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

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

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dc.contributor.author Felicetti, Andrea es_ES
dc.contributor.author Paolanti, Marina es_ES
dc.contributor.author Zingaretti, Primo es_ES
dc.contributor.author Pierdicca, Roberto es_ES
dc.contributor.author Malinverni, Eva Savina es_ES
dc.date.accessioned 2021-02-16T11:28:46Z
dc.date.available 2021-02-16T11:28:46Z
dc.date.issued 2021-01-19
dc.identifier.uri http://hdl.handle.net/10251/161421
dc.description.abstract [EN] 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. es_ES
dc.description.abstract [ES] El mosaico es un tipo de arte antiguo utilizado para crear imágenes decorativas o patrones de pequeños componentes. Una versión digital de un mosaico puede ser útil a los arqueólogos, estudiosos y restauradores que están interesados en el estudio, la comparación y la preservación de los mosaicos. Hoy en día, los arqueólogos basan sus estudios principalmente en la operación manual y la observación visual que, aunque sigue siendo fundamental, debe ser apoyada con la ayuda de un procedimiento automatizado de extracción de la información. En este contexto, esta investigación tiene la intención de superar el procedimiento manual y lento del dibujo de teselas en mosaico proponiendo Mo.Se. (Mosaic Segmentation), un algoritmo que explota técnicas de aprendizaje profundo y segmentación de imagen; específicamente, la metodología combina la red U-Net 3 con el algoritmo Watershed. El propósito final es definir un flujo de trabajo que establezca los pasos para realizar una segmentación robusta y obtener una representación digital (vectorial) de un mosaico. Se presenta el procedimiento detallado y se proporcionan justificaciones teóricas, construyendo varias conexiones con otros modelos, haciendo que el flujo de trabajo sea teóricamente valioso y prácticamente escalable en conjuntos de datos medianos o grandes. El proceso de segmentación automática se probó con la ortoimagen de alta resolución de un mosaico antiguo, siguiendo un procedimiento de fotogrametría de objeto cercano. Nuestro enfoque se ha probado en el pavimento de la Iglesia de San Esteban en Umm ar-Rasas, un sitio arqueológico de Jordania, ubicado a 30 km al sureste de la ciudad de Madaba (Jordania). Los resultados experimentales muestran que este marco generalizado produce buenos rendimientos, obteniendo una mayor precisión en comparación con otros enfoques de vanguardia. Mo.Se. se ha validado utilizando conjuntos de datos disponibles públicamente como punto de referencia, lo que demuestra que la combinación de métodos basadosen el aprendizaje con métodos procedimentales mejora el rendimiento de la segmentación en casi un 10% en términos de exactitud en general. El ambicioso objetivo de este estudio es proporcionar a los arqueólogos una herramienta que acelere su trabajo de extracción automática de mosaicos geométricos antiguos. es_ES
dc.description.sponsorship This work was partially found within the framework of the project Innovative technologies and training activities for the conservation and enhancement of the archaeological site of Umm er-Rasas (Jordan) funded by Ministero degli Affari Esteri e della Cooperazione Internazionale. The authors would like to express their gratitude to the ISPC CNR and in particular to Dott. Roberto Gabrielli (project leader) and Alessandra Albiero for providing the dataset. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Virtual Archaeology Review es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Cultural heritage es_ES
dc.subject Mosaic es_ES
dc.subject Deep learning es_ES
dc.subject Image segmentation es_ES
dc.subject Digitization es_ES
dc.subject Patrimonio cultural es_ES
dc.subject Mosaico es_ES
dc.subject Aprendizaje profundo es_ES
dc.subject Segmentación de imagen es_ES
dc.subject Digitalización es_ES
dc.title Mo.Se.: Mosaic image segmentation based on deep cascading learning es_ES
dc.title.alternative Mo.Se.: Segmentación de mosaico de imágenes basado en aprendizaje profundo en cascada es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/var.2021.14179
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Felicetti, A.; Paolanti, M.; Zingaretti, P.; Pierdicca, R.; Malinverni, ES. (2021). Mo.Se.: Mosaic image segmentation based on deep cascading learning. Virtual Archaeology Review. 12(24):25-38. https://doi.org/10.4995/var.2021.14179 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/var.2021.14179 es_ES
dc.description.upvformatpinicio 25 es_ES
dc.description.upvformatpfin 38 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 12 es_ES
dc.description.issue 24 es_ES
dc.identifier.eissn 1989-9947
dc.relation.pasarela OJS\14179 es_ES
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