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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-8
Vassilis Athitsos , Computer Science and Engineering Department, University of Texas at Arlington, USA
Carol Neidle , Linguistics Program, Boston University, Massachusetts, USA
Stan Sclaroff , Computer Science Department, Boston University, Massachusetts, USA
Joan Nash , Linguistics Program, Boston University, Massachusetts, USA
Alexandra Stefan , Computer Science Department, Boston University, Massachusetts, USA
Quan Yuan , Computer Science Department, Boston University, Massachusetts, USA
Ashwin Thangali , Computer Science Department, Boston University, Massachusetts, USA
ABSTRACT
The lack of a written representation for American Sign Language (ASL) makes it difficult to do something as commonplace as looking up an unknown word in a dictionary. The majority of printed dictionaries organize ASL signs (represented in drawings or pictures) based on their nearest English translation; so unless one already knows the meaning of a sign, dictionary look-up is not a simple proposition. In this paper we introduce the ASL Lexicon Video Dataset, a large and expanding public dataset containing video sequences of thousands of distinct ASL signs, as well as annotations of those sequences, including start/end frames and class label of every sign. This dataset is being created as part of a project to develop a computer vision system that allows users to look up the meaning of an ASL sign. At the same time, the dataset can be useful for benchmarking a variety of computer vision and machine learning methods designed for learning and/or indexing a large number of visual classes, and especially approaches for analyzing gestures and human communication.
CITATION
Vassilis Athitsos, Carol Neidle, Stan Sclaroff, Joan Nash, Alexandra Stefan, Quan Yuan, Ashwin Thangali, "The American Sign Language Lexicon Video Dataset", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-8, doi:10.1109/CVPRW.2008.4563181
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