Abstract
Preservation and restoration of ancient mosaics is a crucial activity for the perpetuation of cultural heritage of many countries. Such an activity is usually based on manual procedures which are typically lengthy and costly. Digital imaging technologies have a great potential in this important application domain, from a number of points of view including smaller costs and much broader functionalities. In this work, we propose a mosaic-oriented image segmentation algorithm aimed at identifying automatically the tiles composing a mosaic based solely on an image of the mosaic itself. Our proposal consists of a Genetic Algorithm, in which we represent each candidate segmentation with a set of quadrangles whose shapes and positions are modified during an evolutionary search based on multi-objective optimization. We evaluate our proposal in detail on a set of real mosaics which differ in age and style. The results are highly promising and in line with the current state-of-the-art.
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References
Benyoussef, L., Derrode, S.: Tessella-oriented segmentation and guidelines estimation of ancient mosaic images. J. Electron. Imaging 17(4), 043014 (2008)
Fenu, G., Jain, N., Medvet, E., Pellegrino, F.A., Namer, M.P.: On the assessment of segmentation methods for images of mosaics. In: Proceedings of 10th International Conference on Computer Vision Theory and Applications, Institute for Systems and Technologies of Information, Control and Communication (2015)
Man, K.F., Tang, K.S., Kwong, S.: Genetic Algorithms: Concepts and Designs. Springer Science & Business Media, Heidelberg (2012)
Goldberg, D.E.: Genetic Algorithms. Pearson Education India, Delhi (2006)
Brice, C.R., Fennema, C.L.: Scene analysis using regions. Artif. Intell. 1(3), 205–226 (1970)
Szeliski, R.: Computer Vision Algorithms and Applications. Springer, London (2011)
Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice Hall, Upper Saddle River (2003)
Mesejo, P., Ibánez, O., Cordón, O., Cagnoni, S.: A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis. Appl. Soft Comput. 44, 1–29 (2016)
Sethian, J.A.: Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, vol. 3. Cambridge University Press, Cambridge (1999)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)
Glover, F.W., Kochenberger, G.A.: Handbook of Metaheuristics, vol. 57. Springer Science & Business Media, Heidelberg (2006)
Maulik, U.: Medical image segmentation using genetic algorithms. IEEE Trans. Inf Technol. Biomed. 13(2), 166–173 (2009)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000). doi:10.1007/3-540-45356-3_83
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Bartoli, A., Fenu, G., Medvet, E., Pellegrino, F.A., Timeus, N. (2017). Segmentation of Mosaic Images Based on Deformable Models Using Genetic Algorithms. In: Gaggi, O., Manzoni, P., Palazzi, C., Bujari, A., Marquez-Barja, J. (eds) Smart Objects and Technologies for Social Good. GOODTECHS 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 195. Springer, Cham. https://doi.org/10.1007/978-3-319-61949-1_25
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DOI: https://doi.org/10.1007/978-3-319-61949-1_25
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