Month: August 2017

DH2017 – Computer Vision in DH workshop (Papers – Second Block)

Second block: Tools
Chair: Melvin Wevers (Utrecht University)

4) A Web-Based Interface for Art Historical Research (Sabine Lang & Bjorn Ommer)

Abstract
Slides
Computer Vision Group (University of Heidelberg)

  • Area: art history <-> computer vision
  • First experiment: Can computers propel the understanding and reconstruction of drawing processes?
  • Goal: Study production process. Understand the types and degrees of transformation between an original piece of art and its reproductions.
  • Experiment 2: Can computers help with the analysis of large image corpora, e.g. find gestures?
  • Goal: Find visual similarities and do formal analysis.
  • Central questions: which gestures can we identify? Do there exist varying types of one gesture?
  • Results: Visuelle Bildsuche (interface for art historical research)
Visuelle Bildsuche – Interface start screen. Data collection Sachsenspiegel (c1220)
  • Interesting and potential feature: in the image, you can markup areas and find others images with visual similarities:
Search results with visual similarities based on selected bounding boxes
Bautista, Miguel A., Artsiom Sanakoyeu, Ekaterina Sutter, and Björn Ommer. “CliqueCNN: Deep Unsupervised Exemplar Learning.” arXiv:1608.08792 [Cs], August 31, 2016. http://arxiv.org/abs/1608.08792.

5) The Media Ecology Project’s Semantic Annotation Tool and Knight Prototype Grant (Mark Williams, John Bell, Dimitrios Latsis, Lorenzo Torresani)

Abstract
Slides
Media Ecology Project (Dartmouth)

The Semantic Annotation Tool (SAT)

Is a drop-in module that facilitates the creation and sharing of time-based media annotations on the Web

Knight News Challenge Prototype Grant

Knight Foundation has awarded a Prototype Grant for Media Innovation to The Media Ecology Project (MEP) and Prof. Lorenzo Torresani’s Visual Learning Group at Dartmouth, in conjunction with The Internet Archive and the VEMI Lab at The University of Maine.

“Unlocking Film Libraries for Discovery and Search” will apply existing software for algorithmic object, action, and speech recognition to a varied collection of 100 educational films held by the Internet Archive and Dartmouth Library. We will evaluate the resulting data to plan future multimodal metadata generation tools that improve video discovery and accessibility in libraries.