Museums around the world possess hundreds of thousands of priceless objects, which have stories to tell about human history. While students and scholars study them, even the general public is interested in these stories. If there is a way to automate the information delivery system about these objects it will be of immense value, e.g. it will support students to study these objects and speed up research. Adaptive blended learning options are conceivable, which can perfectly merge digital analysis and onsite viewing. Thus, the preparation and post-processing of studied objects is just as conceivable as the adequate acquisition of information for on-site studies. Examples of such solutions would be mobile apps and computer software that can be used for history and archaeology education as well. However, it is important to identify these objects correctly in order to build such solutions. Computer vision technologies in artificial intelligence (AI) can be used for this. Therefore, this paper will show how AI-algorithms can be used for digital humanities in novel ways, such as for detecting museum treasures.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:73554 |
| Date | 28 January 2021 |
| Creators | Perera, Walpola Layantha, Messemer, Heike, Heinz, Matthias, Kretzschmar, Michael |
| Contributors | Technische Universität Dresden |
| Publisher | TUDpress |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-3-95908-21-1, urn:nbn:de:bsz:14-qucosa2-728085, qucosa:72808, 978-3-95908-21-1 |
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