Unveiling Historical Depth: Semantic annotation of the Panorama of the Battle of Murten

In the context of the research project Digitizing and Augmenting the Panorama of the Battle of Murten (EPFL, 2022-2026), we have produced the largest digital image of a single work of art (1.62 terapixel, the original painting dimensions are 10x100m, oil on canvas, 1894). To enhance the interpretation of the panorama's intricate visual content, we propose a deep semantic annotation approach for data curation, operating at the Point of Interest (POI) level. This workshop is being offered by Daniel Jaquet and Tsz Kin Chau (EPFL).

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-09-13
DOI
https://doi.org/10.5281/zenodo.14175342
Primary Topic
Aesthetic Perception and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Unveiling Historical Depth: Semantic annotation of the Panorama of the Battle of Murten

Zenodo (CERN European Organization for Nuclear Research)
Aesthetic Perception and Analysis
article

Unveiling Historical Depth: Semantic annotation of the Panorama of the Battle of Murten

article en

Abstract

In the context of the research project Digitizing and Augmenting the Panorama of the Battle of Murten (EPFL, 2022-2026), we have produced the largest digital image of a single work of art (1.62 terapixel, the original painting dimensions are 10x100m, oil on canvas, 1894). To enhance the interpretation of the panorama's intricate visual content, we propose a deep semantic annotation approach for data curation, operating at the Point of Interest (POI) level. This workshop is being offered by Daniel Jaquet and Tsz Kin Chau (EPFL).

Zenodo (CERN European Organization for Nuclear Research)
École Polytechnique Fédérale de Lausanne (CH)
Openalex Percentile: Top 54%
Aesthetic Perception and Analysis
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Unveiling Historical Depth: Semantic annotation of the Panorama of the Battle of Murten · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS