• Jump to content
  • Jump to navigation
  • Jump to bottom of page
Simulate organization breadcrumb open Simulate organization breadcrumb close
Pattern Recognition Lab
  • FAUTo the central FAU website
  • Campo
  • UnivIS
  • Jobs
  • Map
  • Help

Pattern Recognition Lab

Navigation Navigation close
  • Overview
    • Contact
    • Directions
    Portal Overview
  • Team
    • Former PRL members
    Portal Team
  • Research
    • Research Groups
    • Research Projects
    • Pattern Recognition Blog
    • Beyond the Patterns
    • Publications
    • Research Demo Videos
    • Datasets
    • Competitions
    Portal Research
  • Teaching
    • Curriculum / Courses
    • Lecture Notes
    • Lecture Videos
    • Thesis / Projects
    • Free Machine and Deep Learning Resources
    • Free Medical Engineering Resources
    • LME Videos
    Portal Teaching
  • Lab
    • News
    • Ph.D. Gallery
    • Cooperations
    • Join the Pattern Recognition Lab
    Portal Lab
  1. Home
  2. Research
  3. Research Groups
  4. Computer Vision
  5. ICONOGRAPHICS: Computational Understanding of Iconography and Narration in Visual Cultural Heritage

ICONOGRAPHICS: Computational Understanding of Iconography and Narration in Visual Cultural Heritage

In page navigation: Research
  • Beyond the Patterns
  • Competitions
  • Publications
  • Datasets

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: Peter Bell, Andreas Maier, Corinna Reinhardt, Ute Verstegen
Project members: Ronak Vijaypal Kosti, Prathmesh Madhu, Torsten Bendschus, Lara Mührenberg
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:

  1. The
    Annunciation of the Lord
  2. The Adoration of the Magi
  3. The Baptism
    of Christ
  4. Noli me tangere (Do not touch me)

Publications

  • Madhu P., Kosti RV., Mührenberg L., Bell P., Maier A., Christlein V.:
    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
  • Madhu P., Marquart T., Kosti RV., Bell P., Maier A., Christlein V.:
    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
Friedrich-Alexander-Universität
Erlangen-Nürnberg

Schlossplatz 4
91054 Erlangen
  • Login
  • Intranet
  • Imprint
  • Privacy
  • Accessibility
Up