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Identifying and Discovering Curve Pattern Designs from Fragments of Pottery.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identifying and Discovering Curve Pattern Designs from Fragments of Pottery./
Author:	Zhou, Jun.
Description:	1 online resource (119 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-05, Section: A.
Contained By:	Dissertations Abstracts International84-05A.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29214423click for full text (PQDT)
ISBN:	9798352954973

Identifying and Discovering Curve Pattern Designs from Fragments of Pottery.
Zhou, Jun.

Identifying and Discovering Curve Pattern Designs from Fragments of Pottery. - 1 online resource (119 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: A.

Thesis (Ph.D.)--University of South Carolina, 2022.

Includes bibliographical references

The surface of many cultural heritage objects, such as pottery sherds found in the Southeastern Woodlands, were embellished with curve patterns. The original full designs of these patterns reflect rich historical and cultural information. However, in practice, most of the objects are fragmentary, making the underlying complete designs unknowable at the scale of the sherd fragment. The challenge to reconstruct and study complete designs is stymied because 1) most pottery sherds contain only a small portion of the underlying full design, 2) curve patterns detected on a sherd are usually incomplete and noisy, and 3) in the case of a stamping application, the same design may be applied multiple times with spatial overlap on a pottery, resulting in a \extit{composite} pattern. The goal of our study is to address these challenges and better identify and discover the full designs from fragmented pottery sherds. In this research, we study two important computer vision problems: design identification that identifies a sherd underlying design, and sherd identification that clusters unidentified sherds to discover unknown designs. We focus both problems on curve patterns, and develop new algorithms to address them respectively. For design identification, we formulate this problem as matching: a binary curve pattern image segmented from a sherd depth image is matched to each known full design, and the best matching proposes its underlying design. We develop two curve-pattern matching algorithms for this purpose. First, we develop a new curve matching method by extending Chamfer matching, which decomposes a composite pattern into multiple candidate components as long as these components match to a partial design. A new matching cost is defined by the optimal combination of these components. Second, we develop a new patch-based curve pattern matching method aiming to locate the most similar regions between the sherd and the considered full design. Specifically, we apply uniform sampling for constructing patches and employ a learning-based curve feature descriptor to derive a heatmap for the local similarity between the sherd and the design. With this heatmap, we locate the best matching portions by region growing and define a new matching cost considering overall similarity of these portions. For sherd identification, we develop a new clustering algorithm to identify and group sherds with the same design. Given the segmented curve-pattern images of a collection of sherds, we first conduct patch-based pairwise matching between each pair of sherds to construct a similarity matrix. The pairwise matching is based on the best-matched patches between the two sherds to handle possible composite patterns. We build a fully connected graph based on this similarity matrix and partition the graph into subgraphs/clusters by adaptive thresholding. An iterative cluster refining strategy is developed, with curve-pattern stitching in the iteration, for identifying and refining the sherd clustering.We collect a set of pottery sherds from the heartland of the paddle-stamping tradition with a subset of known paddle-stamped designs from southeastern North America to evaluate the developed algorithms. Moreover, we develop the Snowvision, a computer-aid system that includes sherd digitization, preservation, curve pattern segmentation from a digitized sherd depth image, design identification and sherd identification based on the developed algorithms.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798352954973Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Curve-pattern clusteringIndex Terms--Genre/Form:

542853
Electronic books.

Identifying and Discovering Curve Pattern Designs from Fragments of Pottery.
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520 $a The surface of many cultural heritage objects, such as pottery sherds found in the Southeastern Woodlands, were embellished with curve patterns. The original full designs of these patterns reflect rich historical and cultural information. However, in practice, most of the objects are fragmentary, making the underlying complete designs unknowable at the scale of the sherd fragment. The challenge to reconstruct and study complete designs is stymied because 1) most pottery sherds contain only a small portion of the underlying full design, 2) curve patterns detected on a sherd are usually incomplete and noisy, and 3) in the case of a stamping application, the same design may be applied multiple times with spatial overlap on a pottery, resulting in a \extit{composite} pattern. The goal of our study is to address these challenges and better identify and discover the full designs from fragmented pottery sherds. In this research, we study two important computer vision problems: design identification that identifies a sherd underlying design, and sherd identification that clusters unidentified sherds to discover unknown designs. We focus both problems on curve patterns, and develop new algorithms to address them respectively. For design identification, we formulate this problem as matching: a binary curve pattern image segmented from a sherd depth image is matched to each known full design, and the best matching proposes its underlying design. We develop two curve-pattern matching algorithms for this purpose. First, we develop a new curve matching method by extending Chamfer matching, which decomposes a composite pattern into multiple candidate components as long as these components match to a partial design. A new matching cost is defined by the optimal combination of these components. Second, we develop a new patch-based curve pattern matching method aiming to locate the most similar regions between the sherd and the considered full design. Specifically, we apply uniform sampling for constructing patches and employ a learning-based curve feature descriptor to derive a heatmap for the local similarity between the sherd and the design. With this heatmap, we locate the best matching portions by region growing and define a new matching cost considering overall similarity of these portions. For sherd identification, we develop a new clustering algorithm to identify and group sherds with the same design. Given the segmented curve-pattern images of a collection of sherds, we first conduct patch-based pairwise matching between each pair of sherds to construct a similarity matrix. The pairwise matching is based on the best-matched patches between the two sherds to handle possible composite patterns. We build a fully connected graph based on this similarity matrix and partition the graph into subgraphs/clusters by adaptive thresholding. An iterative cluster refining strategy is developed, with curve-pattern stitching in the iteration, for identifying and refining the sherd clustering.We collect a set of pottery sherds from the heartland of the paddle-stamping tradition with a subset of known paddle-stamped designs from southeastern North America to evaluate the developed algorithms. Moreover, we develop the Snowvision, a computer-aid system that includes sherd digitization, preservation, curve pattern segmentation from a digitized sherd depth image, design identification and sherd identification based on the developed algorithms.
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