HeritageGO (HeGO)

Fontanella, Francesco; Molinara, Mario; Gallozzi, Arturo; Cigola, Michela; Senatore, Luca James; Florio, Riccardo; Clini, Paolo; D'Amico, Flavio Celis

doi:10.1145/3314183.3323863

Cited by 6 publications

(1 citation statement)

References 5 publications

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“…Moreover, for restorers, a digital model of a mosaic could become an essential professional tool. Researchers are thus supplied with highly reliable models, so to have new reading keys to study monuments (Fontanella et al, 2019). Furthermore, it serves as a record of their state-ofconservation and, at the same time, as a method by which they can be preserved (Bourke, 2014).…”

Section: Introductionmentioning

confidence: 99%

Mo.Se.: Mosaic image segmentation based on deep cascading learning

Felicetti

Paolanti

Zingaretti

et al. 2021

Virtual archaeol. rev.

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p class="VARAbstract">Mosaic is an ancient type of art used to create decorative images or patterns combining small components. A digital version of a mosaic can be useful for archaeologists, scholars and restorers who are interested in studying, comparing and preserving mosaics. Nowadays, archaeologists base their studies mainly on manual operation and visual observation that, although still fundamental, should be supported by an automatized procedure of information extraction. In this context, this research explains improvements which can change the manual and time-consuming procedure of mosaic tesserae drawing. More specifically, this paper analyses the advantages of using Mo.Se. (Mosaic Segmentation), an algorithm that exploits deep learning and image segmentation techniques; the methodology combines U-Net 3 Network with the Watershed algorithm. The final purpose is to define a workflow which establishes the steps to perform a robust segmentation and obtain a digital (vector) representation of a mosaic. The detailed approach is presented, and theoretical justifications are provided, building various connections with other models, thus making the workflow both theoretically valuable and practically scalable for medium or large datasets. The automatic segmentation process was tested with the high-resolution orthoimage of an ancient mosaic by following a close-range photogrammetry procedure. Our approach has been tested in the pavement of St. Stephen's Church in Umm ar-Rasas, a Jordan archaeological site, located 30 km southeast of the city of Madaba (Jordan). Experimental results show that this generalized framework yields good performances, obtaining higher accuracy compared with other state-of-the-art approaches. Mo.Se. has been validated using publicly available datasets as a benchmark, demonstrating that the combination of learning-based methods with procedural ones enhances segmentation performance in terms of overall accuracy, which is almost 10% higher. This study’s ambitious aim is to provide archaeologists with a tool which accelerates their work of automatically extracting ancient geometric mosaics.</p><p><strong>Highlights:</strong></p><ul><li><p>A Mo.Se. (Mosaic Segmentation) algorithm is described with the purpose to perform robust image segmentation to automatically detect tesserae in ancient mosaics.</p></li><li><p>This research aims to overcome manual and time-consuming procedure of tesserae segmentation by proposing an approach that uses deep learning and image processing techniques, obtaining a digital replica of a mosaic.</p></li><li><p>Extensive experiments show that the proposed framework outperforms state-of-the-art methods with higher accuracy, even compared with publicly available datasets.</p></li></ul></div></div></div>

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Section: Introductionmentioning

confidence: 99%

Mo.Se.: Mosaic image segmentation based on deep cascading learning

Felicetti

Paolanti

Zingaretti

et al. 2021

Virtual archaeol. rev.

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UMAP PATCH 2019 Chairs' Welcome

Ardissono¹,

Gena²,

Kuflik

et al. 2019

Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization

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HeGO, a Social Game as a Tool for Cultural Heritage Valorization

Fontanella

Molinara

Gallozzi

et al. 2021

J. Comput. Cult. Herit.

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It has been shown that digital games can help people, especially young people, get the most from cultural heritage. Successful usage of these games includes teaching and learning, as well as virtual reality tools used to simulate historical sites and events. They have also been shown to be a powerful and effective tool to make cultural heritage more attractive, especially for those sites that are less known and visited. In this article, we present HeGO, a digital platform for cultural heritage. It allows photos from cultural sites taken by simple users to be sent and stored on a web server. Then the collected photos can be used for an accurate 3D reconstruction of the site of interest. The HeGO platform can be used to implement a social media game, where players get a score for each photo they take and send to the HeGO web server. We also present the case study of the Atina historical center, where we simulated the usage of the HeGO platform for the 3D reconstruction of portions of the urban fabric of its historical center. The results obtained showed that the HeGO platform obtains good 3D reconstructions, whose quality is comparable with those of the more expensive laser scanner technique.

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HeritageGO (HeGO)

Cited by 6 publications

References 5 publications

Mo.Se.: Mosaic image segmentation based on deep cascading learning

Mo.Se.: Mosaic image segmentation based on deep cascading learning

UMAP PATCH 2019 Chairs' Welcome

HeGO, a Social Game as a Tool for Cultural Heritage Valorization

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