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As a non-destructive method for multi-element analysis, portable X-ray fluorescence spectrometry (pXRF) has the potential for broad archaeological application. Here, we employ pXRF for the compositional analysis of whiteware and stoneware sherds collected from Stoddartsville, a nineteenth century milling village built along the upper Lehigh River in northeast Pennsylvania. Our analysis demonstrates that we can use compositional data to distinguish between makers of macroscopically similar pottery. In turn, these data will expand our ability to source pottery from Stoddartsville, providing insight into the regional economy, the development of local ceramic industries, and consumer agency.
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A Framework for Design Identification on Heritage Objects
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.
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- Award ID(s):
- 1658987
- PAR ID:
- 10172223
- Date Published:
- Journal Name:
- PEARC '19: Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3332186.3332190
