Identifying designs from incomplete, fragmented cultural heritage objects by curve-pattern matching

Abstract

Study of cultural-heritage objects with embellished realistic and abstract designs made up of connected and intertwined curves crosscuts a number of related disciplines, including archaeology, art history, and heritage management. However, many objects, such as pottery sherds found in the archaeological record, 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 fragmentary cultural-heritage objects contain only a small portion of the underlying full design, 2) in the case of a stamping application, the same design may be applied multiple times with spatial overlap on one object, and 3) curve patterns detected on an object are usually incomplete and noisy. As a result, traditional curve-pattern matching algorithms, such as Chamfer matching, may perform poorly in identifying the underlying design. In this paper, we develop a new partial-to-global curve matching algorithm to address these challenges and better identify the full design from a fragmented cultural heritage object. Specifically, we develop the algorithm to identify the designs of the carved wooden paddles of the Southeastern Woodlands from unearthed pottery sherds. A set of pottery sherds, curated at Georgia Southern University, are used to test the proposed algorithm, with promising results.

Social Networks and Networked Scholars

“…More advanced methods such as this will probably prove fruitful in the future. Indeed, pattern recognition algorithms run on digital images (e.g., Zhou et al 2016) may eventually replace the laborious process of identifying design matches through comparison of sherds.…”

Curve-Structure Segmentation from Depth Maps: A CNN-based Approach and Its Application to Exploring Cultural Heritage Objects

“…For example, in (Smith et al 2010;Makridis and Daras 2012;Rasheed and Nordin 2015), various archaeological fragments are classified based on color and texture features. In (Zhou et al 2017), an extended Chamfer matching algorithms is developed to identify the design of a pottery sherd by matching the curve structures on the sherd to all the known designs, where the curve structures on the sherds are segmented with manual assistance. In this paper, we focus on accurate segmentation of curve structures on the surface of sherds, which is a fundamental step before the classification and matching.…”

“…This is a classical partial matching problem and the key component is the definition of a matching score or distance. In this paper, we use the classical Chamfer matching (Barrow et al 1977;Zhou et al 2017) for this purpose. As shown in Figure 5, we first thin both the segmented curve structures and the considered design into one-pixel wide skeletons and denote them as U and V , respectively.…”

“…Embellished designs on the surface of cultural heritage objects, such as pottery, shell, stone and wood contain important information for archaeologists (Zhou et al 2017). These designs, if successfully identified and correlated, can be used to build chronologies and track trade networks of a region thousands of years ago.…”

Exploring the Use of Computer Vision Metrics for Spatial Pattern Comparison

“…In the geographic analysis context, we consider spatial pattern comparison to be a specific geographic instance of a broader class of spatial pattern comparison problems which are prevalent across a wide range of disciplines. Many disciplines, from astronomy (Groth ; Makowiecki and Alda ) to archeology (Papaodysseus et al ; Zhou et al ), to brain imaging (McIntosh et al ; Luders, Cherbuin, and Gaser ), rely on techniques to quantify and compare spatial patterns. Some of these have been informed by geographic spatial analysis, while others have not.…”