Anisotropic Diffusion and Curve Evolution for Segmentation of Color Images in Cultural Heritage

  • Luigi Cinque
  • Rossella Cossu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


We propose an innovative procedure for extracting decay regions from color images of stony materials. The use of appropriate image analysis techniques can offer an important contribution to be used together with the traditional methodologies for studying and diagnosing the decay of stony materials that constitute ancient monuments. The presented approach is constituted by the PDE (Partial Differential Equations) model of anisotropic diffusion and by the level set/fast marching method. The anisotropic diffusion is applied in order to limit the smoothing at the zones of high gradient. In the segmentation process, the numerical technique of the level set/fast marching is applied in order to extract from the image only the color region examined. The study case concerns impressive remains of the city of Aosta (Italy).


Color image segmentation anisotropic diffusion fast marching level set 


  1. 1.
    Cerimele, M.M., Cossu, R.: Decay Regions Segmentation from Color Images of Ancient Monuments Using Fast Marching Method. J. Cul. Her. 8, 170–175 (2007)CrossRefGoogle Scholar
  2. 2.
    Åström, F., Felsberg, M., Lenz, R.: Color Persistent Anisotropic Diffusion of Images. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 262–272. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Tschumperlé, D., Deriche, R.: Vector Valued Image Regularization with PDEs: A Common Framework for Different Applications. IEEE Transaction on Pattern Analysis and Machine Intelligence 27(4), 506–517 (2005)CrossRefGoogle Scholar
  4. 4.
    Sapiro, G.: Color Snakes. Computer Vision and Image Understanding 68(2), 247–253 (1997)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Sethian, J.: Level Set Methods and Fast Marching Methods. Cambridge University Press (1999)Google Scholar
  6. 6.
    Perona, P., Malik, J.: Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Transaction on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)CrossRefGoogle Scholar
  7. 7.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour Detection and Hierarchical Image Segmentation. IEEE TPAMI 33(5), 898–916 (2011)CrossRefGoogle Scholar
  8. 8.
    Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color Image Segmentation: Advances and Prospects. Pattern Recognition 34, 2259–2281 (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
    Wyszecki, G., Stiles, W.S.: Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd edn. John Wiley and Sons (1982)Google Scholar
  10. 10.
    Salonia, P., Bellucci, V., Scolastico, S., Marcolongo, A., Leti Messina, T.: 3D Survey Technologies for Reconstruction, Analysis and Diagnosis in the Conservation Process of Cultural Heritage. In: Atti del XXI CIPA International Symposium, Atene (2007)Google Scholar
  11. 11.
    Cerimele, M.M., Cossu, R.: A Numerical Modeling for the Extraction of Decay Regions from Color Images of Monuments. Mathematics and Computers in Simulation 79, 2334–2344 (2009)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Luigi Cinque
    • 1
  • Rossella Cossu
    • 2
  1. 1.Dipartimento InformaticaUnversitá degli Studi, SapienzaRomaItaly
  2. 2.Istituto per le Applicazioni del Calcolo-CNRRomaItaly

Personalised recommendations