Name
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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3332186.3332190 | Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) |
Keywords | Field | DocType |
Cultural heritage protection, composite curve patterns, curve pattern extraction, curve pattern matching, neural networks | Computer science,Human–computer interaction | Conference |
ISSN | ISBN | Citations |
978-1-4503-7227-5 | 978-1-4503-7227-5 | 0 |
PageRank | References | Authors |
0.34 | 21 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Zhou | 1 | 614 | 95.94 |
| Yuhang Lu | 2 | 17 | 4.62 |
| Karen Smith | 3 | 4 | 1.77 |
| Colin Wilder | 4 | 4 | 1.77 |
| Song Wang | 5 | 954 | 79.55 |
| Paul Sagona | 6 | 0 | 0.34 |
| Ben Torkian | 7 | 0 | 0.34 |