Comparing network construction techniques in the context of local transport networks in the Dutch part of the Roman limes

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

From archaeological excavations, huge quantities of material are recovered, usually in the form of fragments. Their correct interpretation and classification are laborious and time-consuming and requires measurement, analysis and comparison of several items. Basing these activities on quantitative methods that process 3D digital data from experimental measurements allows optimizing the entire restoration process, making it faster, more accurate and cheaper. The 3D point clouds, captured by the scanning process, are raw data that must be properly processed to be used in automatic systems for the analysis of archeological finds. This paper focuses on the integration of a shape feature recognizer, able to support the semantic decomposition of the ancient artifact into archaeological features, with a structured database, able to query the large amount of information extracted. Through the automatic measurement of the dimensional attributes of the various features, it is possible to facilitate the comparative analyses between archaeological artifacts and the inferences of the archaeologist and to reduce the routine work. Here, a dedicated database has been proposed, able to store the information extracted from huge quantities of archaeological material using a specific shape feature recognizer. This information is useful for making comparisons but also to improve the archaeological knowledge. The database has been implemented and used for the identification of pottery fragments and the reconstruction of archaeological vessels. Reconstruction, in particular, often requires the solution of complex problems, especially when it involves types of potsherds that cannot be treated with traditional methods.

A 3D Informational Database for Automatic Archiving of Archaeological Pottery Finds.

SHREC 2021: Retrieval of cultural heritage objects

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

We propose a generalized 3D shape descriptor for the efficient classification of 3D archaeological artifacts. Our descriptor is based on a multi-view approach of curvature features, consisting of the following steps: pose normalization of 3D models, local curvature descriptor calculation, construction of 3D shape descriptor using the multi-view approach and curvature maps, and dimensionality reduction by random projections. We generate two descriptors from two different paradigms: 1) handcrafted, wherein the descriptor is manually designed for object feature extraction, and directly passed on to the classifier and 2) machine learnt, in which the descriptor automatically learns the object features through a pretrained deep neural network model (VGG-16) for transfer learning and passed on to the classifier. These descriptors are applied to two different archaeological datasets: 1) non-public Mexican dataset, represented by a collection of 963 3D archaeological objects from the Templo Mayor Museum in Mexico City that includes anthropomorphic sculptures, figurines, masks, ceramic vessels, and musical instruments; and 2) 3D pottery content-based retrieval benchmark dataset, consisting of 411 objects. Once the multi-view descriptors are obtained, we evaluate their effectiveness by using the following object classification schemes: $K$ 
-nearest neighbor, support vector machine, and structured support vector machine. Our object descriptors classification results are compared against five popular 3D descriptors in the literature, namely, rotation invariant spherical harmonic, histogram of spherical orientations, signature of histograms of orientations, symmetry descriptor, and reflective symmetry descriptor. Experimentally, we were able to verify that our machine learnt and handcrafted descriptors offer the best classification accuracy (20% better on average than comparative descriptors), independently of the classification methods. Our proposed descriptors are able to capture sufficient information to discern among different classes, concluding that it adequately characterizes the datasets.

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Classification of 3D Archaeological Objects Using Multi-View Curvature Structure Signatures

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.

Categorization of Aztec Potsherds Using 3D Local Descriptors

Relative Neighbourhood Networks for Archaeological Analysis

Background : As cultural institutions embark in projects oriented to digitise art and archaeological collections in three dimensions, the need for developing means to access the resulting 3D models has become imperative. Shape recognition techniques developed in the field of computer vision can help in this task. Methods : This paper describes the implementation of three shape descriptors, specifically shape distributions, reflective symmetry and spherical harmonics as part of the development of a search engine that retrieves 3D models from an archaeological database without the need of using keywords as query criteria. Use case: The usefulness of this system is obvious in the context of cultural heritage museums, where it is essential to provide automatic access to archaeological and art collections. The prototype described in this paper uses, as study case, 3D models of archaeological objects belonging to Museo del Templo Mayor, a Mexican institution that preserves one of the largest collections of Aztec cultural heritage. Conclusions : This work is part of an ongoing project focused on creating generic methodologies and user-friendly computational tools for shape analysis for the benefit of scholars and students interested in describing, interpreting and disseminating new knowledge about the morphology of cultural objects

Application of computer vision techniques for 3D matching and retrieval of archaeological objects [version 1; peer review: 1 approved with reservations]

Diego Jimenez-Badillo

Papers

Classification and Retrieval of Archaeological Potsherds Using Histograms of Spherical Orientations

Classification of 3D Archaeological Objects Using Multi-View Curvature Structure Signatures

Categorization of Aztec Potsherds Using 3D Local Descriptors

Relative Neighbourhood Networks for Archaeological Analysis

Application of computer vision techniques for 3D matching and retrieval of archaeological objects [version 1; peer review: 1 approved with reservations]