Authors:
K. Dia
1
;
2
;
V. L. Coli
1
;
2
;
L. Blanc-Féraud
3
;
J. Leblond
2
;
L. Gomart
4
and
D. Binder
1
Affiliations:
1
University Côte d’Azur, CNRS, CEPAM, 06300 Nice, France
;
2
University Côte d’Azur, INRIA, Team Factas, B.P. 93, 06902 Sophia Antipolis Cedex, France
;
3
University Côte d’Azur, CNRS, INRIA, I3S Lab., Sophia Antipolis, France
;
4
University Paris 1 Panthéon-Sorbonne, CNRS-UMR 8215 Trajectoires, 92023 Nanterre, France
Keyword(s):
Machine Learning Algorithms, Convolution Neural Network, Support Vector Machine, Image Classification and Analysis, Hough Transform, Archaeology, Neolithic Pottery.
Abstract:
Archaeological studies involve more and more numerical data analyses. In this work, we are interested in the analysis and classification of ceramic sherds tomographic images in order to help archaeologists in learning about the fabrication processes of ancient pottery. More specifically, a particular manufacturing process (spiral patchwork) has recently been discovered in early Neolithic Mediterranean sites, along with a more traditional coiling technique. It has been shown that the ceramic pore distribution in available tomographic images of both archaeological and experimental samples can reveal which manufacturing technique was used. Indeed, with the spiral patchwork, the pores exhibit spiral-like behaviours, whereas with the traditional one, they are distributed along parallel lines, especially in the experimental samples. However, in archaeological samples, these distributions are very noisy, making analysis and discrimination hard to process. Here, we investigate how Learning M
ethods (Deep Learning and Support Vector Machine) can be used to answer these numerically difficult problems. In particular, we study how the results depend on the input data (either raw data at the output of the tomographic device, or after a preliminary pore segmentation step), and the quality of the information they could provide to archaeologists.
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