Journal of Imaging

Editorial

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3 pages, 166 KiB

Open AccessEditorial

Computer Vision and Robotics for Cultural Heritage: Theory and Applications

by Guillaume Caron, Olga Regina Pereira Bellon and Ilan Shimshoni

J. Imaging 2023, 9(1), 9; https://doi.org/10.3390/jimaging9010009 - 30 Dec 2022

Viewed by 1625

Abstract

Computer vision and robotics are more and more involved in cultural heritage [...] Full article

(This article belongs to the Special Issue Computer Vision and Robotics for Cultural Heritage: Theory and Applications)

Research

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11 pages, 30941 KiB

Open AccessArticle

LightBot: A Multi-Light Position Robotic Acquisition System for Adaptive Capturing of Cultural Heritage Surfaces

by Ramamoorthy Luxman, Yuly Emilia Castro, Hermine Chatoux, Marvin Nurit, Amalia Siatou, Gaëtan Le Goïc, Laura Brambilla, Christian Degrigny, Franck Marzani and Alamin Mansouri

J. Imaging 2022, 8(5), 134; https://doi.org/10.3390/jimaging8050134 - 12 May 2022

Cited by 8 | Viewed by 2026

Abstract

Multi-light acquisitions and modeling are well-studied techniques for characterizing surface geometry, widely used in the cultural heritage field. Current systems that are used to perform this kind of acquisition are mainly free-form or dome-based. Both of them have constraints in terms of reproducibility, [...] Read more.

Multi-light acquisitions and modeling are well-studied techniques for characterizing surface geometry, widely used in the cultural heritage field. Current systems that are used to perform this kind of acquisition are mainly free-form or dome-based. Both of them have constraints in terms of reproducibility, limitations on the size of objects being acquired, speed, and portability. This paper presents a novel robotic arm-based system design, which we call LightBot, as well as its applications in reflectance transformation imaging (RTI) in particular. The proposed model alleviates some of the limitations observed in the case of free-form or dome-based systems. It allows the automation and reproducibility of one or a series of acquisitions adapting to a given surface in two-dimensional space. Full article

(This article belongs to the Special Issue Computer Vision and Robotics for Cultural Heritage: Theory and Applications)

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14 pages, 14670 KiB

Open AccessArticle

Few-Shot Object Detection: Application to Medieval Musicological Studies

by Bekkouch Imad Eddine Ibrahim, Victoria Eyharabide, Valérie Le Page and Frédéric Billiet

J. Imaging 2022, 8(2), 18; https://doi.org/10.3390/jimaging8020018 - 19 Jan 2022

Cited by 6 | Viewed by 3010

Abstract

Detecting objects with a small representation in images is a challenging task, especially when the style of the images is very different from recent photos, which is the case for cultural heritage datasets. This problem is commonly known as few-shot object detection and [...] Read more.

Detecting objects with a small representation in images is a challenging task, especially when the style of the images is very different from recent photos, which is the case for cultural heritage datasets. This problem is commonly known as few-shot object detection and is still a new field of research. This article presents a simple and effective method for black box few-shot object detection that works with all the current state-of-the-art object detection models. We also present a new dataset called MMSD for medieval musicological studies that contains five classes and 693 samples, manually annotated by a group of musicology experts. Due to the significant diversity of styles and considerable disparities between the artistic representations of the objects, our dataset is more challenging than the current standards. We evaluate our method on YOLOv4 (m/s), (Mask/Faster) RCNN, and ViT/Swin-t. We present two methods of benchmarking these models based on the overall data size and the worst-case scenario for object detection. The experimental results show that our method always improves object detector results compared to traditional transfer learning, regardless of the underlying architecture. Full article

(This article belongs to the Special Issue Computer Vision and Robotics for Cultural Heritage: Theory and Applications)

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18 pages, 4848 KiB

Open AccessFeature PaperArticle

Historic Timber Roof Structure Reconstruction through Automated Analysis of Point Clouds

by Taşkın Özkan, Norbert Pfeifer, Gudrun Styhler-Aydın, Georg Hochreiner, Ulrike Herbig and Marina Döring-Williams

J. Imaging 2022, 8(1), 10; https://doi.org/10.3390/jimaging8010010 - 13 Jan 2022

Cited by 3 | Viewed by 2930

Abstract

We present a set of methods to improve the automation of the parametric 3D modeling of historic roof structures using terrestrial laser scanning (TLS) point clouds. The final product of the TLS point clouds consist of 3D representation of all objects, which were [...] Read more.

We present a set of methods to improve the automation of the parametric 3D modeling of historic roof structures using terrestrial laser scanning (TLS) point clouds. The final product of the TLS point clouds consist of 3D representation of all objects, which were visible during the scanning, including structural elements, wooden walking ways and rails, roof cover and the ground; thus, a new method was applied to detect and exclude the roof cover points. On the interior roof points, a region-growing segmentation-based beam side face searching approach was extended with an additional method that splits complex segments into linear sub-segments. The presented workflow was conducted on an entire historic roof structure. The main target is to increase the automation of the modeling in the context of completeness. The number of manually counted beams served as reference to define a completeness ratio for results of automatically modeling beams. The analysis shows that this approach could increase the quantitative completeness of the full automatically generated 3D model of the roof structure from 29% to 63%. Full article

(This article belongs to the Special Issue Computer Vision and Robotics for Cultural Heritage: Theory and Applications)

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13 pages, 16300 KiB

Open AccessArticle

HTR for Greek Historical Handwritten Documents

by Lazaros Tsochatzidis, Symeon Symeonidis, Alexandros Papazoglou and Ioannis Pratikakis

J. Imaging 2021, 7(12), 260; https://doi.org/10.3390/jimaging7120260 - 02 Dec 2021

Cited by 7 | Viewed by 2666

Abstract

Offline handwritten text recognition (HTR) for historical documents aims for effective transcription by addressing challenges that originate from the low quality of manuscripts under study as well as from several particularities which are related to the historical period of writing. In this paper, [...] Read more.

Offline handwritten text recognition (HTR) for historical documents aims for effective transcription by addressing challenges that originate from the low quality of manuscripts under study as well as from several particularities which are related to the historical period of writing. In this paper, the challenge in HTR is related to a focused goal of the transcription of Greek historical manuscripts that contain several particularities. To this end, in this paper, a convolutional recurrent neural network architecture is proposed that comprises octave convolution and recurrent units which use effective gated mechanisms. The proposed architecture has been evaluated on three newly created collections from Greek historical handwritten documents that will be made publicly available for research purposes as well as on standard datasets like IAM and RIMES. For evaluation we perform a concise study which shows that compared to state of the art architectures, the proposed one deals effectively with the challenging Greek historical manuscripts. Full article

(This article belongs to the Special Issue Computer Vision and Robotics for Cultural Heritage: Theory and Applications)

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12 pages, 2569 KiB

Open AccessArticle

Revealing Hidden Features in Multilayered Artworks by Means of an Epi-Illumination Photoacoustic Imaging System

by George J. Tserevelakis, Antonina Chaban, Evgenia Klironomou, Kristalia Melessanaki, Jana Striova and Giannis Zacharakis

J. Imaging 2021, 7(9), 183; https://doi.org/10.3390/jimaging7090183 - 10 Sep 2021

Cited by 8 | Viewed by 2758

Abstract

Photoacoustic imaging is a novel, rapidly expanding technique, which has recently found several applications in artwork diagnostics, including the uncovering of hidden layers in paintings and multilayered documents, as well as the thickness measurement of optically turbid paint layers with high accuracy. However, [...] Read more.

Photoacoustic imaging is a novel, rapidly expanding technique, which has recently found several applications in artwork diagnostics, including the uncovering of hidden layers in paintings and multilayered documents, as well as the thickness measurement of optically turbid paint layers with high accuracy. However, thus far, all the presented photoacoustic-based imaging technologies dedicated to such measurements have been strictly limited to thin objects due to the detection of signals in transmission geometry. Unavoidably, this issue restricts seriously the applicability of the imaging method, hindering investigations over a wide range of cultural heritage objects with diverse geometrical and structural features. Here, we present an epi-illumination photoacoustic apparatus for diagnosis in heritage science, which integrates laser excitation and respective signal detection on one side, aiming to provide universal information in objects of arbitrary thickness and shape. To evaluate the capabilities of the developed system, we imaged thickly painted mock-ups, in an attempt to reveal hidden graphite layers covered by various optically turbid paints, and compared the measurements with standard near-infrared (NIR) imaging. The obtained results prove that photoacoustic signals reveal underlying sketches with up to 8 times improved contrast, thus paving the way for more relevant applications in the field. Full article

(This article belongs to the Special Issue Computer Vision and Robotics for Cultural Heritage: Theory and Applications)

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20 pages, 10112 KiB

Open AccessArticle

Documenting Paintings with Gigapixel Photography

by Pedro M. Cabezos-Bernal, Pablo Rodriguez-Navarro and Teresa Gil-Piqueras

J. Imaging 2021, 7(8), 156; https://doi.org/10.3390/jimaging7080156 - 21 Aug 2021

Cited by 10 | Viewed by 2817

Abstract

Digital photographic capture of pictorial artworks with gigapixel resolution (around 1000 megapixels or greater) is a novel technique that is beginning to be used by some important international museums as a means of documentation, analysis, and dissemination of their masterpieces. This line of [...] Read more.

Digital photographic capture of pictorial artworks with gigapixel resolution (around 1000 megapixels or greater) is a novel technique that is beginning to be used by some important international museums as a means of documentation, analysis, and dissemination of their masterpieces. This line of research is extremely interesting, not only for art curators and scholars but also for the general public. The results can be disseminated through online virtual museum displays, offering a detailed interactive visualization. These virtual visualizations allow the viewer to delve into the artwork in such a way that it is possible to zoom in and observe those details, which would be negligible to the naked eye in a real visit. Therefore, this kind of virtual visualization using gigapixel images has become an essential tool to enhance cultural heritage and to make it accessible to everyone. Since today’s professional digital cameras provide images of around 40 megapixels, obtaining gigapixel images requires some special capture and editing techniques. This article describes a series of photographic methodologies and equipment, developed by the team of researchers, that have been put into practice to achieve a very high level of detail and chromatic fidelity, in the documentation and dissemination of pictorial artworks. The result of this research work consisted in the gigapixel documentation of several masterpieces of the Museo de Bellas Artes of Valencia, one of the main art galleries in Spain. The results will be disseminated through the Internet, as will be shown with some examples. Full article

(This article belongs to the Special Issue Computer Vision and Robotics for Cultural Heritage: Theory and Applications)

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18 pages, 8763 KiB

Open AccessTutorial

Use of Low-Cost Spherical Cameras for the Digitisation of Cultural Heritage Structures into 3D Point Clouds

by Sorin Herban, Domenica Costantino, Vincenzo Saverio Alfio and Massimiliano Pepe

J. Imaging 2022, 8(1), 13; https://doi.org/10.3390/jimaging8010013 - 17 Jan 2022

Cited by 22 | Viewed by 3518

Abstract

The digitization of Cultural Heritage is an important activity for the protection, management, and conservation of structures of particular historical and architectural interest. In this context, the use of low-cost sensors, especially in the photogrammetric field, represents a major research challenge. In this [...] Read more.

The digitization of Cultural Heritage is an important activity for the protection, management, and conservation of structures of particular historical and architectural interest. In this context, the use of low-cost sensors, especially in the photogrammetric field, represents a major research challenge. In this paper, the use of cameras capable of capturing a 360° scene with a single image was assessed. By using spherical photogrammetry and the algorithm based on the structure from motion and multi-view stereo, it is possible to reconstruct the geometry (point cloud) of an object or structure. In particular, for this experiment, the Ricoh theta SC2 camera was used. The analysis was conducted on two sites: one in the laboratory and another directly in the field for the digitization of a large structure (Colonada in Buziaș, Romania). In the case study of the laboratory, several tests were carried out to identify the best strategy for reconstructing the 3D model of the observed environment. In this environment, the approach that provided the best result in terms of both detail and dimensional accuracy was subsequently applied to the case study of Colonada in Buziaș. In this latter case study, a comparison of the point cloud generated by this low-cost sensor and one performed by a high-performance Terrestrial Laser Scanner (TLS), showed a difference of 15 centimeters for 80% of the points. In addition, the 3D point cloud obtained from 360° images is rather noisy and unable to construct complex geometries with small dimensions. However, the photogrammetric dataset can be used for the reconstruction of a virtual tour for the documentation and dissemination of Cultural Heritage. Full article

(This article belongs to the Special Issue Computer Vision and Robotics for Cultural Heritage: Theory and Applications)

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