Pattern Recognition Systems for Cultural Heritage

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Computer Vision and Pattern Recognition".

Deadline for manuscript submissions: closed (1 May 2023) | Viewed by 4807

Special Issue Editors


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Guest Editor
Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, Catania 95125, Italy
Interests: digital restoration; cultural heritage; zooming; super-resolution; artifacts removal; interpolation; texture; GIS

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, Catania 95125, Italy
Interests: machine learning; computer vision; pattern recognition; image processing; cultural heritage
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical and Information Engineering “Maurizio Scarano”, University of Cassino and Southern Lazio, 03043 Cassino, Italy
Interests: machine learning; pattern recognition; IoT; image understanding; biomedical imaging; sensors
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical and Information Engineering “Maurizio Scarano”, University of Cassino and Southern Lazio, 03043 Cassino, Italy
Interests: artificial Intelligence; statistical pattern recognition

Special Issue Information

Dear Colleagues,

Pattern recognition and artificial intelligence are rapidly infiltrating new areas of our lives. On the other hand, the management of cultural heritage is increasingly in need of new solutions to document, manage and visit (even virtually) the enormous number of artifacts and information that come from the past. The meeting of these two worlds is now a reality, and represents the scope of the main topics of this Special Issue.

Pattern recognition (PR) technologies are already employed in cultural heritage preservation and exploitation. From these fields, two main issues arise:

  • The information contained in digital representations of physical objects (scanned documents, scanned artifacts, maps, digital music, etc.) is not easy to exploit, and advanced pattern recognition analysis is required.
  • The production of digital material such as augmented reality, cultural heritage games, robotics applications, etc. requires innovative techniques and methodologies.

The aim of this Special Issue is to present recent advances in PR and AI for data analysis and representation in cultural heritage, bringing together the works of many experts in this multidisciplinary subject that involves different skills and knowledge, spanning from the study of cultural heritage to the development of PR/AI techniques for cultural heritage analysis, reconstruction and understanding. At the same time, the Special Issue aims to highlight the advances on these topics from a broad perspective, and to stimulate new theoretical and applied research for better characterizing the state of the art in this subject.

The Special Issue will follow the 3nd International Workshop Pattern Recognition for Cultural Heritage (PatReCH 2022 - http://aida.unicas.it/patrech2022/), which will take place in conjunction with the 26th International Conference on Pattern Recognition (ICPR 2022 - https://www.icpr2022.com/), to be held 21–25 August 2022 in Montréal Québec, Canada, but submissions will be not restricted to workshop contributors.

Dr. Filippo Stanco
Dr. Dario Allegra
Dr. Mario Molinara
Dr. Alessandra Scotto di Freca
Guest Editors

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. Journal of Imaging is an international peer-reviewed open access monthly 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 1800 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

  • machine learning applications for cultural heritage
  • ancient document analysis
  • digital artifact capture, representation and manipulation
  • automatic annotation of tangible and intangible heritage
  • interactive software tools for cultural heritage applications
  • multimedia music classification and reconstruction
  • augmented and virtual reality applications
  • image processing, classification, and retrieval
  • semantic segmentation
  • object and artifacts detection
  • serious game for cultural heritage
  • robotic applications
  • ontology learning for cultural heritage domain
  • knowledge representations and reasoning
  • techniques for exploring and interacting with repositories of digital artifacts

Published Papers (2 papers)

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Research

18 pages, 14851 KiB  
Article
A Computational Approach to Hand Pose Recognition in Early Modern Paintings
by Valentine Bernasconi, Eva Cetinić and Leonardo Impett
J. Imaging 2023, 9(6), 120; https://doi.org/10.3390/jimaging9060120 - 15 Jun 2023
Cited by 1 | Viewed by 2147
Abstract
Hands represent an important aspect of pictorial narration but have rarely been addressed as an object of study in art history and digital humanities. Although hand gestures play a significant role in conveying emotions, narratives, and cultural symbolism in the context of visual [...] Read more.
Hands represent an important aspect of pictorial narration but have rarely been addressed as an object of study in art history and digital humanities. Although hand gestures play a significant role in conveying emotions, narratives, and cultural symbolism in the context of visual art, a comprehensive terminology for the classification of depicted hand poses is still lacking. In this article, we present the process of creating a new annotated dataset of pictorial hand poses. The dataset is based on a collection of European early modern paintings, from which hands are extracted using human pose estimation (HPE) methods. The hand images are then manually annotated based on art historical categorization schemes. From this categorization, we introduce a new classification task and perform a series of experiments using different types of features, including our newly introduced 2D hand keypoint features, as well as existing neural network-based features. This classification task represents a new and complex challenge due to the subtle and contextually dependent differences between depicted hands. The presented computational approach to hand pose recognition in paintings represents an initial attempt to tackle this challenge, which could potentially advance the use of HPE methods on paintings, as well as foster new research on the understanding of hand gestures in art. Full article
(This article belongs to the Special Issue Pattern Recognition Systems for Cultural Heritage)
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0 pages, 5370 KiB  
Article
A Siamese Transformer Network for Zero-Shot Ancient Coin Classification
by Zhongliang Guo, Ognjen Arandjelović, David Reid, Yaxiong Lei and Jochen Büttner
J. Imaging 2023, 9(6), 107; https://doi.org/10.3390/jimaging9060107 - 25 May 2023
Cited by 3 | Viewed by 1931 | Correction
Abstract
Ancient numismatics, the study of ancient coins, has in recent years become an attractive domain for the application of computer vision and machine learning. Though rich in research problems, the predominant focus in this area to date has been on the task of [...] Read more.
Ancient numismatics, the study of ancient coins, has in recent years become an attractive domain for the application of computer vision and machine learning. Though rich in research problems, the predominant focus in this area to date has been on the task of attributing a coin from an image, that is of identifying its issue. This may be considered the cardinal problem in the field and it continues to challenge automatic methods. In the present paper, we address a number of limitations of previous work. Firstly, the existing methods approach the problem as a classification task. As such, they are unable to deal with classes with no or few exemplars (which would be most, given over 50,000 issues of Roman Imperial coins alone), and require retraining when exemplars of a new class become available. Hence, rather than seeking to learn a representation that distinguishes a particular class from all the others, herein we seek a representation that is overall best at distinguishing classes from one another, thus relinquishing the demand for exemplars of any specific class. This leads to our adoption of the paradigm of pairwise coin matching by issue, rather than the usual classification paradigm, and the specific solution we propose in the form of a Siamese neural network. Furthermore, while adopting deep learning, motivated by its successes in the field and its unchallenged superiority over classical computer vision approaches, we also seek to leverage the advantages that transformers have over the previously employed convolutional neural networks, and in particular their non-local attention mechanisms, which ought to be particularly useful in ancient coin analysis by associating semantically but not visually related distal elements of a coin’s design. Evaluated on a large data corpus of 14,820 images and 7605 issues, using transfer learning and only a small training set of 542 images of 24 issues, our Double Siamese ViT model is shown to surpass the state of the art by a large margin, achieving an overall accuracy of 81%. Moreover, our further investigation of the results shows that the majority of the method’s errors are unrelated to the intrinsic aspects of the algorithm itself, but are rather a consequence of unclean data, which is a problem that can be easily addressed in practice by simple pre-processing and quality checking. Full article
(This article belongs to the Special Issue Pattern Recognition Systems for Cultural Heritage)
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