Paper on the identification of pottery forming based on statistical surface analysis was published in the Archaeological and Anthropological Science.

Abstract:

This article explores the possibilities of distinguishing different pottery forming methods utilising rotational movement based on a statistical analysis of the surface topography and variations in wall thickness. The presented topographic analysis is based on calculation of the surface regularity that is approached as measurement of the difference between the 3D representation of the pottery surface and the corresponding ideal vessel shape, obtained by rotating a model profile around the rotational axis. These differences are expressed using basic surface roughness parameters. In addition, analysis of wall thickness variability and the overall shape of the horizontal sections using elliptic Fourier analysis (EFA) were performed. The study was based on a pilot experimental dataset of vessels made using three forming methods: coiling in combination with wheel finishing employed using a turntable and using a potter’s wheel and wheel throwing. The results show that, with an increasing contribution of rotational movement in the forming sequence, a gradual increase in the regularity of vessel shapes and a decrease in wall thickness variability are observed. The differences in these two parameters allow us to distinguish among the studied forming methods. Automatic classification using elliptic Fourier analysis and support vector machine (SVM) indicates reliable classification for the lower parts of the experimental vessels.

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Paper on the discrimination of pottery surface treatment by Deep Learning was published in the Archaeological and Anthropological Science.

Abstract:

The study of pottery surface treatment is essential to understand techniques used by ancient potters, in order to explore the cultural and economic organisation of past societies. Pottery is one of the most abundant materials found in archaeological excavation, yet classifcation of pottery surface treatments remains challenging. The goal of this study is to propose a workfow to classify pottery surface treatments automatically, based on the extraction of images depicting surface geometry, calculated from 3D models. These images are then classifed by Deep Learning. Three Convolutional Neural Network algorithms (VGG16 and VGG19 transfer learning, and a custom network) are quantitatively evaluated on an experimental dataset of 48 wheel-thrown vessels, created by a professional potter specifcally for this study. To demonstrate workfow feasibility, six diferent surface treatments were applied to each vessel. Results obtained for all three classifers (accuracy of 93 to 95%) surpass other state-of-the-art quantitative approaches proposed for pottery classifcation. The workfow is able to take into account the entire surface of the pottery, not only a pre-selected spatially limited area.

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Paper on the documentation of carved stones was published in the Journal of Cultural Heritages.

Abstract: Revealing carved parts in rock art is of primary importance and remains a major challenge for archaeological documentation. Computational geometry applied to 3D imaging provides a unique opportunity todocument rock art. This study evaluates five algorithms and derivatives used to compute ambient occlusion and sky visibility on 3D models of Mongolian stelae, also known as deer stones. By contrast withthe previous companion work, models are processed directly in 3D, without preliminary projection. Volumetric obscurance gives the best results for the identification of carved figures. The effects of modelresolution and parameters specific to ambient occlusion are then evaluated. Keeping tridimensional information intact allows accurate measurement of distance, volume, and depth. Objects augmented by ambient occlusion can easily be manipulated in 3D viewers, for seamless and effortless access tothe overall organization of the figures, at the scale of the entire object. Qualitatively speaking, the 2D projected outputs are equivalent to, or even better than, existing archaeological documentation. The proposed workflow should be easily applicable in many situations, particularly as the functions providedfor the free R programming software are fully configurable.

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Paper on the semi-automatic classification of pottery fragments was published in the Journal of Archaeological Science: Reports.

Abstract: Archaeologists working with pottery spend a considerable amount of time on a fundamental task – providing precise descriptions of pottery fragments. This study presents a survey of existing computational solutions to identify the best matches for a given fragment, based on its shape. Four methods (ICP, DCT, RDP, and RTC) are compared, using a pottery dataset from Graufesenque (southern France), dated to the Roman Period. The first three methods produced successful and very similar results for rim fragments (within the five best candidates for 95% of the dataset). The ICP algorithm produced the best overall results for rim fragments, and can also be used for non-rim fragments. A practical computer application, including all the above methods, was developed in R programming language, with an easy-to-use graphical interface, and is now made freely available to the archaeological community for future studies, and further development.

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Presentations for the course of “3D scanning and modelling in archaeology” which will be held this spring at the University of Hradec Kralove have been added to teaching materials.

The aim of the course is to present basic methods, principles and devices used for 3D acquisition of archaeological artefacts and ecofacts. The course will consist of both theoretical and practical parts. The theoretical part will focus on a brief presentation of 3D acquisition methods (e.g. laser triangulation scanners, structured light, computed tomography, photogrammetry). The practical part will consist of scanning of real-world artefacts.

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Presentations and datasets for the cours of “Geometric morphometrics methods in archaeology” have been added to teaching materials. They were created within the context of the EU Grant of University of Hradec Králové (“Strategický rozvoj Univerzity Hradec Králové”, CZ.02.2.69/0.0/0.0/16 015/0002427).

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The new course – Iron Age in Europe – an extension to one given by Tomáš Mangel, will take place at the Departement of archaeology at the University of Hradec Králové in spring 2019.

The aim of this course is to present and discuss new discoveries and current research topics in the Iron Age archaeology in the European context. Students will be introduced into the issues linked with the emergence of European archaeological/cultural entities, throughout their social and economic development and long-distance relations, until their final destabilisation and/or transformations. The main part of the course will deal with issues linked with the complexity and biases of existing methodological approaches of funerary and settlement areas and will point out to some new methodologies and perspectives of their application for the modern archaeological inquiries. This theoretical background will be complemented by the presentation of several recent case-studies intended to investigate the problematics.

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