Interdisciplinary Approaches to Computational Moving Images
6-7 July 2023
King’s College London, Strand Campus
This workshop brings together a select group of researchers in the fields of digital and computational humanities, film, cultural history, informatics, computer vision, and digital art, with the purpose of exploring together emerging computational approaches to the study of moving images.
Participants include researchers from leading laboratories in Europe, including the Cultural Data Analytics Open Lab (CUDAN) at Tallinn University and the Cultural Analytics Lab (CANAL) at the University of Amsterdam, as well as archives and digital preservation experts from public UK institutions such as the BBC and the BFI. The workshop is hosted by the Computational Humanities Research Group in the Department of Digital Humanities at King’s College London.
Over two days, we will consider the modelling of moving images as computational artefacts, and reflect on the past, present, and future of computational moving image studies. We will then discuss and actively experiment with several ways of encoding the flows of moving images in time: from shot lengths measurements to high-dimensional representations, computational techniques that might afford new perspectives on the constitution and analysis of cinematic time.
The workshop is broadly split between a day of introductions and theory, and a second day of practical work and plans for future collaboration. The workshop will take place in the Embankment Room (MB -1.1.4), except the public panel on High-dimensional cinema, which will be at the Nash Lecture Theatre (K2.31). See programme in the next page.
We are also organising a public event on July 6th, click here for more details.
Any queries, please contact daniel.chavez@kcl.ac.uk
This event is funded by the Digital Futures Institute at KCL.
CUDAN participants are supported partially via the CUDAN ERA Chair project, funded through the Horizon 2020 research and innovation program of the European Commission (Grant no. 810961).
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