ABSTRACT
A challenging problem in modern archaeology is to automatically identify fragmented heritage objects by their decorative full designs, such as the pottery sherds from Southeastern America. The difficulties of this problem lie in: 1) these pottery sherds are usually fragmented so that each sherd only covers a small portion of its underlying full design; 2) these sherds can be so highly degraded that curves may contain missing segments or become very shallow; and 3) curve patterns may overlap with each other from the making of these potteries. This paper presents a deep-learning based framework for matching a sherd with a database of known designs to find its underlying design. This framework contains three steps: 1) extracting curve pattern using an FCN-based curve pattern segmentation method from the digitized sherd's depth map, 2) matching a sherd with a non-composite (single copy of a design) pattern combining template matching algorithm with a dual-source CNN re-ranking method to find its underlying design, and 3) matching a sherd with a composite (multiple copies of a design) pattern using a Chamfer Matching based method. The framework was evaluated on a set of sherds from the heartland of the paddle-stamping tradition with a subset of known paddle-stamped designs of Pre-colonial southeastern North America. Extensive experimental results show the effectiveness of the proposed framework and algorithms.
- Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence 39, 12 (2017), 2481--2495.Google Scholar
- Harry Barrow, Jay Tenenbaum, Robert Bolles, and Helen Wolf. 1977. Parametric correspondence and Chamfer matching: Two new techniques for image matching. In International Joint Conference on Artificial Intelligence, Vol. 2. 659--663. Google ScholarDigital Library
- Serge Belongie, Jitendra Malik, and Jan Puzicha. 2001. Shape Context: A new descriptor for shape matching and object recognition. In Advances in Neural Information Processing Systems. 831--837. Google ScholarDigital Library
- Roberto Brunelli. 2009. Template Matching Techniques in Computer Vision: Theory and Practice. John Wiley & Sons. Google ScholarDigital Library
- Y. Cao, Z. Zhang, I. Czogiel, et al. 2011. 2D nonrigid partial shape matching using MCMC and contour subdivision. In IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2345--2352. Google ScholarDigital Library
- Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. 2018. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40, 4 (2018), 834--848.Google Scholar
- Fernand Cohen, Ezgi Taslidere, Zexi Liu, et al. 2010. Virtual reconstruction of archaeological vessels using expert priors & surface markings. In IEEE Conference on Computer Vision and Pattern Recognition - Workshops. IEEE, 7--14.Google ScholarCross Ref
- David Cooper, Andrew Willis, Stuart Andrews, et al. 2001. Assembling virtual pots from 3D measurements of their fragments. In Conference on Virtual Reality, Archeology, and Cultural Heritage. ACM, 241--254. Google ScholarDigital Library
- Ayelet Gilboa, Avshalom Karasik, Ilan Sharon, et al. 2004. Towards computerized typology and classification of ceramics. Journal of Archaeological Science 31, 6 (2004), 681--694.Google ScholarCross Ref
- Radim Halíř. 1999. An automatic estimation of the axis of rotation of fragments of archaeological pottery: A multi-step model-based approach. In International Conference in Central Europe on Computer Graphics, Visualization and Interactive Digital Media. WSCG.Google Scholar
- Xufeng Han, Thomas Leung, Yangqing Jia, Rahul Sukthankar, and Alexander Berg. 2015. MatchNet: Unifying feature and metric learning for patch-based matching. In IEEE Conference on Computer Vision and Pattern Recognition. 3279--3286.Google Scholar
- Martin Kampel and Robert Sablatnig. 2007. Rule based system for archaeological pottery classification. Pattern Recognition Letters 28, 6 (2007), 740--747. Google ScholarDigital Library
- Avshalom Karasik and Uzy Smilansky. 2008. 3D scanning technology as a standard archaeological tool for pottery analysis: practice and theory. Journal of Archaeological Science 35, 5 (2008), 1148--1168.Google ScholarCross Ref
- Avshalom Karasik and Uzy Smilansky. 2011. Computerized morphological classification of ceramics. Journal of Archaeological Science 38, 10 (2011), 2644--2657.Google ScholarCross Ref
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems. 1097--1105. Google ScholarDigital Library
- Kevin Lin, Hueifang Yang, Jenhao Hsiao, and Chusong Chen. 2015. Deep learning of binary hash codes for fast image retrieval. In IEEE Conference on Computer Vision and Pattern Recognition - Workshops. 27--35.Google ScholarCross Ref
- Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3431--3440.Google ScholarCross Ref
- Liana M Lorigo, Olivier D Faugeras, W Eric L Grimson, Renaud Keriven, Ron Kikinis, Arya Nabavi, and C-F Westin. 2001. Curves: Curve evolution for vessel segmentation. Medical image analysis 5, 3 (2001), 195--206.Google Scholar
- Yuhang Lu, Jun Zhou, Jun Chen, Jing Wang, Karen Smith, Wilder Colin, and Song Wang. 2018. Curve-structure segmentation from depth maps: A CNN-based approach and its application to exploring cultural heritage objects. AAAI Conference on Artificial Intelligence (2018).Google Scholar
- Michael Makridis and Petros Daras. 2012. Automatic classification of archaeological pottery sherds. Journal on Computing and Cultural Heritage 5, 4, Article 15 (2012), 21 pages. Google ScholarDigital Library
- Bangalore Manjunath and Weiying Ma. 1996. Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 8 (1996), 837--842. Google ScholarDigital Library
- Philip Phillips and James Brown. 1978. Pre-Columbian Shell Engravings from the Craig Mound at Spiro, Oklahoma, Part 1. Peabody Museum Press, Cambridge.Google Scholar
- Li-Ying Qi and Ke-Gang Wang. 2010. Kernel fuzzy clustering based classification of Ancient-Ceramic fragments. In International Conference on Information Management and Engineering. IEEE, 348--350.Google Scholar
- Dorie Reents-Budet. 1994. Painting the Maya Universe: Royal Ceramics of the Classic Period (Duke University Museum of Art). Duke University Press, Durham.Google Scholar
- Edgar Roman-Rangel, Diego Jimenez-Badillo, and Estibaliz Aguayo-Ortiz. 2014. Categorization of Aztec potsherds using 3D local descriptors. In Asian Conference on Computer Vision - Workshops. Springer, 567--582.Google Scholar
- Edgar Roman-Rangel, Carlos Pallan, Jean-Marc Odobez, et al. 2011. Analyzing ancient Maya glyph collections with contextual shape descriptors. International Journal of Computer Vision 94, 1 (2011), 101--117. Google ScholarDigital Library
- Xiaodong Tao, Jerry L Prince, and Christos Davatzikos. 2002. Using a statistical shape model to extract sulcal curves on the outer cortex of the human brain. IEEE Transactions on Medical Imaging 21, 5 (2002), 513--524.Google ScholarCross Ref
- Roger Weber, HansJörg Schek, and Stephen Blott. 1998. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In International Conference on Very Large Data Bases, Vol. 98. 194--205. Google ScholarDigital Library
- Sergey Zagoruyko and Nikos Komodakis. 2015. Learning to compare image patches via convolutional neural networks. In IEEE Conference on Computer Vision and Pattern Recognition. 4353--4361.Google ScholarCross Ref
- Christoph Zauner. 2010. Implementation and Benchmarking of Perceptual Image Hash Functions. Upper Austria University of Applied Sciences.Google Scholar
- Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, and Philip HS Torr. 2015. Conditional random fields as recurrent neural networks. In Proceedings of the IEEE international conference on computer vision. 1529--1537. Google ScholarDigital Library
- Jun Zhou, Haozhou Yu, Karen Smith, Colin Wilder, Hongkai Yu, and Song Wang. 2017. Identifying designs from incomplete, fragmented cultural heritage objects by curve-pattern matching. Journal of Electronic Imaging 26, 1 (2017), 011022--011022.Google ScholarCross Ref
- Qin Zou, Yu Cao, Qingquan Li, Qingzhou Mao, and Song Wang. 2012. CrackTree: Automatic crack detection from pavement images. Pattern Recognition Letters 33, 3 (2012), 227--238. Google ScholarDigital Library
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