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A Computational Approach to Hand Pose Recognition in Early Modern Paintings
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 comprehensiv...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299537/ https://www.ncbi.nlm.nih.gov/pubmed/37367468 http://dx.doi.org/10.3390/jimaging9060120 |
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author | Bernasconi, Valentine Cetinić, Eva Impett, Leonardo |
author_facet | Bernasconi, Valentine Cetinić, Eva Impett, Leonardo |
author_sort | Bernasconi, Valentine |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10299537 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102995372023-06-28 A Computational Approach to Hand Pose Recognition in Early Modern Paintings Bernasconi, Valentine Cetinić, Eva Impett, Leonardo J Imaging Article 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. MDPI 2023-06-15 /pmc/articles/PMC10299537/ /pubmed/37367468 http://dx.doi.org/10.3390/jimaging9060120 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bernasconi, Valentine Cetinić, Eva Impett, Leonardo A Computational Approach to Hand Pose Recognition in Early Modern Paintings |
title | A Computational Approach to Hand Pose Recognition in Early Modern Paintings |
title_full | A Computational Approach to Hand Pose Recognition in Early Modern Paintings |
title_fullStr | A Computational Approach to Hand Pose Recognition in Early Modern Paintings |
title_full_unstemmed | A Computational Approach to Hand Pose Recognition in Early Modern Paintings |
title_short | A Computational Approach to Hand Pose Recognition in Early Modern Paintings |
title_sort | computational approach to hand pose recognition in early modern paintings |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299537/ https://www.ncbi.nlm.nih.gov/pubmed/37367468 http://dx.doi.org/10.3390/jimaging9060120 |
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