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Applied Sciences
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Advanced Technologies Applied to Cultural Heritage
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Advanced Technologies Applied to Cultural Heritage

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 25 September 2024 | Viewed by 3429

Special Issue Editor

Dr. Michail Panagopoulos

E-Mail Website
Guest Editor
Department of Audio and Visual Arts, Ionian University, 49100 Corfu, Greece
Interests: application of digital signal processing and pattern recognition in archaeology heritage; arts and cultural heritage; digital image processing; pattern recognition; computer vision; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, rapid technological advances, especially in the fields of artificial intelligence and machine learning, have had a significant impact on applications related to the study of cultural heritage. The vast variety of new approaches in understanding the past, civilizations, and the arts leads to new challenges and opportunities in preserving and promoting cultural heritage.

Cultural heritage encompasses a vast array of artifacts, monuments, archaeological sites, artworks, and intangible traditions that are crucial for understanding our collective past and shaping our future. By leveraging cutting-edge technologies, we have the opportunity to unlock new insights, enhance conservation efforts, and promote wider public engagement with cultural heritage.

This Special Issue explores the application of advanced technologies to the preservation, analysis, and dissemination of cultural heritage and seeks to promote new methods, pervasive applications, and platforms related to the application of digital technologies.

We thus invite researchers, scholars, and practitioners to contribute to this Special Issue with original research articles as well as reviews. Research areas may include (but are not limited to) the following:

  • Digitalization and 3D modeling;
  • Artificial intelligence and machine learning applied to cultural heritage;
  • Augmented reality (AR) and virtual reality (VR);
  • Data analytics and visualization;
  • Preservation and conservation;
  • Digital humanities.

Dr. Michail Panagopoulos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cultural heritage
  • preservation
  • dissemination
  • digital documentation
  • artificial intelligence
  • augmented reality
  • conservation
  • immersive experiences

Published Papers (3 papers)

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Research

14 pages, 10411 KiB  
Article
A Metaverse Platform for Preserving and Promoting Intangible Cultural Heritage
by Chiara Innocente, Francesca Nonis, Antonio Lo Faro, Rossella Ruggieri, Luca Ulrich and Enrico Vezzetti
Appl. Sci. 2024, 14(8), 3426; https://doi.org/10.3390/app14083426 - 18 Apr 2024
Viewed by 366
Abstract
The metaverse, powered by XR technologies, enables human augmentation by enhancing physical, cognitive, and sensory capabilities. Cultural heritage sees the metaverse as a vehicle for expression and exploration, providing new methods for heritage fruition and preservation. This article proposes a metaverse application, inspired [...] Read more.
The metaverse, powered by XR technologies, enables human augmentation by enhancing physical, cognitive, and sensory capabilities. Cultural heritage sees the metaverse as a vehicle for expression and exploration, providing new methods for heritage fruition and preservation. This article proposes a metaverse application, inspired by the events of the Italian Resistance, promoting interactions between multiple users in an immersive VR experience while safeguarding intangible cultural assets according to an edutainment approach. The virtual environment, based on Ivrea’s town hall square, provides in-depth information about the partisan’s life and the historical value of its actions for the city. Furthermore, the application allows users to meet in the same virtual place and engage with one another in real time through the Spatial SDK. Before the public presentation, a heterogeneous group of thirty users underwent usability and engagement tests to assess the experience on both VR headsets and smartphones. Tests revealed statistically significant evidence that there is a genuine difference in users’ perceptions of usability and engagement with different devices and types of interaction. This study highlights the effectiveness of adopting XR as a supporting technology to complement the real experience of cultural heritage valorization. Full article
(This article belongs to the Special Issue Advanced Technologies Applied to Cultural Heritage)
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23 pages, 6956 KiB  
Article
Paint-CUT: A Generative Model for Chinese Landscape Painting Based on Shuffle Attentional Residual Block and Edge Enhancement
by Zengguo Sun, Haoyue Li and Xiaojun Wu
Appl. Sci. 2024, 14(4), 1430; https://doi.org/10.3390/app14041430 - 09 Feb 2024
Viewed by 558
Abstract
As one of the precious cultural heritages, Chinese landscape painting has developed unique styles and techniques. Researching the intelligent generation of Chinese landscape paintings from photos can benefit the inheritance of traditional Chinese culture. To address detail loss, blurred outlines, and poor style [...] Read more.
As one of the precious cultural heritages, Chinese landscape painting has developed unique styles and techniques. Researching the intelligent generation of Chinese landscape paintings from photos can benefit the inheritance of traditional Chinese culture. To address detail loss, blurred outlines, and poor style transfer in present generated results, a model for generating Chinese landscape paintings from photos named Paint-CUT is proposed. In order to solve the problem of detail loss, the SA-ResBlock module is proposed by combining shuffle attention with the resblocks in the generator, which is used to enhance the generator’s ability to extract the main scene information and texture features. In order to solve the problem of poor style transfer, perceptual loss is introduced to constrain the model in terms of content and style. The pre-trained VGG is used to extract the content and style features to calculate the perceptual loss and, then, the loss can guide the model to generate landscape paintings with similar content to landscape photos and a similar style to target landscape paintings. In order to solve the problem of blurred outlines in generated landscape paintings, edge loss is proposed to the model. The Canny edge detection is used to generate edge maps and, then, the edge loss between edge maps of landscape photos and generated landscape paintings is calculated. The generated landscape paintings have clear outlines and details by adding edge loss. Comparison experiments and ablation experiments are performed on the proposed model. Experiments show that the proposed model can generate Chinese landscape paintings with clear outlines, rich details, and realistic style. Generated paintings not only retain the details of landscape photos, such as texture and outlines of mountains, but also have similar styles to the target paintings, such as colors and brush strokes. So, the generation quality of Chinese landscape paintings has improved. Full article
(This article belongs to the Special Issue Advanced Technologies Applied to Cultural Heritage)
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26 pages, 1589 KiB  
Article
Aesthetic Experience and Popularity Ratings for Controversial and Non-Controversial Artworks Using Machine Learning Ranking
by Sofia Vlachou and Michail Panagopoulos
Appl. Sci. 2023, 13(19), 10721; https://doi.org/10.3390/app131910721 - 26 Sep 2023
Viewed by 1528
Abstract
Currently, a substantial portion of images snapped at exhibitions and galleries on social media demonstrates that aesthetic experience is not restricted to the confines of cultural institutions. The primary objective of this paper is to examine whether the content or aspect of an [...] Read more.
Currently, a substantial portion of images snapped at exhibitions and galleries on social media demonstrates that aesthetic experience is not restricted to the confines of cultural institutions. The primary objective of this paper is to examine whether the content or aspect of an artwork influences the aesthetic experience of the viewer and to measure the artwork’s social media popularity. To compare controversial works of art with those whose design, qualities, or intended message are non-controversial, we first sought out controversial works. A variety of artworks were revealed on Instagram; thus, the objective was to identify a non-controversial artwork published in the same year as each controversial artwork. We adhered to the complete procedure for cleansing, standardizing, and transforming the data to ensure comparability. Popularity was measured using a ranking algorithm and quantitative approaches for the recognition and statistical measurement of emotions. In addition, the exhaustive literature survey on models of aesthetic experience revealed no link between the experience of art and its social media popularity. Considering this, we have proposed, among other things, a new framework for interacting with art that integrates these parameters. According to the findings, controversial artworks elicited stronger emotions than non-controversial artworks. Furthermore, investigations have determined the three most popular works of art in each category. Under the scrutiny of social media, these results may inspire future research on the popularity of museum artworks and the design of aesthetic experiences. Full article
(This article belongs to the Special Issue Advanced Technologies Applied to Cultural Heritage)
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