loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.192.15.251

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Dia, K.; Coli, V.; Blanc-Féraud, L.; Leblond, J.; Gomart, L. and Binder, D. (2021). Applications of Learning Methods to Imaging Issues in Archaeology, Regarding Ancient Ceramic Manufacturing. In Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA; ISBN 978-989-758-526-5; ISSN 2184-9277, SciTePress, pages 109-116. DOI: 10.5220/0010519101090116

@conference{delta21,
author={K. Dia. and V. L. Coli. and L. Blanc{-}Féraud. and J. Leblond. and L. Gomart. and D. Binder.},
title={Applications of Learning Methods to Imaging Issues in Archaeology, Regarding Ancient Ceramic Manufacturing},
booktitle={Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA},
year={2021},
pages={109-116},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010519101090116},
isbn={978-989-758-526-5},
issn={2184-9277},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA
TI - Applications of Learning Methods to Imaging Issues in Archaeology, Regarding Ancient Ceramic Manufacturing
SN - 978-989-758-526-5
IS - 2184-9277
AU - Dia, K.
AU - Coli, V.
AU - Blanc-Féraud, L.
AU - Leblond, J.
AU - Gomart, L.
AU - Binder, D.
PY - 2021
SP - 109
EP - 116
DO - 10.5220/0010519101090116
PB - SciTePress