Skip to main content
Log in

Mending broken vessels a fusion between color markings and anchor points on surface breaks

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

Abstract

This paper presents a method to assist in the tedious process of reconstructing ceramic vessels from excavated fragments. The method exploits vessel surface marking information coupled with a series of generic models constructed by the archaeologists to produce a virtual reconstruction of what the original vessel may have looked like. Generic models are generated based on the experts’ historical knowledge of the period, provenance of the artifact, and site location. The generic models need not to be identical to the original vessel, but must be within a geometric transformation of it in most parts. By aligning the fragments against the generic models, the ceramic vessels are virtually reconstructed. The alignment is based on a novel set of weighted discrete moments computed from convex hulls of the markings on the surface of the fragments and the generic vessels. When the fragments have no markings on them, they are virtually mended to abutting fragments using intrinsic differential anchor points computed on the surface breaks and aligned using a set of absolute invariants. For axially symmetric objects, a global constraint induced by the surface of revolution is added to guarantee global mending consistency.

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

Access this article

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

Similar content being viewed by others

References

  1. Amigoni F, Gazzani S, Podico S(2003) “A method for reassembling fragments in image reconstruction,” in International Conference on Image Processing, pp 581–584

  2. Barber CB, Dobkin DP, Huhdanpaa H (1996) The quickhull algorithm for convex hulls. ACM Trans Math Softw 22:469–483

    Article  MathSciNet  MATH  Google Scholar 

  3. Bratko I (1990) Prolog Programming for Arificial Intelligence. Addison Wesley, London

    Google Scholar 

  4. Brown BJ, Rusinkiewicz S (2007) “Global non-rigid alignment of 3-D scans,” presented at the ACM SIGGRAPH, San Diego, California

  5. Brown BJ, Toler-Franklin C, Nehab D, Burns M, Dobkin D, Vlachopoulos A et al., (2008) “A System for High-Volume Acquisition and Matching of Fresco Fragments: Reassembling Theran Wall Paintings,” ACM Transactions on Graphics, vol. 27, August

  6. Bujakiewicz A, Kowalczyk M, Podlasiak P, Zawieska D (2006) “3D Reconstruction and Modelling of the Contact Surfaces for the Archaeological Small Museum Pieces,” in Proceedings of the ISPRS Commission V Symposium ‘Image Engineering and Vision Metrology’, Dresden, Germany

  7. Cohen F, Liu Z, Zhang Z (2013) “Reconstructing Archeological Vessels by Fusing Surface Markings and Border Anchor Points on Fragments,” presented at the International Conference on Image Analysis and Processing workshop, Naples, Italy

  8. Cohen F, Taslidere E, Liu Z, Muschio G (2010) “Virtual Reconstruction of Archaeological Vessels using Expert Priors & Surface Markings,” presented at the CVPR, San Francisco, CA, USA

  9. Cohen F, Zhang Z, Jeppson P (2010) “Virtual Reconstruction of Archaeological Vessels using Convex Hulls of Surface Markings,” presented at the CVPR, San Francisco, CA, USA

  10. Faber TL, Stokely EM (1988) Orientation of 3-D structures in medical images. IEEE Trans Pattern Anal Mach Intell 10:626–633

    Article  Google Scholar 

  11. Gardner LYH, Jin H, Liu N, Hawkins R, Farrington I (2007) “Interactive reconstruction of archaeological fragments in a collaborative environment,” in Proceedings of the IEEE 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications, pp 23–29

  12. Gool LV, Sablatnig R (2006) Special issue on 3D acquisition technology for cultural heritage. Mach Vis Appl 17:347–348

    Article  Google Scholar 

  13. Huang Q, Flöry S, Gelfand N, Hofer M, Pottmann H (2006) Reassembling fractured objects by geometric matching. ACM Trans Graph 25:569–578

    Article  Google Scholar 

  14. Igwe PC, Knopf GK (2006) “3D Object Reconstruction Using Geometric Computing,” in Geometric Modeling and Imaging--New Trends, 2006, pp 9–14

  15. Jeppson PL New Methods -- and forthcoming Records! http://digginginthearchives.blogspot.com [Online].

  16. Kampel M, Sablating R (2006) “3D data retrieval of archaeological pottery”. In: H. Zha, Z Pan, H Thwaites, AC Addison, M Forte (Eds) in Lecture Notes in Computer Science. vol. 4270 pp 387–395

  17. Karasik A, Smilansky U (2008) 3D scanning technology as a standard archaeological tool for pottery analysis: practice and theory. J Archaeol Sci 35:1148–1168

    Article  Google Scholar 

  18. Kleber F, Sablatnig R (2009) “A Survey of Techniques for Document and Archaeology Artefact Reconstruction” in Document Analysis and Recognition, 2009. ICDAR ′09. 10th International Conference on, pp 1061–1065

  19. Knopf GK, Kofman J (2002) Surface reconstruction using neural network mapping of range-sensor images to object space. J Electron Imaging 11:187–194

    Article  Google Scholar 

  20. Koutsoudis A, Pavlidis G, Arnaoutoglou F, Tsiafakis D, Chamzas C (2009) Qp: A tool for generating 3D models of ancient Greek pottery. J Cult Herit 10:281–295

    Article  Google Scholar 

  21. Kozinska D, Tretiak OJ, Nissanov J, Ozturk C (1997) Multidimensional alignment using the euclidean distance transform. Graph Model Image Proc 59:373–387

    Article  Google Scholar 

  22. Kühnel W (2006) Differential geometry : curves - surfaces - manifolds, 2nd ed. American Mathematical Society, Providence

    Google Scholar 

  23. Liu Z, Cohen F, Taslidere E (2013) “Reconstructing Archeological Vessels from Fragments using Anchor Points Residing on Shard Fragment Borders,” presented at the International Conference on Computer Vision Theory and Applications, Barcelona, Spain

  24. Lo C, Don H (1989) 3D moment forms: their construction and application to object identification and positioning. IEEE Trans Pattern Anal Mach Intell 11:1053–1064

    Article  Google Scholar 

  25. Mara H, Kampel M, Niccolucci F, Sablatnig R (2007) “Ancient coins & ceramics – 3D and 2D documentation for preservation and retrieval of lost heritage,” in Proceedings of the 2nd ISPRS International Workshop 3D-ARCH, Zurich, Switzerland

  26. McBride JC, Kimia BB (2003) “Archaeological Fragment Reconstruction Using Curve-Matching,” in Computer Vision and Pattern Recognition Workshop, 2003. CVPRW ′03. Conference on, pp 3–3

  27. Olsen S, Brickman A, Cai Y (2004) “Discovery by Reconstruction: Exploring Digital Archeology,” presented at the SIGCHI Workshop (Ambient Intelligence for Scientific Discovery (AISD)), Vienna

  28. Oxholm G, Nishino K (2011) “Aligning surfaces without aligning surfaces,” in Proceedings of the IEEE Workshop on Applications of Computer Vision, pp 174–181

  29. Papaioannou G, Karabassi EA (2003) On the automatic assemblage of arbitrary broken solid artefacts. Image Vis Comput 21:401–412

    Article  Google Scholar 

  30. Rodriguez W, Last M, Kandel A, Bunke H (2004) 3-Dimensional curve similarity using string matching. Robot Auton Syst 49:165–172

    Article  Google Scholar 

  31. Sagiroglu MS, Ercil A (2006) “A Texture Based Matching Approach for Automated Assembly of Puzzles,” presented at the International Conference on Pattern Recognition, Hong Kong

  32. Sağıroğlu MS, Erçil A (2005) “A texture based approach to reconstruction of archaeological finds,” presented at the Symposium on Virtual Reality, Archaeology, and Cultural Heritage

  33. Saharan R, Singh CV (2011) Reassembly of 2D fragments in image reconstruction. Int J Comput Appl 19:41–45

    Google Scholar 

  34. Son K, Almeida EB, Cooper DB (2013) “Axially Symmetric 3D Pots Configuration System Using Axis of Symmetry and Break Curve,” presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR

  35. Taubin G (1991) Estimation of planar curves, surfaces, nonplanar surface curves defined by implicit equations with applications to edge and range image segmentation. IEEE Trans Pattern Anal Mach Intell 13:1115–1138

    Article  Google Scholar 

  36. Thomas TP, Anderson DD, Willis AR, Liu P, Frank MC, Marsh JL et al (2011) A computational/experimental platform for investigating three-dimensional puzzle solving of comminuted articular fractures. Comput Meth Biomech Biomed Eng 14:263–270

    Article  Google Scholar 

  37. Trummer M, Suesse H, Denzler J (2009) “Coarse registration of 3D surface triangulations based on moment invariants with applications to object alignment and identification,” presented at the IEEE International Conference on Computer Vision (ICCV)

  38. Tsamoura E, Pitas I (2010) Automatic color based reassembly of fragmented images and paintings. IEEE Trans Image Process 19:680–690

    Article  MathSciNet  Google Scholar 

  39. Ucoluk G, Toroslu IH (1999) Automatic reconstruction of broken 3-D surface objects. Comput Graph 23:573–582

    Article  Google Scholar 

  40. Umbach D, Jones KN (2000) A Few methods for fitting circles to data. IEEE Trans Instrum Meas 52:1881–1885

    Article  Google Scholar 

  41. Umeyama S (1991) Least-square estimation of transformation parameters between two point patterns. IEEE Trans Pattern Anal Mach Intell 13:376–380

    Article  Google Scholar 

  42. Willis A, Orriols X, Cooper DB (2003) “Accurately Estimating Sherd 3D Surface Geometry with Application to Pot Reconstruction,” presented at the CVPR Workshop: ACVA, Madison, WI, USA

  43. Winkelbach S, Wahl FM (2008) Pairwise matching of 3D fragments using cluster trees. Int J Comput Vis 78:1–13

    Article  Google Scholar 

  44. Yang Z, Cohen F (1999) “Image Registration and Object Recognition Using Affine Invariants and Convex Hulls,” IEEE Transactions on Image Processing, vol. 8

  45. Yang Z, Cohen F (1999) “Cross-Weighted Moments and Affine Invariants for Image Registration and Matching,” Ieee Transactions on Pattern Analysis and Machine Intelligence, vol. 21

  46. Zhu L, Zhou Z, Hu D (2008) Globally consistent reconstruction of ripped-up documents. IEEE Trans Pattern Anal Mach Intell 30:1–13

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Science Foundation IIS division under grant number 0803670. We would also like to acknowledge the efforts in scanning which was conducted by Drexel University STAR Scholar Program undergraduate students Girish Balakrishnan, David Myers and Mark Petrovitch, under the direction of Dr. Glen Muschio and by Drexel University Electrical and Computer Engineering Department graduate student Ezgi Taslidere. We would like also to acknowledge the expertise provided our archaeologist collaborator Dr. Patrice Jeppson in providing the generic models.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhongchuan Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cohen, F., Zhang, Z. & Liu, Z. Mending broken vessels a fusion between color markings and anchor points on surface breaks. Multimed Tools Appl 75, 3709–3732 (2016). https://doi.org/10.1007/s11042-014-2190-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-2190-0

Keywords

Navigation