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Adaptive automatic gesture stroke detection

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons4454

Gebre,  Binyam Gebrekidan
The Language Archive, MPI for Psycholinguistics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons216

Wittenburg,  Peter
The Language Archive, MPI for Psycholinguistics, Max Planck Society;

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Gebre_HamburgUP_dh2012_BoA.pdf
(Verlagsversion), 197KB

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Zitation

Gebre, B. G., & Wittenburg, P. (2012). Adaptive automatic gesture stroke detection. In J. C. Meister (Ed.), Digital Humanities 2012 Conference Abstracts. University of Hamburg, Germany; July 16–22, 2012 (pp. 458-461).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000E-78DC-A
Zusammenfassung
Print Friendly XML Gebre, Binyam Gebrekidan, Max Planck Institute for Psycholinguistics, The Netherlands, binyamgebrekidan.gebre@mpi.nl Wittenburg, Peter, Max Planck Institute for Psycholinguistics, The Netherlands, peter.wittenburg@mpi.nl Introduction Many gesture and sign language researchers manually annotate video recordings to systematically categorize, analyze and explain their observations. The number and kinds of annotations are so diverse and unpredictable that any attempt at developing non-adaptive automatic annotation systems is usually less effective. The trend in the literature has been to develop models that work for average users and for average scenarios. This approach has three main disadvantages. First, it is impossible to know beforehand all the patterns that could be of interest to all researchers. Second, it is practically impossible to find enough training examples for all patterns. Third, it is currently impossible to learn a model that is robustly applicable across all video quality-recording variations.