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Moreover, Professor Ommer analyzed the potential of machine vision for the humanities-and also the competences the humanities can offer to vision and machine learning. This talk discussed the challenges along the way towards artificial vision systems that can adequately support the humanities. In short, the image analysis that is presently available to the humanities needs to be upgraded from the equivalent of a typewriter to that of a highly versatile content processor. After all, one of the main challenges we face is finding and setting related artifacts into perspective although they are in different, otherwise unrelated corpora. Presently, computers can process images only on a low level, such as pixel by pixel or based on textual annotations. Given vast amounts of visual data, there is, however, a dire need for a much more intuitive access to individual images and a more effective handling of large image corpora. How would research in the humanities benefit if computers could handle images just as competently as they presently process text? What if performance would improve even further? University of Heidelberg, Germany Visual Analytics: Enabling Images to Speak for Themselves

  • Arthur Schnitzler – Literary Correspondences II.
  • Saving Ukrainian Cultural Heritage Online.
  • PROVIDEDH - Progressive Visual Decision Making in Digital Humanities.
  • (Ps.)-Athanasius - Expositiones in Psalmos.
  • NoMansLand: Nomads' Manuscripts Landscape.
  • Music at the Courts of the House of Habsburg.
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  • Medieval Music Manuscripts in Austrian Libraries.
  • Gustav Mahler – Digital Catalog of Works.
  • Images of Music for Austrian Music History.
  • Eduard Hanslick’s Criticism between Aesthetics, Journalism, and Scholarship.
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  • Discourses on music at the Habsburg military border.
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