The 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. 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.

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

We address the problem of the statistical description of 3D surfaces with the purpose of automatic classification and retrieval of archaeological potsherds. These are particularly interesting problems in archaeology, as pottery comprises a great volume of findings in archaeological excavations. Indeed, the analysis of potsherds brings relevant cues for understanding the culture of ancient groups. In particular, we develop a new local shape descriptor for 3D surfaces, called the histogram of spherical orientations (HoSO), which we use in combination with a bag-of-words approach to compute visual similarity between 3D surfaces. Given a point of interest on a 3D surface, its local shape descriptor (HoSO) captures the distribution of the spherical orientations of its neighboring points. In turn, those spherical orientations are computed with respect to the point of interest itself, both in the azimuth and the zenith axis. The proposed HoSO is invariant to scale transformations and highly robust to rotation and noise. In addition, it is efficient, as it only exploits the information of the position of the 3D points and disregards other types of information like faces or normals. We performed experiments on a set of 3D surfaces representing potsherds from the Teotihuacan civilization and further validations on a set of 3D models of generic objects. Our results show that our methodology is effective for describing 3D models and that it improves classification performance with respect to previous local descriptors.

Classification and Retrieval of Archaeological Potsherds Using Histograms of Spherical Orientations

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, classical 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 from the Snow Collection, curated at Georgia Southern University, are used to test the proposed algorithm, with promising results.

Identifying Designs from Incomplete, Fragmented Cultural Heritage Objects by Curve-Pattern Matching

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.

A Framework for Design Identification on Heritage Objects

Cultural relic objects got from archaeology are often incomplete fragments. Some old and classical designs are preserved in these objects’ surfaces. But the objects are generally incomplete and their design is only the partial of the full design. Since the sherds or fragment objects suffer from serious corrosion, the fragment designs are often obscure and do not enough to cover all complete designs. Obviously, manual matching is inefficient and practically impossible for some complicated partial to global matches. This paper presents a feature matching algorithm to overcome these difficulties. Firstly, the color image of sherd object is converted to gray image. Then we detect the edges and the feature curves of the design and get one-pixel-wide edges and curves. Gray-scale image will be enhanced and removed noises. Some evident missing curves will be added to and incorrect curves will be removed manually. The fast matching algorithm is used to exclude the impossible matching designs in the design image database. For any possible matching designs, we use the parallel image matching algorithm based on the Hausdorff distance. The matching process consists a translation and a rotation transform. We divide the translation t into a number of subsets which will be assigned to different processors to compute any rotation Hausdorff distance and match the image of fragment object. All processors will stop when any processor with successful matching result. The experiment on a set of fragment designs shows that the algorithm is efficient and better than traditional matching method.

A parallel matching algorithm for archaeological fragment object designs based on Hausdorff distance

We introduce the Tepalcatl project, an ongoing bi-disciplinary effort conducted by archaeologists and computer vision researchers, which focuses on developing statistical methods for the automatic categorization of potsherds; more precisely, potsherds from ancient Mexico including the Teotihuacan and Aztec civilizations. We captured 3D models of several potsherds, and annotated them using seven taxonomic criteria appropriate for categorization. Our first task consisted in exploiting the descriptive power of two state-of-the-art 3D descriptors. Then, we evaluated their retrieval and classification performance. Finally, we investigated the effects of dimensionality reduction for categorization of our data. Our results are promising and demonstrate the potential of computer vision techniques for archaeological classification of potsherds.

Estibaliz Aguayo-Ortiz

Papers

Categorization of Aztec Potsherds Using 3D Local Descriptors