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3D Shape Analysis for Archaeology

  • Ayellet Tal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8355)

Abstract

Archaeology is rapidly approaching an impasse in its ability to handle the overwhelming amount and complexity of the data generated by archaeological research. In this paper, we describe some results of our efforts in developing automatic shape analysis techniques for supporting several fundamental tasks in archaeology. These tasks include documentation, looking for corollaries, and restoration. We assume that the input to our algorithms is 3D scans of archaeological artifacts. Given these scans, we describe three techniques of documentation, for producing 3D visual descriptions of the scans, which are all non-photorealistic. We then proceed to explain our algorithm for partial similarity of 3D shapes, which can be used to query databases of shape, searching for corollaries. Finally, within restoration, we describe our results for digital completion of broken 3D shapes, for reconstruction of 3D shapes based on their line drawing illustrations, and for restoration of colors on 3D objects. We believe that when digital archaeological reports will spread around the globe and scanned 3D representations replace the 2D ones, our methods will not only accelerate, but also improve the results obtained by the current manual procedures.

Keywords

Line Drawing Step Edge Archaeological Artifact Colorization Algorithm Mesh Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    van Kaick, O., Zhang, H., Hamarneh, G., Cohen-Or, D.: A survey on shape correspondence. In: Proc. of Eurographics State-of-the-art Report (2010)Google Scholar
  2. 2.
    Tangelder, J., Veltkamp, R.: A survey of content based 3D shape retrieval methods. Multimedia Tools and Applications 39(3), 441–471 (2008)CrossRefGoogle Scholar
  3. 3.
    Leifman, G., Meir, R., Tal, A.: Semantic-oriented 3D shape retrieval using relevance feedback. The Visual Computer (Pacific Graphics) 21(8-10), 865–875 (2005)CrossRefGoogle Scholar
  4. 4.
    Attene, M., Katz, S., Mortara, M., Patané, G., Spagnuolo, M., Tal, A.: Mesh segmentation-a comparative study. In: Shape Modeling and Applications (2006)Google Scholar
  5. 5.
    Katz, S., Tal, A.: Hierarchical mesh decomposition using fuzzy clustering and cuts. ACM Trans. Graph. 22(3), 954–961 (2003)CrossRefGoogle Scholar
  6. 6.
    Shamir, A.: A survey on mesh segmentation techniques. Computer Graphics Forum 27, 1539–1556 (2008)CrossRefzbMATHGoogle Scholar
  7. 7.
    Besl, P., McKay, H.: A method for registration of 3-D shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  8. 8.
    Lucas, B., Kanade, T., et al.: An iterative image registration technique with an application to stereo vision. In: 7th International Joint Conference on Artificial Intelligence (1981)Google Scholar
  9. 9.
    DeCarlo, D., Finkelstein, A., Rusinkiewicz, S., Santella, A.: Suggestive contours for conveying shape. ACM Transactions on Graphics 22(3), 848–855 (2003)CrossRefGoogle Scholar
  10. 10.
    Judd, T., Durand, F., Adelson, E.: Apparent ridges for line drawing. ACM Transactions on Graphics 26(3), 19:1–19:7 (2007)Google Scholar
  11. 11.
    Ohtake, Y., Belyaev, A., Seidel, H.: Ridge-valley lines on meshes via implicit surface fitting. ACM Transactions on Graphics 23(3), 609–612 (2004)CrossRefGoogle Scholar
  12. 12.
    Lee, C., Varshney, A., Jacobs, D.: Mesh saliency. ACM Trans. on Graph. 24(3), 659–666 (2005)CrossRefGoogle Scholar
  13. 13.
    Shilane, P., Funkhouser, T.: Distinctive regions of 3D surfaces. ACM Trans. on Graphics 26(2) (2007)Google Scholar
  14. 14.
    Leifman, G., Shtrom, E., Tal, A.: Surface regions of interest for viewpoint selection. In: IEEE Computer Vision and Pattern Recognition (CVPR), pp. 414–421 (2012)Google Scholar
  15. 15.
    Ju, T.: Fixing geometric errors on polygonal models: a survey. J. of Computer Science and Technology 24(1), 19–29 (2009)CrossRefGoogle Scholar
  16. 16.
    Liepa, P.: Filling holes in meshes. In: SGP, pp. 200–205 (2003)Google Scholar
  17. 17.
    Sharf, A., Alexa, M., Cohen-Or, D.: Context-based surface completion. ACM Transactions on Graphics 23(3), 878–887 (2004)CrossRefGoogle Scholar
  18. 18.
    Levoy, M., Pulli, K., Curless, B., Rusinkiewicz, R., Koller, D., Pereira, L., Ginzton, M., Anderson, S., Davis, J., Ginsberg, J., Shade, J., Fulk, D.: The digital michelangelo project: 3D scanning of large statues. In: Proceedings of ACM SIGGRAPH 2000, pp. 131–144 (July 2000)Google Scholar
  19. 19.
    Brown, B., Toler-Franklin, C., Nehab, D., Burns, M., Dobkin, D., Vlachopoulos, A., Doumas, C., Rusinkiewicz, S., Weyric, T.: A system for high-volume acquisition and matching of fresco fragments: Reassembling Theran wall paintings. ACM Trans. Graph. 27(3), 84:1–84:9 (2008)Google Scholar
  20. 20.
    Gilboa, A., Tal, A., Shimshoni, I., Kolomenkin, M.: Computer-based, automatic recording and illustration of complex archaeological artifacts. Journal of Archaeological Science (2012)Google Scholar
  21. 21.
    Cignoni, P., Scopigno, R.: Sampled 3d models for ch applications: A viable and enabling new medium or just a technological exercise? Journal on Computing and Cultural Heritage 1(1) (2008)Google Scholar
  22. 22.
    Brutto, M., Spera, M.: Image-based and range-based 3D modeling of archaeological cultural heritage: the Telamon of the Temple of Olympian Zeus in Agrigento (Italy). In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences (2011)Google Scholar
  23. 23.
    Gilboa, A., Karasik, A., Sharon, I., Smilansky, U.: Computerized typology and classification of ceramics. Journal of Archaeological Science 31, 681–694 (2004)CrossRefGoogle Scholar
  24. 24.
    Hanke, K., Moser, M., Grimm-Pitzinger, A., Goldenberg, G., Toechterle, U.: Enhanced potential for the analysis of archaeological finds based on 3d modeling. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVII, Part B, vol. 5, pp. 187–192 (2008)Google Scholar
  25. 25.
    Karasik, A., Smilansky, U.: 3D scanning technology as a standard archaeological tool for pottery analysis: practice and theory. Journal of Archaeological Science 35 (2008)Google Scholar
  26. 26.
    Pires, H., Ortiz, P., Marques, P., Sanchez, H.: Close-range laser scanning applied to archaeological artifacts documentation. In: International Symposium on Virtual Reality, Archaeology and Cultural Heritage (VAST), pp. 284–289 (2006)Google Scholar
  27. 27.
    Gooch, B., Gooch, A.: Non-Photorealistic Rendering. AK Peters Ltd. (2001)Google Scholar
  28. 28.
    Biederman, I.: Visual Cognition. MIT Press, Cambridge, MA and London (1995)zbMATHGoogle Scholar
  29. 29.
    Kolomenkin, M., Shimshoni, I., Tal, A.: Demarcating curves for shape illustration. ACM Transactions on Graphics 27(5), 157:1–157:9 (2008)Google Scholar
  30. 30.
    Kolomenkin, M., Shimshoni, I., Tal, A.: On edge detection on surfaces. In: CVPR, pp. 2767–2774 (2009)Google Scholar
  31. 31.
    Ohtake, Y., Belyaev, A., Seidel, H.P.: Ridge-valley lines on meshes via implicit surface fitting. ACM Trans. Graph. 23(3), 609–612 (2004)CrossRefGoogle Scholar
  32. 32.
    Kindlmann, G., Whitaker, R., Tasdizen, T., Moller, T.: Curvature-Based Transfer Functions for Direct Volume Rendering: Methods and Applications. In: IEEE Visualization, pp. 67–76 (2003)Google Scholar
  33. 33.
    Kolomenkin, M., Shimshoni, I., Tal, A.: Prominent field for shape processing of archaeological artifacts. IJCV 94(1), 89–100 (2011)CrossRefGoogle Scholar
  34. 34.
    Zatzarinni, R., Tal, A., Shamir, A.: Relief analysis and extraction. ACM Transactions on Graphics 28(5), 136 (2009)CrossRefGoogle Scholar
  35. 35.
    Itskovich, A., Tal, A.: Surface partial matching and application to archaeology. Computers & Graphics (2011)Google Scholar
  36. 36.
    Kimia, B., Frankel, I., Popescu, A.: Euler spiral for shape completion. Int. J. Comp. Vision 54(1), 159–182 (2003)zbMATHGoogle Scholar
  37. 37.
    Harary, G., Tal, A.: 3D Euler spirals for 3D curve completion. Computational Geometry: Theory and Applications 45(3), 115–126 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Singh, M., Fulvio, J.: Visual extrapolation of contour geometry. PNAS 102(3), 939–944 (2005)CrossRefGoogle Scholar
  39. 39.
    Knuth, D.: Mathematical typography. Bulletin AMS 1(2), 337–372 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Ullman, S.: Filling-in the gaps: The shape of subjective contours and a model for their generation. Biological Cybernetics 25(1), 1–6 (1976)Google Scholar
  41. 41.
    Kolomenkin, M., Leifman, G., Shimshoni, I., Tal, A.: Reconstruction of relief objects from line drawings. CVPR 2(12), 13–19 (2011)Google Scholar
  42. 42.
    Bonn-Muller, E.: Carved in living color. Archaeology 61(1) (2008)Google Scholar
  43. 43.
    Leifman, G., Tal, A.: Mesh colorization. Computer Graphics Forum 31(2), 421–430 (2012)CrossRefGoogle Scholar
  44. 44.
    Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. ACM Transactions on Graphics 23(3), 689–694 (2004)CrossRefGoogle Scholar
  45. 45.
    Huang, Y.C., Tung, Y.S., Chen, J.C., Wang, S.W., Wu, J.L.: An adaptive edge detection based colorization algorithm and its applications. ACM Multimedia, 351–354 (2005)Google Scholar
  46. 46.
    Johnson, A., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433–449 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ayellet Tal
    • 1
  1. 1.TechnionHaifaIsrael

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