Techniques for Mosaics Documentation Through Photogrammetry Data Acquisition. The Byzantine Mosaics of the Nativity Church

Doria, Elisabetta; Picchio, Francesca

doi:10.5194/isprs-annals-v-2-2020-965-2020

Cited by 4 publications

(4 citation statements)

References 7 publications

(3 reference statements)

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“…In order to reduce scale error, 21 Ground Control Points (GCP) with defined values x, y and z (Figure 6), were placed around the mosaic on the surface of the wall on which the mosaic is placed. The numbering of the points started in the bottom-left corner outside the mosaic and continued to the right in an anti-clockwise direction [33][34][35]. Each GCP was measured using Geodetic Total Station Leica TS09plus 5 ′′ (Leica Geosystems AG, Heerbrugg, Switzerland).…”

Section: Photogrammetrymentioning

confidence: 99%

Graphic Reconstruction of a Roman Mosaic with Scenes of the Abduction of Europa

Oštir,

Javoršek,

Stergar

et al. 2024

Applied Sciences

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This paper presents the reconstruction framework of the Roman mosaic with the central scene from the abduction of Europa. The mosaic depicting Europa, discovered in Ptuj (Slovenia) and dated from the second half of the third to the beginning of the fourth century AD, once decorated the representative room of a Roman villa. The experimental section addresses the materials and methods used in the 2D reconstruction of the mosaic, including the creation of line drawings of the mosaic based on the preserved part of the mosaic, photogrammetric acquisition, and the creation and processing of 1:1 raster reconstructions of the entire mosaic. This is followed by color management and interpretation approaches which allow the mosaic elements to be implemented in a 3D animation. The presented approaches could be implemented in the reconstruction process of other mosaics and archaeological objects with adaptations to the specifics of related objects.

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Section: Photogrammetrymentioning

confidence: 99%

Graphic Reconstruction of a Roman Mosaic with Scenes of the Abduction of Europa

Oštir,

Javoršek,

Stergar

et al. 2024

Applied Sciences

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“…With a particular focus on mosaics, most of the related computer applications deal with their digital reconstruction using image-based techniques (i.e., photogrammetry) for documentation and analysis [46][47][48][49]. Besides, literature presents a few examples of image processing applications: In [50], a registration method in the framework of a restoration process of a medieval mosaic to compare a historical black and white photograph with a current digital one is presented.…”

Section: Related Workmentioning

confidence: 99%

Classification of Geometric Forms in Mosaics Using Deep Neural Network

et al. 2021

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The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that autonomously quantifies the feature map and classifies it. Convolution, pooling and dense layers are three distinct categories of levels that generate attributes from the dataset images by introducing certain specified filters. As a case study, a Roman mosaic is considered, which is digitally reconstructed by close-range photogrammetry based on standard photos. During the digital transformation from a 2D perspective view of the mosaic into an orthophoto, each photo is rectified (i.e., it is an orthogonal projection of the real photo on the plane of the mosaic). Image samples of the geometric forms, e.g., triangles, squares, circles, octagons and leaves, even if they are partially deformed, were extracted from both the original and the rectified photos and originated the dataset for testing the CNN-based approach. The proposed method has proved to be robust enough to analyze the mosaic geometric forms, with an accuracy higher than 97%. Furthermore, the performance of the proposed method was compared with standard deep learning frameworks. Due to the promising results, this method can be applied to many other pattern identification problems related to artworks.

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“…In cultural and architectural heritage, digitalization is an important tool, and an effective way for documentation, preservation, interpretation (Adami et al, 2018;Doria and Picchio, 2020), and visualization (Kharroubi et al, 2019(Kharroubi et al, , 2020. It became necessary when dealing with rare and unique masterpieces as mosaics.…”

Section: Related Workmentioning

confidence: 99%

“…These are then assembled using mortar to obtain patterns or figures. As the majority of the mosaics of interest are very old, dating thousands of years (like the mosaic of Germigny-des-Prés which dates from the end of the 8-9th century), the digitization of these masterpieces permits to represents the base element for documentation, preservation, and valorisation (Adami et al, 2018;Ajioka and Hori, 2014;Doria and Picchio, 2020;Placa et al, 2020). In architecture, mosaics are used to decorate soils, walls, or ceilings, including non-flat surfaces which makes specific 3D capture techniques central to their description.…”

Section: Introductionmentioning

confidence: 99%

Tesserae3d: A Benchmark for Tesserae Semantic Segmentation in 3d Point Clouds

Kharroubi

Wersch

Billen

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 3D point cloud of mosaic tesserae is used by heritage researchers, restorers and archaeologists for digital investigations. Information extraction, pattern analysis and semantic assignment are necessary to complement the geometric information. Automated processes that can speed up the task are highly sought after, especially new supervised approaches. However, the availability of labelled data necessary for training supervised learning models is a significant constraint. This paper introduces Tesserae3D, a 3D point cloud benchmark dataset for training and evaluating machine learning models, applied to mosaic tesserae segmentation. It is a publicly available, very high density and coloured dataset, accompanied by a standard multi-class semantic segmentation baseline. It consists of about 502 million points and contains 11 semantic classes covering a wide range of tesserae types. We propose a semantic segmentation baseline building on radiometric and covariance features fed to ensemble learning methods. The results delineate an achievable 89% F1-score and are made available under https://github.com/akharroubi/Tesserae3D, providing a simple interface to improve the score based on feedback from the research community.

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Techniques for Mosaics Documentation Through Photogrammetry Data Acquisition. The Byzantine Mosaics of the Nativity Church

Cited by 4 publications

References 7 publications

Graphic Reconstruction of a Roman Mosaic with Scenes of the Abduction of Europa

Graphic Reconstruction of a Roman Mosaic with Scenes of the Abduction of Europa

Classification of Geometric Forms in Mosaics Using Deep Neural Network

Tesserae3d: A Benchmark for Tesserae Semantic Segmentation in 3d Point Clouds

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