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An Automated Methodology for Assessing the Damage on Byzantine Icons

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Progress in Cultural Heritage Preservation (EuroMed 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7616))

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Abstract

Byzantine art is overwhelmed by a multitude of icons that portray sacred faces. However, a large number of icons of historical value are either partially or totally damaged and thus in need of undergoing conservation. The detection and assessment of damage in cultural heritage artifacts comprise an integral part of the conservation process. In this paper, a method that can be used for assessing the damage on faces appearing in Byzantine icons is presented. The main approach involves the estimation of the residuals obtained after the coding and reconstruction of face image regions using trained Principal Component Analysis texture models. The extracted residuals can be used as the basis for obtaining information about the amount of damage and the positions of the damaged regions. Due to the specific nature of Byzantine icons several variations of the basic approach are tested through a quantitative experimental evaluation so that the methods most suited to the specific application domain are identified. As part of the experimental evaluation, holistic as well as patch-decomposition techniques have been utilized in order to catch the global and local information of the images, respectively. According to the results it is possible to detect and localize with reasonable accuracy the damaged areas of faces appearing in Byzantine icons.

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References

  1. Spagnolo, G.S., Somma, F.: Virtual Restoration of Cracks in Digitized Image of Paintings. Journal of Physics Conference Series 249(1) (2010)

    Google Scholar 

  2. Petzet, M.: Principles of conservation: An introduction to the International Charters for Conservation and Restoration 40 years after the Venice Charter, Monuments and Sites. Int. Charters for Conservation and Restoration I, 7–29 (2004)

    Google Scholar 

  3. Lanitis, A., Stylianou, G., Voutounos, C.: Virtual Restoration of Faces Appearing in Byzantine Icons. International Journal of Cultural Heritage (2012), doi:10.1016/j.culher.2012.01.001

    Google Scholar 

  4. Wu, C., Liu, C., Shum, H.-Y., Xy, Y.-Q., Zhang, Z.: Automatic Eyeglasses Removal from Face Images. IEEE Trans. on Pattern Analysis and Machine Intelligence 26, 322–336 (2004)

    Article  Google Scholar 

  5. Park, J.-S., Oh, Y., Ahn, S., Lee, S.-W.: Glasses Removal from Facial Image Using Recursive PCA Reconstruction. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 369–376. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  6. Wang, Z.M., Tao, J.H.: Reconstruction of Partially Occluded Face by Fast Recursive PCA. In: Int. Conference on Computational Intelligence and Security Workshops, Harbin, December 15-19 (2007)

    Google Scholar 

  7. Hwang, B.W., Lee, S.W.: Reconstruction of Partially Damaged Face Images Based on a Morphable Face Model. IEEE Trans. on Pattern Analysis and Machine Intelligence 25, 365–372 (2003)

    Article  Google Scholar 

  8. Smet, M.D., Fransens, R., Gool, L.V.: A Generalized EM Approach for 3D Model Based Face Recognition under Occlusions. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1423–1430 (2006)

    Google Scholar 

  9. Colombo, A., Cusano, C., Schettini, R.: Three-Dimensional Occlusion Detection and Restoration of Partially Occluded Faces. J. Math. Imaging Vis. 40, 105–119 (2011)

    Article  MathSciNet  Google Scholar 

  10. Martinez, A.M.: Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class. IEEE Trans. on Pattern Analysis and Machine Intelligence 24, 748–763 (2002)

    Article  Google Scholar 

  11. Kumar, G.S., Reddy, P., Swamy, M.S., Gupta, S.: Skin based Occlusion Detection and Face Recognition using Machine Learning Techniques. International Journal of Computer Applications 41, 11–15 (2012)

    Article  Google Scholar 

  12. Kim, G., Suhr, J.K., Jung, H.G., Kim, J.: Face Occlusion Detection by using B-spline Active Contour and Skin Color Information. In: 11th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 627–632 (2010)

    Google Scholar 

  13. Lanitis, A.: Person Identification From Heavily Occluded Face Images. In: Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 5–9 (2004)

    Google Scholar 

  14. Venkat, I., Khader, A.T., Subramanian, K.G., De Wilde, P.: Recognizing occluded faces by exploiting psychophysically inspired similarity maps. Pattern Recognition Letters (2012), doi: http://dx.doi.org/10.1016/j.patrec.2012.05.003

  15. Yang, M., Zhang, L.: Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 448–461. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  16. Edwards, G.J., Lanitis, A., Taylor, C.J., Cootes, T.F.: Statistical Face Models: Improving Specificity. Image and Vision Computing 16(3), 203–211 (1998)

    Article  Google Scholar 

  17. Lanitis, A., Stylianou, G.: Reconstructing 3D faces in Cultural Heritance Applications. In: 14th International Conference on Virtual Systems and Multimedia, Limassol, October 20-25 (2008)

    Google Scholar 

  18. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

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Maronidis, A., Lanitis, A. (2012). An Automated Methodology for Assessing the Damage on Byzantine Icons. In: Ioannides, M., Fritsch, D., Leissner, J., Davies, R., Remondino, F., Caffo, R. (eds) Progress in Cultural Heritage Preservation. EuroMed 2012. Lecture Notes in Computer Science, vol 7616. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34234-9_32

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  • DOI: https://doi.org/10.1007/978-3-642-34234-9_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34233-2

  • Online ISBN: 978-3-642-34234-9

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