107–118.
  • Sheng Q, Zhou LS and Yue YX. A clonal selection based differential evolution algorithm for double-track railway train schedule optimization, Proc. 2nd IEEE Int. Conf. on Advanced computer control: ICACC 2010, Shenyang, China, January 2010, IEEE, 155–158.
  • Yap DFW, Koh SP and Tiong SK. A comparative analysis on the performance of particle swarm optimization and artificial immune systems for mathematical test functions. Aust. J. Basic Appl. Sci., 2009, 3, 4344–4350.
  • Coello Coello CA and Cortes NC. Solving multiobjective optimization problems using an artificial immune system. Genet. Program. Evolv. Mach., 2005, 6, 163–190.
  • Campelo F, Guimaraes FG, Igarashi H and Ramirez JA. A clonal selection algorithm for optimization in electromagnetics. IEEE Trans. Magn., 2005, 41, 1736–1739.
  • Dong WS, Shi GM and Li Z. Immune memory clonal selection algorithms for designing stack filters. Neurocomputing 2007, 70, 777–784.
  • Gong M, Jiao L, Zhang L and Du H. Immune secondary response and clonal selection inspired optimizers. Prog. Nat. Sci., 2009, 19, 237–253.
  • de Castro L, N and von Zuben FJ. Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput., 2002, 6, 239–251.
  • Cutello V, Narzisi G, Nicosia G and Pavone M. Clonal Selection Algorithms: a comparative case study using effective mutation potentials. Lect. Notes Comput. Sci., 2005, 3627, 13–28.
  • Gong M, Jiao L and Zhang X. A population-based artificial immune system for numerical optimization. Neurocomputing, 2008, 72, 149–161.
  • Gao X, Wang X and Ovaska S. Fusion of clonal selection algorithm and differential evolution method in training cascade–correlation neural network. Neurocomputing, 2009, 72, 2483–2490.
  • Poli R and Langdon WB. Foundations of Genetic Programming, 2002 (Springer, Berlin).
  • Yoo J and Hajela P. Immune network simulations in multicriterion design. Struct. Optimiz., 1999, 18, 85–94.
  • Wang X, Gao XZ and Ovaska SJ. A hybrid optimization algorithm in power filter design, Proc. 31st Annual Conf. of the IEEE Industrial Electronics Society, Raleigh, NC, USA, November 2005, IEEE Industrial Electronics Society, pp.1335–1340.
  • Xu X and Zhang J. An improved immune evolutionary algorithm for multimodal function optimization, Proc. 3rd Int. Conf. on Natural computation, Haikou, China, August 2007, IEEE, pp.641–646.
  • Tang T and Qiu J. An improved multimodal artificial immune algorithm and its convergence analysis, Proc. 6th World Cong. on Intelligent control and automation, Dalian, China, June 2006, IEEE, pp.3335–3339.
  • Bresenham JE. A linear algorithm for incremental digital display of circular arcs. Commun. ACM, 1987, 20, 100–106.
  • van Aken JR. An efficient ellipse drawing algorithm. Comput. Graph. Appl., 1984, 4, 24–35.
  • Chen T.-C and Chung K.-L. An efficient randomized algorithm for detecting circles. Comput. Vis. Image Understand., 2001, 83, 172–191.

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