ICONOGRAPHICS: Computational Understanding of Iconography and Narration in Visual Cultural Heritage
ICONOGRAPHICS: Computational Understanding of Iconography and Narration in Visual Cultural Heritage
(FAU Funds)
Overall project:
Project leader: , , ,
Project members: , , ,
Start date: April 1, 2019
End date: March 31, 2021
Acronym: ICONOGRAPHICS
Funding source:
URL:
Abstract
The interdisciplinary research project Iconographics is dedicated to
innovative possibilities of digital image recognition for the arts and
humanities. While computer vision is already often able to identify
individual objects or specific artistic styles in images, the project is
confronted with the open problem of also opening up the more complex
image structures and contexts digitally. On the basis of a close
interdisciplinary collaboration between Classical Archaeology, Christian
Archaeology, Art History and the Computer Sciences, as well as joint
theoretical and methodological reflection, a large number of
multi-layered visual works will be analyzed, compared and
contextualized. The aim is to make the complex compositional, narrative
and semantic structures of these images tangible for computer vision.
Iconography and Narratology are identified as a challenging research
questions for all subjects of the project. The iconography will be
interpreted in its plot, temporality, and narrative logic. Due to its
complex cultural structure; we selected four important scenes:
- The
Annunciation of the Lord - The Adoration of the Magi
- The Baptism
of Christ - Noli me tangere (Do not touch me)
Publications
Recognizing Characters in Art History Using Deep Learning
SUMAC 2019 - The 1st workshop on Structuring and Understanding of Multimedia heritAge Contents (Nice, October 21, 2019 - October 25, 2019)
In: Recognizing Characters in Art History Using Deep Learning 2019
DOI: 10.1145/3347317.3357242
BibTeX: Download
, , , , , :
Understanding Compositional Structures in Art Historical Images Using Pose and Gaze Priors
ECCV 2020 (Glasgow, August 23, 2020 - August 28, 2020)
In: Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm (ed.): Computer Vision – ECCV 2020 Workshops. ECCV 2020, Switzerland: 2020
DOI: 10.1007/978-3-030-66096-3_9
URL: https://link.springer.com/chapter/10.1007/978-3-030-66096-3_9
BibTeX: Download
, , , , , :