Abstract
Image recognition is a problem present in many real-world applications. In this paper we present an application of genetic algorithms (GAs) to solve one of those problems: the recovery of a deteriorated old document from the damages caused by centuries. This problem is particularly hard because these documents are affected by many aggresive agents, mainly by the humidity caused by a wrong storage during many years. This makes this problem unaffordable by other image processing techniques, but results show how GAs can succesfully solve this problem.
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References
Castleman, K. R.: Digital Image Processing. Prentice-Hall (1996)
Russ, J. C.: The Image Processing Handbook (third edition). CRC Press LLC (1999)
Roberts, L. G.: Machine Perception of Three-Dimensional Solidsin J.T. Tippett, ed., Optical and Electro-Optical Information Processing, MIT Press, Cambridge, MA, (1965) 159–197
Davis, L. S.: A Survey of Edge Detection Techniques. CGIP, 4:248–270. (1975)
Prewitt, J.: Object Enhacement and Extraction, in B. Lipkin and A. Rosenfeld, eds., Picture Processing and Psychopictorics, Academic Press, New York (1970)
Kirsch, R. A.: Computer Determination of the Constituent Structure of Biological Images, Computers in Biomedical Research, 4 (1971) 315–328
Holland, J. H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975.)
Goldberg, D. E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Reading, MA (1989)
Darwin, C.: On the Origin of Species by means of Natural Selection or the Preservation of Favoured Races in the Struggle for Life. Cambridge University Press, Cambridge, UK, sixth edition, (1864), originally published in 1859.
Suckley, D.: Genetic algorithm in the design of FIR filters, IEE Proceedings-G, vol. 138 (1991) 234–238
Nambiar, R., Tang, C.K.K., Mars, P.: Genetic and learning automata algorithms for adaptive digital filters, in Proc. ICASSP-92, vol. 4, New York, NY (1992) 41–44
Poli, R., Cagnoni, S., Valli, G.: Genetic design of optimum linear and non-linear QRS detectors, IEEE Trans. On Biomed. Engineering, vol. 42, no. 11 (1995) 1137–1141
Bounsaythip, C., Alander, J.T.: Genetic Algorithms in Image Processing-A Review, Proc. Of the 3rd Nordic Workshop on Genetic Algorithms and their Applications, Metsatalo, Univ. Of Helsinki, Helsinki, Finland, (1997) 173–192
Howard, D., Roberts, S. C.: A Staged Genetic Programming Strategy for Image Analysis, Proceedings of the Genetic and Evolutionary Computation Conference. Vol. 2. (1999) 1047–1052
Howard, D., Roberts, S. C.: The Boru Data Crawler for Object Detection Tasks in Machine Vision, Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN, (2002) 222–232
Ramos, V., Muge, F.: Image Colour Segmentation by Genetic Algorithms (2000)
Ramos, V.: The Biological Concept of Neoteny in Evolutionary Computation-Simple Experiments in Simple Non-Memetic Genetic Algorithms (2001)
Cagnoni, S., Dobrzeniecki, A.B., Poli, R., Yanch, J.C.: Genetic Algorithm-based Interactive Segmentation of 3D Medical Images, Image and Vision Computing 17 (1999) 881–895
Bhanu, B., Lee, S.: Genetic Learning for Adaptive Segmentation, Kluwer Academic Press (1994)
Bhanu, B., Lee, S., Ming, J.: Adaptive Image Segmentation using a Genetic Algorithm, IEEE Transactions on Systems, Man, and Cybernetics 25(12), pp. 1543–1567. (1995)
Hwang, W., Chang, H.: Character Extraction from Documents using Wavelet Maxima, Image and Vision Computing. Volume 16, Issue 5 (1998) 307–315
Negishi, H., Kato, J., Hase, H., Watanabe, T.: Character Extraction from Noisy Background for an Automatic Reference System, Proceedings of the Fifth International Conference on Document Analysis and Recognition, Bangalore, India, 20–22 September(1999)
Vidal, R.: Old Text Reconstruction: An Artificial Intelligence Approach, Graduate Thesis, Facultad de Informática, Universidade da Coruña (1999)
Poli, R., Langdon, W.B.: Sub-machine-code Genetic Programming. In L. Spector, U.M. O’Reilly W.B. Langdon and P.J. Angeline, editors, Advances in Genetic Programming 3, MIT Press, chapter 13 (1999) 301–323
Adorni, G., Cagnoni, S.: Design of explicitly or implicitly parallel low-resolution character recognition algorithms by means of genetic programming, in Roy, R., Koppen, M., Ovaska, S., Furuhashi, T., Hoffmann, F. (eds.), Soft Computing and Industry: Recent Applications, (Proc. 6th Online Conference on Soft Computing). Springer (2002) 387–398
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Rivero, D., Vidal, R., Dorado, J., Rabuñal, J.R., Pazos, A. (2003). Restoration of Old Documents with Genetic Algorithms. In: Cagnoni, S., et al. Applications of Evolutionary Computing. EvoWorkshops 2003. Lecture Notes in Computer Science, vol 2611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36605-9_40
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DOI: https://doi.org/10.1007/3-540-36605-9_40
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