Mo.Se.: Mosaic image segmentation based on deep cascading learning
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 studi...
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| Format: | Article |
| Language: | English |
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Universitat Politècnica de València
2021-01-01
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| Series: | Virtual Archaeology Review |
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| Online Access: | https://polipapers.upv.es/index.php/var/article/view/14179 |
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| author | Andrea Felicetti Marina Paolanti Primo Zingaretti Roberto Pierdicca Eva Savina Malinverni |
| author_facet | Andrea Felicetti Marina Paolanti Primo Zingaretti Roberto Pierdicca Eva Savina Malinverni |
| author_sort | Andrea Felicetti |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8ce70334191d4c6f9bb4dc954aabd1b1 |
| institution | DOAJ |
| issn | 1989-9947 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Universitat Politècnica de València |
| record_format | Article |
| series | Virtual Archaeology Review |
| spelling | doaj-art-8ce70334191d4c6f9bb4dc954aabd1b12025-08-20T02:51:04ZengUniversitat Politècnica de ValènciaVirtual Archaeology Review1989-99472021-01-011224253810.4995/var.2021.141798387Mo.Se.: Mosaic image segmentation based on deep cascading learningAndrea Felicetti0Marina Paolanti1Primo Zingaretti2Roberto Pierdicca3Eva Savina Malinverni4Università Politecnica delle MarcheUniversità Politecnica delle MarcheUniversità Politecnica delle MarcheUniversitá Politecnica delle MarcheUniversità Politecnica delle MarcheMosaic 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.https://polipapers.upv.es/index.php/var/article/view/14179cultural heritagemosaicdeep learningimage segmentationdigitization |
| spellingShingle | Andrea Felicetti Marina Paolanti Primo Zingaretti Roberto Pierdicca Eva Savina Malinverni Mo.Se.: Mosaic image segmentation based on deep cascading learning Virtual Archaeology Review cultural heritage mosaic deep learning image segmentation digitization |
| title | Mo.Se.: Mosaic image segmentation based on deep cascading learning |
| title_full | Mo.Se.: Mosaic image segmentation based on deep cascading learning |
| title_fullStr | Mo.Se.: Mosaic image segmentation based on deep cascading learning |
| title_full_unstemmed | Mo.Se.: Mosaic image segmentation based on deep cascading learning |
| title_short | Mo.Se.: Mosaic image segmentation based on deep cascading learning |
| title_sort | mo se mosaic image segmentation based on deep cascading learning |
| topic | cultural heritage mosaic deep learning image segmentation digitization |
| url | https://polipapers.upv.es/index.php/var/article/view/14179 |
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