Tessella-oriented segmentation and guidelines estimation of ancient mosaic images

Digital 3D mosaics generation is a current trend of NPR (Non Photorealistic Rendering) field; in this demo we present an interactive system realized in JAVA where the user can simulate ancient mosaic in a 3D environment starting for any input image. Different simulation engines able to render the so-called "Opus Musivum" and "Opus Vermiculatum" are employed. Different parameters can be dynamically adjusted to obtain very impressive results.

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“…• the usage of methods able to infer from high quality picture of real ancient mosaics the underlying structure or original schema [6];…”

Section: Final Discussionmentioning

confidence: 99%

3D ancient mosaics

Battiato

Puglisi

2010

Proceedings of the 18th ACM International Conference on Multimedia

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“…Another important task is the automatic detection of tesserae. To the best of our knowledge, a single mosaicoriented segmentation algorithm has been proposed in the literature (Youssef & Derrode, 2008), that is based on the well-known watershed algorithm (Vincent & Soille, 1991) and some mosaic-specific pre-processing steps.…”

Section: Related Workmentioning

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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Segmentation of Mosaic Images Based on Deformable Models Using Genetic Algorithms

Bartoli

Fenu

Medvet

et al. 2017

Smart Objects and Technologies for Social Good

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Tessella-oriented segmentation and guidelines estimation of ancient mosaic images

Cited by 6 publications

References 16 publications

3D ancient mosaics

3D ancient mosaics

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

Segmentation of Mosaic Images Based on Deformable Models Using Genetic Algorithms

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