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Authors: Gianfranco Fenu ; Eric Medvet ; Daniele Panfilo and Felice Pellegrino

Affiliation: Dipartimento di Ingegneria e Architettura, Università degli Studi di Trieste, Trieste, Italy

ISBN: 978-989-758-397-1

Keyword(s): Cultural Heritage, Computer Vision, Deep Learning, Convolutional Neural Networks.

Abstract: We consider the task of segmentation of images of mosaics, where the goal is to segment the image in such a way that each region corresponds exactly to one tile of the mosaic. We propose to use a recent deep learning technique based on a kind of convolutional neural networks, called U-net, that proved to be effective in segmentation tasks. Our method includes a preprocessing phase that allows to learn a U-net despite the scarcity of labeled data, which reflects the peculiarity of the task, in which manual annotation is, in general, costly. We experimentally evaluate our method and compare it against the few other methods for mosaic images segmentation using a set of performance indexes, previously proposed for this task, computed using 11 images of real mosaics. In our results, U-net compares favorably with previous methods. Interestingly, the considered methods make errors of different kinds, consistently with the fact that they are based on different assumptions and techniques. This finding suggests that combining different approaches might lead to an even more effective segmentation. (More)

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Paper citation in several formats:
Fenu, G.; Medvet, E.; Panfilo, D. and Pellegrino, Felice Andrea (2020). Mosaic Images Segmentation using U-net.In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 485-492. DOI: 10.5220/0008967404850492

@conference{icpram20,
author={Gianfranco Fenu. and Eric Medvet. and Daniele Panfilo. and Pellegrino, Felice Andrea},
title={Mosaic Images Segmentation using U-net},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={485-492},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008967404850492},
isbn={978-989-758-397-1},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Mosaic Images Segmentation using U-net
SN - 978-989-758-397-1
AU - Fenu, G.
AU - Medvet, E.
AU - Panfilo, D.
AU - Pellegrino, Felice Andrea
PY - 2020
SP - 485
EP - 492
DO - 10.5220/0008967404850492

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