Documenting the State of Preservation of Historical Stone Sculptures in Three Dimensions with Digital Tools

Adamopoulos, Efstathios; Rinaudo, Fulvio

doi:10.1007/978-3-030-68796-0_48

Cited by 8 publications

(8 citation statements)

References 20 publications

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“…e research of Suciati et al [14] showed that when shooting, the two cameras shoot at the same time, imitating the perspective of the human eye, and during projection, the images captured by the two cameras are projected onto the same screen at the same time, and the principle of polarized light is used, So that each eye can only see the corresponding polarized light image; that is, the left eye can only see the image taken by the left camera, and the right eye can only see the image taken by the right camera, so that people feel the real three-dimensional scene. Adamopoulos et al [15] in computer vision systems and digital photogrammetry systems also simulate the way that human eyes observe the scene. e stereo vision method generally obtains two different perspective images of the same space scene by a stereo camera system and calculates the points in the space.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Sculpture 3D Modeling Method Based on Image Sequence

Liu

2021

Complexity

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This thesis first introduces the basic principles of model-based image sequence coding technology, then discusses in detail the specific steps in various implementation algorithms, and proposes a basic feature point calibration required in three-dimensional motion and structure estimation. This is a simple and effective solution. Aiming at the monocular video image sequence obtained by only one camera, this paper introduces the 3D model of the sculpture building into the pose tracking framework to provide initial depth information. The whole posture tracking framework can be divided into three parts, namely, the construction of the initial sculpture model, the posture tracking between frames, and the robustness processing during continuous tracking. In order to reduce the complexity of calculation, this paper proposes a new three-dimensional mesh model and a moving image restoration algorithm based on this model. At the same time, the influence of the intensity and direction factors in the scene is added, the simulation results are given, and the next step is discussed. The optimization work that needs to be done.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Sculpture 3D Modeling Method Based on Image Sequence

Liu

2021

Complexity

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“…[81]. Similarly, Adamopoulos et al (2021) projected a 2D unsupervised decay classification of stone monuments on 3D mesh models using near-infrared texture, with the possibility to differentiate healthier material from biodegradation, black crusts or stone patina [82]. Gong et al (2021) applied deep learning (edge-enhanced CNN-Mask R-CNN) on 2D images, with the first goal of object recognition in the main architectural elements of the Great Wall in China.…”

Section: Semi-automatized and Automatized Mappingmentioning

confidence: 99%

Condition Assessment of Heritage Buildings via Photogrammetry: A Scoping Review from the Perspective of Decision Makers

De Fino,

Galantucci,

Fatiguso

2023

Heritage

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In recent years, advanced digital technologies have driven an outstanding paradigm shift in the field of architectural heritage, particularly for building modelling, historical documentation and touristic promotion. Nonetheless, they show great potentialities in the field of assessment and control of the state of conservation of heritage buildings. In particular, close-range and aerial photogrammetry have increasingly relied on low-cost and user-friendly tools and procedures, with a high degree of automation that makes them accessible to specialists who are foremost involved in architectural diagnosis and conservation, rather than in remote sensing sciences. In this framework, this paper provides a scoping review of 117 publications, based on the PRISMA protocol, from Scopus and Web of Science databases, related to the employment of photogrammetric models and methods, with specific focus on the targets and purposes of the diagnostic process, including decay mapping, structural monitoring and modelling, non-destructive investigation and multi-source documentation. In detail, the results point out that current studies mainly support robust processing of large amounts of information from direct observation of surface alterations, systematic correlation between materials, construction characteristics, visible anomalies and experimental measurements, as well as multi-disciplinary collaborative workflows through remote inspection and harmonized data management. Further improvements were identified, including standardization of acquisition procedures, automatization of elaboration pipelines, integration of real-time data, validation of diagnosis decision-making support tools and scalability to networks of assets.

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“…The need for more efficient inspection [44] and intelligent identification of conservation needs [45] has led to the adoption of image processing approaches to generate the thematic data needed for deterioration mapping. Digital image processing (DIP) refers to the manipulation of the digital images to extract features and recognize patterns, which, after having acquired the suitable base-maps, can be performed with techniques as simple as thresholding, edge detection, or information reduction to obtain the required results [33,[46][47][48]. However, these approaches still largely depend on the human factor since many parameters have to be tuned differently for each application, and often deterioration patterns have to be identified and extracted one at a time.…”

Section: Digital Image Processingmentioning

confidence: 99%

“…Nevertheless, deep-learning implementations require large image datasets to be efficiently trained, which is often impractical for built heritage applications. They may also underperform considering the uniqueness of each heritage asset, many of which present a distinctive mixture Multiband and multispectral image segmentation for built heritage inspection purposes has been applied via a range of clustering algorithms, some of the most common being maximum-likelihood, minimum-distance, and k-means [36,37,39,48,[53][54][55][56]. However, most of the relevant works aim at segmenting the materials and elements of historic façades, and when deterioration is considered, it is determined as present or not present.…”

Section: Digital Image Processingmentioning

confidence: 99%

Learning-based classification of multispectral images for deterioration mapping of historic structures

Adamopoulos

2021

J Build Rehabil

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The conservation of historic structures requires detailed knowledge of their state of preservation. Documentation of deterioration makes it possible to identify risk factors and interpret weathering mechanisms. It is usually performed using non-destructive methods such as mapping of surface features. The automated mapping of deterioration is a direction not often explored, especially when the investigated architectural surfaces present a multitude of deterioration forms and consist of heterogeneous materials, which significantly complicates the generation of thematic decay maps. This work combines reflectance imaging and supervised segmentation, based on machine learning methods, to automatically segment deterioration patterns on multispectral image composites, using a weathered historic fortification as a case study. Several spectral band combinations and image classification techniques (regression, decision tree, and ensemble learning algorithmic implementations) are evaluated to propose an accurate approach. The automated thematic mapping facilitates the spatial and semantic description of the deterioration patterns. Furthermore, the utilization of low-cost photographic equipment and easily operable digital image processing software adds to the practicality and agility of the presented methodology.

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Documenting the State of Preservation of Historical Stone Sculptures in Three Dimensions with Digital Tools

Cited by 8 publications

References 20 publications

[Retracted] Sculpture 3D Modeling Method Based on Image Sequence

[Retracted] Sculpture 3D Modeling Method Based on Image Sequence

Condition Assessment of Heritage Buildings via Photogrammetry: A Scoping Review from the Perspective of Decision Makers

Learning-based classification of multispectral images for deterioration mapping of historic structures

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