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Snowvision: Segmenting, Identifying, and Discovering Stamped Curve Patterns from Fragments of Pottery

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Abstract

In southeastern North America, Indigenous potters and woodworkers carved complex, primarily abstract, designs into wooden pottery paddles, which were subsequently used to thin the walls of hand-built, clay vessels. Original paddle designs carry rich historical and cultural information, but pottery paddles from ancient times have not survived. Archaeologists have studied design fragments stamped on sherds to reconstruct complete or nearly complete designs, which is extremely laborious and time-consuming. In Snowvision, we aim to develop computer vision methods to assist archaeologists to accomplish this goal more efficiently and effectively. For this purpose, we identify and study three computer vision tasks: (1) extracting curve structures stamped on pottery sherds; (2) matching sherds to known designs; (3) clustering sherds with unknown designs. Due to the noisy, highly fragmented, composite-curve patterns, each task poses unique challenges to existing methods. To solve them, we propose (1) a weakly-supervised CNN-based curve structure segmentation method that takes only curve skeleton labels to predict full curve masks; (2) a patch-based curve pattern matching method to address the problem of partial matching in terms of noisy binary images; (3) a curve pattern clustering method consisting of pairwise curve matching, graph partitioning and sherd stitching. We evaluate the proposed methods on a set of collected sherds and extensive experimental results show the effectiveness of the proposed algorithms.

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Acknowledgements

This research is supported by the National Endowment for the Humanities (NEH) Digital Humanities Advancement Grant Program (HAA-266472-19), the National Science Foundation Archaeology and Archaeometry Grant Program (1658987), the National Center for Preservation Technology and Training Grant Program (P16AP00373), the Extreme Science and Engineering Discovery Environment (XSEDE) Science Gateway Program (DBS180011), University of South Carolina ASPIRE II Program, and University of South Carolina Social Science Provost Grant Program. We would like to show our gratitude to Frankie Snow at South Georgia State College for sharing his pearls of wisdom and design images with us during the course of this research. We are very grateful to Dr. Matthew Compton, Curator of the R. M. Bogan Repository at Georgia Southern University, and Dr. Amanda Roberts Thompson, Operations Director of the Laboratory of Archaeology at the University of Georgia, for generously providing access to collections, and Scot Keith at New South Associates for his enthusiasm and encouragement of this research. We would like to thank Research Computing at University of South Carolina for high-performance computing support, and the South Carolina Department of Natural Resources for institutional support. The Muscogee (Creek) Nation has approved the scanning of Broyles Collection sherds shown in Figure 16 and requests consultation on any future use of the scans and images including but not limited to presentations, publications, manuscripts, social media posts, and news stories.

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  1. Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA

    Yuhang Lu, Jun Zhou, Canyu Zhang, Deja Scott, Jake Bukuts & Song Wang

  2. Department of Anthropology and School of Information Science, University of South Carolina, Columbia, SC, USA

    Sam T. McDorman

  3. Department of History, University of South Carolina, Columbia, SC, USA

    Colin Wilder

  4. South Carolina Department of Natural Resources, Columbia, SC, USA

    Karen Y. Smith

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Lu, Y., Zhou, J., McDorman, S.T. et al. Snowvision: Segmenting, Identifying, and Discovering Stamped Curve Patterns from Fragments of Pottery. Int J Comput Vis 130, 2707–2732 (2022). https://doi.org/10.1007/s11263-022-01669-7

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