Revisión de los métodos computerizados para la reconstrucción de fragmentos arqueológicos de cerámica

[ES] Las cerámicas son los hallazgos más numerosos encontrados en las excavaciones arqueológicas; a menudo se usan para obtener información sobre la historia, la economía y el arte de un sitio. Los arqueólogos rara vez encuentran jarrones completos; en general, están dañados y en fragmentos, a menudo mezclados con otros grupos de cerámica.El análisis y la reconstrucción de fragmentos se realiza por un operador experto mediante el uso del método manual tradicional. Los artículos revisados proporcionaron evidencias de que el método tradicional no es reproducible, no es repetible, consume mucho tiempo y sus resultados generan grandes incertidumbres. Con el objetivo de superar los límites anteriores, en los últimos años, los investigadores han realizado esfuerzos para desarrollar métodos informáticos que permitan el análisis de fragmentos arqueológicos de cerámica, todo ello destinado a su reconstrucción. Para contribuir a este campo de estudio, en este artículo, se presenta un análisis exhaustivo de las publicaciones disponibles más importantes hasta finales de 2019. Este estudio, centrado únicamente en fragmentos de cerámica, se realiza mediante la recopilación de artículos en inglés de la base de datos Scopus, utilizando las siguientes palabras clave: "métodos informáticos en arqueología", "arqueología 3D", "reconstrucción 3D", "reconocimiento y reconstrucción automática de características", "restauración de reliquias en forma de cerámica ". La lista se completa con referencias adicionales que se encuentran a través de la lectura de documentos seleccionados. Los 53 trabajos seleccionados se dividen en tres períodos de tiempo. Según una revisión detallada de los estudios realizados, los elementos clave de cada método analizado se enumeran en función de las herramientas de adquisición de datos, las características extraídas, los procesos de clasificación y las técnicas de correspondencia. Finalmente, para superar las brechas reales, se proponen algunas recomendaciones para futuras investigaciones.[EN] Potteries are the most numerous finds found in archaeological excavations; they are often used to get information about the history, economy, and art of a site. Archaeologists rarely find complete vases but, generally, damaged and in fragments, often mixed with other pottery groups. By using the traditional manual method, the analysis and reconstruction of sherds are performed by a skilled operator. Reviewed papers provided evidence that the traditional method is not reproducible, not repeatable, time-consuming and its results have great uncertainties. To overcome the aforementioned limits, in the last years, researchers have made efforts to develop computer-based methods for archaeological ceramic sherds analysis, aimed at their reconstruction. To contribute to this field of study, in this paper, a comprehensive analysis of the most important available publications until the end of 2019 is presented. This study, focused on pottery fragments only, is performed by collecting papers in English by the Scopus database using the following keywords: “computer methods in archaeology", "3D archaeology", "3D reconstruction", "automatic feature recognition and reconstruction", "restoration of pottery shape relics”. The list is completed by additional references found through the reading of selected papers. The 53 selected papers are divided into three periods of time. According to a detailed review of the performed studies, the key elements of each analyzed method are listed based on data acquisition tools, features extracted, classification processes, and matching techniques. Finally, to overcome the actual gaps some recommendations for future researches are proposed.Highlights:The traditional manual method for reassembling sherds is very time-consuming and costly; it also requires a great deal effort from skilled archaeologists in repetitive and routine activities.Computer-based methods for archaeological ceramic sherds reconstruction can help archaeologists in the above-mentioned repetitive and routine activities.In this paper, the state-of-the-art computer-based methods for archaeological ceramic sherds reconstruction are reviewed, and some recommendations for future researches are proposed.Eslami, D.; Di Angelo, L.; Di Stefano, P.; Pane, C. (2020). Review of computer-based methods for archaeological ceramic sherds reconstruction. 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3D Acquisition of Archaeological Ceramics and Web-Based 3D Data Storage

Motivated by the requirements of modern archaeology, we are developing an automated system for archaeological classification and reconstruction of ceramics. The goal is to create a tool that satisfies the criteria of accuracy, performance (findings/hour), robustness, transportability, overall costs, and careful handling of the findings. Following our previous work, we present new achievements on the documentation steps for 3D acquisition, 3D data processing, and 3D reconstruction. We have improved our system so that it can handle large quantities of ceramic fragments efficiently and computes a more robust orientation of a fragment. In order to store the sherd data acquired and hold all the information necessary to reconstruct a complete vessel, a database for archaeological fragments was developed. We will demonstrate practical experiments and results undertaken onsite at different excavations in Israel and Peru

An Open System for Collection and Automatic Recognition of Pottery through Neural Network Algorithms

In the last ten years, artificial intelligence (AI) techniques have been applied in archaeology. The ArchAIDE project realised an AI-based application to recognise archaeological pottery. Pottery is of paramount importance for understanding archaeological contexts. However, recognition of ceramics is still a manual, time-consuming activity, reliant on analogue catalogues. The project developed two complementary machine-learning tools to propose identifications based on images captured on-site, for optimising and economising this process, while retaining key decision points necessary to create trusted results. One method relies on the shape of a potsherd; the other is based on decorative features. For the shape-based recognition, a novel deep-learning architecture was employed, integrating shape information from points along the inner and outer profile of a sherd. The decoration classifier is based on relatively standard architectures used in image recognition. In both cases, training the algorithms meant facing challenges related to real-world archaeological data: the scarcity of labelled data; extreme imbalance between instances of different categories; and the need to take note of minute differentiating features. Finally, the creation of a desktop and mobile application that integrates the AI classifiers provides an easy-to-use interface for pottery classification and storing pottery data

Developing the ArchAIDE Application: A digital workflow for identifying, organising and sharing archaeological pottery using automated image recognition

Pottery is of fundamental importance for understanding archaeological contexts, facilitating the understanding of production, trade flows, and social interactions. Pottery characterisation and the classification of ceramics is still a manual process, reliant on analogue catalogues created by specialists, held in archives and libraries. The ArchAIDE project worked to streamline, optimise and economise the mundane aspects of these processes, using the latest automatic image recognition technology, while retaining key decision points necessary to create trusted results. Specifically, ArchAIDE worked to support classification and interpretation work (during both fieldwork and post-excavation analysis) with an innovative app for tablets and smartphones. This article summarises the work of this three-year project, funded by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement N.693548, with a consortium of partners representing both the academic and industry-led ICT (Information and Communications Technology) domains, and the academic and development-led archaeology domains. The collaborative work of the archaeological and technical partners created a pipeline where potsherds are photographed, their characteristics compared against a trained neural network, and the results returned with suggested matches from a comparative collection with typical pottery types and characteristics. Once the correct type is identified, all relevant information for that type is linked to the new sherd and stored within a database that can be shared online. ArchAIDE integrated a variety of novel and best-practice approaches, both in the creation of the app, and the communication of the project to a range of stakeholders

Automated analysis of pottery by QEM-EDS: A case study from Mansiri, Sulawesi

The analysis of raw materials and manufacturing techniques is central to the investigation of pottery assemblages. While various analytical techniques exists, petrography generally remains the go-to method to analyse the fabric of pottery. It combines relatively cheap and simple sample preparation protocol with the ability to yield very detailed information related to provenance and manufacturing technique. Here, we test the utility of performing QEM-EDS on archaeological pottery from the Mansiri site, Sulawesi, to complement petrographic observations. We identify the main non-plastic inclusions as plagioclase, quartz, calcic amphibole, iron oxides and volcanic rock fragments, consistent with the pottery being made locally. Quantitative analysis of inclusion size and direction suggests that the non-plastic inclusions were not manually added, and that in contrast to other Neolithic Sulawesi sites, coiling with beating/paddle and anvil was used to manufacture the pots

ArchAIDE-Archaeological Automatic Interpretation and Documentation of cEramics

The goals of H2020 project "ArchAIDE: are to support the classification and interpretation work of archaeologists with innovative computer-based tools, able to provide the user with features for the semi-automatic description and matching of potsherds over the huge existing ceramic catalogues. Pottery classification is of fundamental importance for the comprehension and dating of the archaeological contexts, and for understanding production, trade flows and social interactions, but it requires complex skills and it is a very time consuming activity, both for researchers and professionals. The aim of ArchAIDE is to support the work of archaeologists, in order to meet real user needs and generate economic benefits, reducing time and costs. This would create societal benefits from cultural heritage, improving access, re-use and exploitation of the digital cultural heritage in a sustainable way. These objectives will be achieved through the development of: - an as-automatic-as-possible procedure to transform the paper catalogues in a digital description, to be used as a data pool for search and retrieval process; - a tool (mainly designed for mobile devices) that will support archaeologists in recognizing and classifying potsherds during excavation and post-excavation analysis, through an easy-to-use interface and efficient algorithms for characterisation, search and retrieval of the visual/geometrical correspondences; - an automatic procedure to derive a complete potsherds identity card by transforming the data collected into a formatted electronic document, printable or visual; - a web-based real-time data visualisation to improve access to archaeological heritage and generate new understanding; - an open archive to allow the archival and re-use of archaeological data, transforming them into common heritage and permitting economic sustainability. Those tools will be tested and assessed on real-cases scenarios, paving the way to future exploitation