Mo.Se

Mosaic image segmentation based on deep cascading learning

Datos Bibliográficos

ID19547089
AutoresAndrea Felicetti (0000-0001-7793-1012, Marche Polytechnic University), Marina Paolanti (0000-0002-5523-7174, Marche Polytechnic University), Primo Zingaretti (0000-0002-5709-2159, Marche Polytechnic University), Roberto Pierdicca (0000-0002-9160-834X, Marche Polytechnic University), Eva Savina Malinverni (0000-0001-6582-2943, Marche Polytechnic University)
Año2021
Volumen12
Número24
Páginas25
Fecha de publicación2021-01-19
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaVirtual Archaeology Review (JOURNAL)
Identificadores de la revistaISSN: 1989-9947 • E-ISSN: 1989-9947
EditorialUniversitat Politecnica de Valencia (PUBLISHER)
DOI10.4995/var.2021.14179
OpenAlexW3097253141
IdiomaEN
Citas recibidas10
Referencias citadas27

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. Highlights: A Mo.Se. (Mosaic Segmentation) algorithm is described with the purpose to perform robust image segmentation to automatically detect tesserae in ancient mosaics. 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. Extensive experiments show that the proposed framework outperforms state-of-the-art methods with higher accuracy, even compared with publicly available datasets

Archaeology · Database · Geography · Mosaic · Orthophoto · Scalability · Segmentation · Workflow · 3D Surveying and Cultural Heritage · Advanced Image and Video Retrieval Techniques · Archaeological Research and Protection · Computer Science · Artificial Intelligence

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