Abstract
Large numbers of Terracotta Warriors fragments are mixed up when they are excavated, which calls the need for classifying them in order to avoid one to all matches and improve the efficiency of reassembly. In this paper, we propose a novel template-based classification algorithm for Terracotta Warriors fragments, which transforms the problem of classification into the problem of partial shape matching. Firstly, a few distinct regions, which indicate categories of the fragments, are selected as templates from collections of fragments. Secondly, a novel partial matching procedure is proposed to find whether a fragment have a sub-region that is similar to one of the templates. If there exists a sub-region on the fragment that is similar to one of the templates, this fragment is considered to belong to the category that the matching template represents. The partial matching procedure consists of a coarse matching and a fine matching, which utilizes the normal distribution descriptor and the modified point feature histogram descriptor, respectively. The normal distribution descriptor characterizes the structure of a Euclidean-sphere region and the modified point feature histogram descriptor characterizes the structure of a geodesic-disk region. Experiments have been conducted on Terracotta Warriors fragments that are scanned from real sites to verify the effectiveness of our method. Comparing with other partial matching methods, more accurate classification results are achieved.
Similar content being viewed by others
References
Attene M, Marini S, Spagnuolo M, Falcidieno B (2011) Part-in-whole 3d shape matching and docking. Visual. Computer 27(11):991–1004
Bespalov D, Shokoufandeh A, Regli WC, Sun W (2003) Scale-space representation of 3D models and topological matching. In: Proceedings of the eighth ACM symposium on solid modeling and applications, pp 208–215
Biasotti S, Marini S, Spagnuolo M, Falcidieno B (2006) Sub-part correspondence by structural descriptors of 3d shapes. Comput Aided Des 38(9):1002–1019
Borgelt MG, Kreveld MV, Luo J (2007) Geodesic disks and clustering in a simple polygon. In: Proceedings of international symposium on algorithms and computation, pp 656–667
Bronstein AM, Bronstein MM, Carmon Y, Kimmel R (2009) Partial similarity of shapes using a statistical significance measure. Transactions on Computer Vision and Applications 1:105–114
Brown B, Rusinkiewicz S, Funkhouser T, Weyrich T (2011) Global consistency in the automatic assembly of fragmented artefacts. In: international conference on virtual reality, archaeology and Cultural heritage, pp 73–80
Cornea ND, Demirci MF, Silver D, Shokoufandeh A, Dickinson SJ, Kantor PB (2005) 3D object retrieval using many-to-many matching of curve skeletons. In: Conference on shape modeling and applications, l(25):368–373
Dubrovina A, Kimmel R (2010) Matching shapes by eigen decomposition of the Laplace-Beltrami operator. In: Proc. of fifth international symposium on 3D data processing visualization and transmission
Ferreira A, Marini S, Attene M, Fonseca MJ, Spagnuolo M, Jorge JA, Falcidieno B (2010) Thesaurus-based 3d object retrieval with part-in-whole matching. Int J Comput Vis 89(2–3):327–347
Du GG, Zhou MQ, Yin CL, Zhang J, Wu ZK, Shui WY (2016) An automatic positioning algorithm for archaeological fragments. In: Proceedings of the 15th ACM SIGGRAPH conference on virtual-reality continuum and its applications in industry, pp 431–439
Hu J, Hua J (2009) Salient spectral geometric features for shape matching and retrieval. Visual. Computer 25(5–7):667–675
Huang QX, Flöry S, Gelfand N, Hofer M, Pottmann H (2006) Reassembling fractured objects by geometric matching. ACM Trans Graph 25(3):569–578
Wu HY, Zha H, Luo T, Wang XL (2010) Global and local isometry-invariant descriptor for 3D shape comparison and partial matching. IEEE conference on computer vision and pattern recognition, 119:438–445
Itskovich A, Tal A (2011) Surface partial matching and application to archaeology. Comput Graph 35(2):334–341
Johnson AE, Hebert M (1999) Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Trans Pattern Anal Mach Intell 21(5):433–449
Kampel M, Sablatnig R (2000) Color classification of archeological fragments. In: International conference on pattern recognition, 4(4):771–774
Kampel M, Sablatnig R, Costa E (2001) classification of archeological fragments using profile primitives. In: Proceedings of the 25th workshop of the austrian association for pattern recognition, pp 151–158
Kazhdan M, Funkhouser T, Rusinkiewicz S (2003) Rotation invariant spherical harmonic representation of 3D shape descriptors. Proc of Eurographics/acm SIGGRAPH Symposium on Geometry Processing 121:156–164
Lavoue G (2011) Bag of words and local spectral descriptor for 3D partial shape retrieval. In: Eurographics conference on 3D object retrieval, pp 41–48
Liu ZB, SH B, Zhou K, Gao SM, Han JW, Wu J (2013) A survey on partial retrieval of 3d shapes. J Comput Sci Technol 28(5):836–851
Malassiotis S, Strintzis MG (2007) Snapshots: a novel local surface descriptor and matching algorithm for robust 3d surface alignment. IEEE Trans Pattern Anal Mach Intell 29(7):1285–1290
Martinek M, Grosso R, Greiner G (2014) Interactive partial 3d shape matching with geometric distance optimization. Visual. Computer 31(2):223–233
Mitchell JSB, Mount DM, Papadimitriou CH (1987) The discrete geodesic problem. SIAM J Comput 16(4):647–668
Ran G, Cohen-Or D (2006) Salient geometric features for partial shape matching and similarity. ACM Trans Graph 25(1):130–150
Rasheed NA, Nordin MJ (2015) A survey of computer methods in reconstruction of 3D archeological pottery objects. Int J Adv Res 3(3):712–714
Rasheed NA, Nordin MJ (2015) Archeological fragments classification based on rgb color and texture features. Journal of Theoretical and Applied Information Technology 76(3):358–365
Rusu RB, Blodow N, Marton ZC, Beetz M (2008) Aligning point cloud views using persistent feature histograms. In: Proceedings of IEEE international conference on intelligent robots and systems, pp 3384–3391
Rusu RB, Marton ZC, Blodow N, Beetz M (2008) Learning informative point classes for the acquisition of object model maps. In: Proceedings of international conference on control, automation, robotics and vision, pp 643–650
Rusu RB, Blodow N, Beetz M (2009) Fast point feature histograms (FPFH) for 3D registration. In: Proceedings of IEEE international conference on robotics and automation, pp 3212–3217
Sablatnig R, Menard C, Kropatsch W (1998) Classification of archeological fragments using a description language. In: Signal processing conference, pp 1–4
Smith P, Bespalov D, Shokoufandeh A, Jeppson P (2010) Classification of archeological ceramic fragments using texture and color descriptors. In: Computer vision & pattern recognition workshops, pp 49–54
Sun J, Ovsjanikov M, Guibas L (2009) A concise and provably informative multi-scale signature based on heat diffusion. Computer Graphics Forum 28:1383–1392
Suzuki MT, Yaginuma Y, Yamada T, Shimizu Y (2005) A partial shape matching method for 3D model databases. In: Proceedings of the ninth IASTED international conference on software engineering and applications, pp 389–394
Tombari F, Salti S, Stefano LD (2010) Unique signatures of histograms for local surface description. European Conference on Computer Vision 6313:356–369
Wei L, Yu W, Li M, Li X (2011) Skull assembly and completion using template-based surface matching. In: International conference on 3D imaging, modeling, processing, visualization and transmission, pp 413–420
Yu W, Li M, Li X (2012) Fragmented skull modeling using heat kernels. Graph Model 74(4):140–151
Zhang K, Yu W, Manhein M, Waggenspack W, Li X (2015) Reassembling 3D thin shells using integrated template guidance and fracture region matching. In: ACM SIGGRAPH conference, pp 88
Zhang K, Yu W, Manhein M, Waggenspack W (2015) 3D fragment reassembly using integrated template guidance and fracture-region matching. In: IEEE international conference on computer vision, pp 2138–2146
Zhong Y (2009) Intrinsic shape signatures: a shape descriptor for 3D object recognition. In: International conference on computer vision workshops, pp 689–696
Funding
This research was carried out at Beijing Normal University, with the financial support of the National Key Technology Research and Development Program of China (2017YFB1002804), National Natural Science Foundation of China (61672103, 61170170, 61731015, 61572078 and 61402042).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Du, G., Zhou, M., Yin, C. et al. Classifying fragments of terracotta warriors using template-based partial matching. Multimed Tools Appl 77, 19171–19191 (2018). https://doi.org/10.1007/s11042-017-5396-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-017-5396-0