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From Fully Supervised to Blind Digital Anastylosis on DAFNE Dataset

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Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

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Abstract

Anastylosis is an archaeological term consisting in a reconstruction technique whereby an artefact is restored using the original architectural elements. Experts can sometimes imply months or years to carry out this task counting on their expertise. Software procedures can represent a valid support but several challenges arise when dealing with practical scenarios. This paper starts from the achievements on DAFNE challenge, with a traditional template matching approach which won the third place at the competition, to arrive to discuss the critical issues that make the unsupervised version, the blind digital anastylosis, a hard problem to solve. A preliminary solution supported by experimental results is presented.

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Notes

  1. 1.

    https://vision.unipv.it/DAFchallenge/DAF-notice.html.

  2. 2.

    Available from the link https://vision.unipv.it/DAFchallenge/DAFNE_dataset/dataset_download.html.

  3. 3.

    The solution to the testing frescoes has been released after the competition.

  4. 4.

    The fresco is “Mantenga - Camera picta 2019-2-20 17.27.40” in the DAFNE dataset.

  5. 5.

    The fresco is “Giotto - Massacre of the Innocents 2019-2-16 16.13.48” in DAFNE dataset.

References

  1. Abate, A.F., Barra, P., Barra, S., Molinari, C., Nappi, M., Narducci, F.: Clustering facial attributes: narrowing the path from soft to hard biometrics. IEEE Access 8, 9037–9045 (2020). https://doi.org/10.1109/ACCESS.2019.2962010

    Article  Google Scholar 

  2. Abate, A.F., Barra, S., Galeotafiore, G., Díaz, C., Aura, E., Sánchez, M., Mas, X., Vendrell, E.: An augmented reality mobile app for museums: virtual restoration of a plate of glass. In: Ioannides, M., Fink, E., Brumana, R., Patias, P., Doulamis, A., Martins, J., Wallace, M. (eds.) EuroMed 2018. LNCS, vol. 11196, pp. 539–547. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01762-0_47

    Chapter  Google Scholar 

  3. Altman, T.: Solving the jigsaw puzzle problem in linear time. Appl. Artif. Intell. Int. J. 3(4), 453–462 (1989)

    Article  Google Scholar 

  4. Andreoli, M.: Mosaics of frescoes: digital photogrammetry, raster representation, pigment analysis and metrology of a flavian wall painting on the caelian hill (rome). Cambridge Archaeol. J. 24(2), 233–248 (2014)

    Article  Google Scholar 

  5. Anzalone, L., Barra, P., Barra, S., Narducci, F., Nappi, M.: Transfer learning for facial attributes prediction and clustering. In: Wang, G., El Saddik, A., Lai, X., Martinez Perez, G., Choo, K.-K.R. (eds.) iSCI 2019. CCIS, vol. 1122, pp. 105–117. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-1301-5_9

    Chapter  Google Scholar 

  6. Barra, P., Barra, S., Nappi, M., Narducci, F.: Saffo: a sift based approach for digital anastylosis for fresco reconstruction. Pattern Recogn. Lett. (2020). https://doi.org/10.1016/j.patrec.2020.07.008, http://www.sciencedirect.com/science/article/pii/S0167865520302543

  7. Chacón, P., Wriggers, W.: Multi-resolution contour-based fitting of macromolecular structures. J. Mol. Biol. 317(3), 375–384 (2002)

    Article  Google Scholar 

  8. Chung, M.G., Fleck, M.M., Forsyth, D.A.: Jigsaw puzzle solver using shape and color. In: ICSP’98. 1998 Fourth International Conference on Signal Processing (Cat. No. 98TH8344), vol. 2, pp. 877–880. IEEE (1998)

    Google Scholar 

  9. Dellepiane, M., Niccolucci, F., Serna, S.P., Rushmeier, H., Van Gool, L., et al.: Reassembling thin artifacts of unknown geometry. In: Proceedings of the 12th International Conference on Virtual Reality, Archaeology and Cultural Heritage, pp. 51–61. Euro Graphics Association Aireola-Ville, Switzerland (2011)

    Google Scholar 

  10. Díaz-Marín, C., Vendrell-Vidal, E., Aura-Castro, E., Abate, A.F., Sánchez-Belenguer, C., Narducci, F.: Virtual reconstruction and representation of an archaeological terracotta statue. In: 2015 Digital Heritage, vol. 2, pp. 699–702. IEEE (2015)

    Google Scholar 

  11. Du, G., Zhou, M., Yin, C., Wu, Z., Shui, W.: Classifying fragments of terracotta warriors using template-based partial matching. Multimed. Tools Appl. 77(15), 19171–19191 (2018)

    Article  Google Scholar 

  12. Du, G., Zhou, M., Yin, C., Zhang, J., Wu, Z., Shui, W.: An automatic positioning algorithm for archaeological fragments. In: Proceedings of the 15th ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Applications in Industry-Volume 1, pp. 431–439 (2016)

    Google Scholar 

  13. Leitao, H.C., Stolfi, J.: Measuring the information content of fracture lines. Int. J. Comput. Vision 65(3), 163–174 (2005)

    Article  Google Scholar 

  14. Li-Ying, Q., Ke-Gang, W.: Kernel fuzzy clustering based classification of ancient-ceramic fragments. In: 2010 2nd IEEE International Conference on Information Management and Engineering, pp. 348–350. IEEE (2010)

    Google Scholar 

  15. Lowe, D.G.: Local feature view clustering for 3d object recognition. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I-I, December 2001. https://doi.org/10.1109/CVPR.2001.990541

  16. Makridis, M., Daras, P.: Automatic classification of archaeological pottery sherds. J. Comput. Cultural Heritage (JOCCH) 5(4), 1–21 (2013)

    Google Scholar 

  17. Paumard, M.M., Picard, D., Tabia, H.: Jigsaw puzzle solving using local feature co-occurrences in deep neural networks. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 1018–1022. IEEE (2018)

    Google Scholar 

  18. Rasheed, N.A., Nordin, M.J.: Classification and reconstruction algorithms for the archaeological fragments. J. King Saud Univ. Comput. Inf. Sci. 26(5), 204–215 (2018)

    Google Scholar 

  19. Smith, P., Bespalov, D., Shokoufandeh, A., Jeppson, P.: Classification of archaeological ceramic fragments using texture and color descriptors. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 49–54. IEEE (2010)

    Google Scholar 

  20. Tammisto, A., Danielli, C., Häyhä, H.: Poster: developing the digital puzzling and reconstruction system for second style fresco fragments from the house of marcus lucretius (ix, 3, 5.24) in pompeii. In: Nuove ricerche archeologiche nell’area Vesuviana (scavi 2003–2006): Atti del Convegno Internazionale, Roma 1.-3.2. 2007, pp. 544–545 (2008)

    Google Scholar 

  21. Tan, T.K., Boon, C.S., Suzuki, Y.: Intra prediction by template matching. In: 2006 International Conference on Image Processing, pp. 1693–1696. IEEE (2006)

    Google Scholar 

  22. Thuswaldner, B., Flöry, S., Kalasek, R., Hofer, M., Huang, Q.X., Thür, H.: Digital anastylosis of the octagon in ephesos. JOCCH 2(1), 1–1 (2009)

    Article  Google Scholar 

  23. Vannucci, P., Maffi-Berthier, V., Stefanou, I., Masi, F.: Structural integrity of notre dame cathedral after the fire of april 15th (2019)

    Google Scholar 

  24. Youguang, W., Li, X., Li, M.: Color and contour based reconstruction of fragmented image. In: 2013 8th International Conference on Computer Science & Education, pp. 999–1003. IEEE (2013)

    Google Scholar 

  25. Yu, R., Russell, C., Agapito, L.: Solving jigsaw puzzles with linear programming. arXiv preprint arXiv:1511.04472 (2015)

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Authors and Affiliations

  1. Department of Computer Sciences, University of Salerno, Fisciano, Italy

    Paola Barra & Fabio Narducci

  2. Department of Electrical and Information Technology Engineering (DIETI), University of Naples Federico II, Naples, Italy

    Silvio Barra

Authors
  1. Paola Barra
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  2. Silvio Barra
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  3. Fabio Narducci
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Corresponding author

Correspondence to Paola Barra .

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Barra, P., Barra, S., Narducci, F. (2021). From Fully Supervised to Blind Digital Anastylosis on DAFNE Dataset. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12663. Springer, Cham. https://doi.org/10.1007/978-3-030-68796-0_45

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  • DOI: https://doi.org/10.1007/978-3-030-68796-0_45

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