Skip to main content
SpringerLink
Log in

Designing and evaluating an expert system for restoring damaged byzantine icons

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The use of image processing techniques in cultural heritage applications has been gaining increasing interest in the research community. In this paper, an integrated framework that can be used for virtual restoration of the facial region of damaged Byzantine icons is presented. A key aspect of the proposed methodology is the integration of practices adopted by expert icon restorers into a machine-based expert system that incorporates the modules of damage detection, shape and texture restoration. Damage detection is performed based on a residual-based approach, while the shape restoration method utilizes a 3D shape model generated by incorporating a set of geometrical rules defined by expert Byzantine style iconographers. Texture restoration is based on the recursive Principal Component Analysis (PCA) technique so that combinations of colors learned from a training set are applied to the damaged icon regions. All modules, developed as part of this framework, are incorporated into a user-friendly application that can be used by amateurs or professional Byzantine icon restorers and conservators. The potential of the developed tool has been validated through a quantitative experimental process and a user-based evaluation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Anagnostopoulos CN, Anagnostopoulos I, Maglogiannis I, Vergados D (2007) Portrait identification in digitized paintings on the basis of a face detection system. Frontiers in Artificial Intelligence and Applications 160:351–360

    Google Scholar 

  2. Barni M, Bartolini F, Cappellini V (2000) Image processing for virtual restoration of artworks. IEEE Multimedia 7:34–37

    Article  Google Scholar 

  3. Blanz V, Vetter T (2003) Face recognition based on fitting 3D morphable model. IEEE Transactions on Pattern Analysis And Machine Intelligence 25:1063–1074

    Article  Google Scholar 

  4. Bruni V, Ramponi G, Restrepo A, Vitulano D (2009) Context-based defading of archive photographs. EURASIP Journal on Image and Video Processing, Article ID 986183

  5. Clapham C, Nicholson J (2009) The concise oxford dictionary of mathematics. Oxford University Press

  6. Colombo A, Cusano C, Schettini R (2011) Three-dimensional occlusion detection and restoration of partially occluded faces. Journal of Mathematical Imaging and Vision 40:105–119

    Article  MathSciNet  MATH  Google Scholar 

  7. Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23(6):681–685

    Article  Google Scholar 

  8. Criminisi A, Perez P, Toyama K (2003) Object removal by exemplar-based inpainting. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 417–424

  9. Del Mastio A, Cappellini V, Caldelli R, De Rosa A, Piva A (2007) Virtual restoration and protection of cultural heritage images. 15th International conference on digital signal processing. 471–474

  10. Drago F, Chiba N (2005) Locally adaptive chromatic restoration of digitally acquired paintings. International Journal of Image and Graphics 5:617–637

    Article  Google Scholar 

  11. Edwards GJ, Lanitis A, Taylor CJ, Cootes TF (1998) Statistical face models: improving specificity. Image and Vision Computing 16(3):203–211

    Article  Google Scholar 

  12. Falcao G, Hurtos N, Massich J (2008) Plane-based calibration of a projector-camera system. VIBOT, Tech. Rep

  13. Giakoumis I, Nikolaidis N, Pitas I (2006) Digital image processing techniques for the detection and removal of cracks in digitized paintings. IEEE Trans Image Process 15:178–188

    Article  Google Scholar 

  14. Hays J, Efros AA (2007) Scene completion using millions of photographs. SIGGRAPH, ACM Transactions on Graphics, 26

  15. Hwang BW, Lee SW (2003) Reconstruction of partially damaged face images based on a morphable face model. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 25:365–372

    Article  Google Scholar 

  16. Jayne C, Lanitis A, Christodoulou C (2012) One-to-many neural network mapping techniques for face image synthesis. Expert Systems with Applications 39(10):9778–9787

    Article  Google Scholar 

  17. Kammerer P (2000) Pose estimation and comparison of painted portraits using a 3D head model. Czech pattern recognition workshop, 2–4

  18. Kim G, Suhr JK, Jung HG, Kim J (2010) Face occlusion detection by using B-spline active contour and skin color information. 11th International Conference on Control, Automation, Robotics and Vision, (ICARCV), 627–632

  19. Kumar GS, Reddy P, Swamy MS, Gupta S (2012) Skin based occlusion detection and face recognition using machine learning techniques. Intl Journal of Computer Applications 41:11–15

    Google Scholar 

  20. Landon GV, Clarke D, Seales WB (2009) A new technique for the digitization and restoration of deteriorated photographic negatives. EURASIP Journal on Image and Video Processing, Article ID 217016

  21. Lanitis A (2004) Person Identification From Heavily Occluded Face Images. Proceedings of the 2004 ACM symposium on Applied computing, 5–9

  22. Lanitis A (2008) comparative evaluation of automatic age progression methodologies. EURASIP Journal on Advances in Signal Processing, Article ID 239480 (10 pages)

  23. Lanitis A, Stylianou G (2008) Reconstructing 3D faces in cultural heritance applications. 14th International conference on virtual systems and multimedia

  24. Lanitis A, Stylianou G, Voutounos C (2012) Virtual restoration of faces appearing in byzantine icons. International Journal of Cultural Heritage, Elsevier. doi:10.1016/j.culher.2012.01.001

    Google Scholar 

  25. Maronidis A, Lanitis A (2012) An Automated methodology for assessing the damage on Byzantine icons. M. Ioannides et al. (Eds.): International conference on cultural heritage EuroMed 2012, LNCS 7616, pp. 320–329

  26. Maronidis A, Voutounos C, Lanitis A (2013) An Integrated tool for virtual restoration of Byzantine icons. Procs. of the 4th International conference on information, intelligence, systems and applications

  27. Martinez AM (2002) 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

    Article  Google Scholar 

  28. Papantoniou G, Loizides F, Lanitis A, Michaelides D (2012) Digitization, restoration and visualization of terracotta figurines from the ‘House of Orpheus’, Nea Paphos, Cyprus. M. Ioannides et al. (Eds.): International Conference on Cultural Heritage EuroMed 2012, LNCS 7616, pp. 543–550

  29. Park JS, Oh Y, Ahn S, Lee SW (2003) Glasses removal from facial image using recursive PCA reconstruction. Lecture Notes in Computer Science (LNCS) 2688:369–376

    Article  MATH  Google Scholar 

  30. Raftopoulos K, Tzouveli P, Ntalianis K, Kollias S, Kalomoirakis D, Fyssas N, et al. (2008) Saint classification in Byzantine art. 14th International conference on virtual systems and multimedia

  31. Sablatnig R, Kammerer P, Zolda E (1998) Hierarchical classification of paintings using face and brush strokemodels. International conference on pattern recognition.

  32. Sikudova E, Gavrielides M, Pitas I (2006) Extracting semantic information from art images. Computer Vision and Graphics 32:394–399

    Article  Google Scholar 

  33. Smet MD, Fransens R, Gool LV (2006) A generalized EM approach for 3D model based face recognition under occlusions. IEEE Conference on Computer Vision and Pattern Recognition, 1423–1430.

  34. Spagnolo GS, Somma F (2010) Virtual restoration of cracks in digitized image of paintings. Journal of Physics Conference Series, 249 (1)

  35. Venkat I, Khader AT, Subramanian KG, De Wilde P (2012) Recognizing occluded faces by exploiting psychophysically inspired similarity maps. Pattern Recognition Letters 34(8):903–911

    Article  Google Scholar 

  36. Vranos IC (2001) H Techniki tis Agiographias. P. S. Pournaras (In Greek), Thessaloniki

  37. Wang ZM, Tao JH (2007) Reconstruction of partially occluded face by fast recursive PCA. International conference on computational intelligence and security workshops, Harbin, December 15–19, 2007

  38. Wu C, Liu C, Shum HY, Xy YQ, Zhang Z (2004) Automatic eyeglasses removal from face images. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 26:322–336

    Article  Google Scholar 

  39. Yang M, Zhang L (2010) Gabor feature based sparse representation for face recognition with gabor occlusion dictionary. In: European Conference on Computer Vision (ECCV) (ed) Volume 6316 of lecture notes in computer science. Springer Berlin, Heidelberg, pp 448–461

    Google Scholar 

  40. Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Cyprus Research Promotion Foundation and the European Union Structural Funds (project ΤΠΕ/ΠΛΗΡΟ/0609(ΒΙΕ)/05). We would also like to thank the iconographers Dr. D. Demosthenous and Mr. C. Karis and the 3D modeler Mr. A. El Kater for their contribution.

Author information

Authors and Affiliations

  1. Visual Media Computing Lab, Department of Multimedia and Graphic Arts, Cyprus University of Technology, 31 Archbishop Kyprianos Street, P. O. Box 50329, 3603, Lemesos, Cyprus

    A. Maronidis, C. Voutounos & A. Lanitis

Authors
  1. A. Maronidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Voutounos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Lanitis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Maronidis.

Appendix A

Appendix A

Byzantine geometric rules

Table 5 Description of 23 utilized Byzantine geometric rules
Full size table
Fig. 20
figure 20

Indicative 3D distances used for the implementation of discrepancy metrics. In the figure the distances d0, d1, d2, d3, d4, d5, d9, d10, d29, d30, d31, d40, d41, d42 and d43 are shown. Distances are derived based on the coordinates of the corresponding vertices

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maronidis, A., Voutounos, C. & Lanitis, A. Designing and evaluating an expert system for restoring damaged byzantine icons. Multimed Tools Appl 74, 9747–9770 (2015). https://doi.org/10.1007/s11042-014-2149-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-2149-1

Keywords

Search

Navigation