Automatic sign categorization using visual data
Author: 
Marek HruúzAuthors Info & Claims
Pages 229 - 230
Abstract
This paper presents a method of visual tracking in recordings of isolated signs and the usage of the tracked features for automatic sign categorization. The tracking method is based on skin color segmentation and is suitable for recordings of a sign language dictionary. The result of the tracking is the location and outer contour of head and both hands. These features are used to categorize the signs into several categories: movement of hands, contact of body parts, symmetry of trajectory, location of the sign.
References
[1]
P. Campr, M. Hrúz, and M. Železný. Design and recording of czech sign language corpus for automatic sign language recognition. Proc. of Interspeech 2007, pages 678--681, 2007.
[2]
O. Crasborn and I. Zwitserlood. The corpus ngt: an online corpus for professionals and laymen. Proceedings of the sixth Conference on Language Resources and Evaluation (LREC 2008), pages 44--49, 2008.
[3]
M. Hrúz, Z. Krňoul, P. Campr, and L. Müller. Towards automatic annotation of sign language dictionary corpora. Lecture Notes in Artificial Intelligence, LNAI 6836, in press, 2011.
[4]
J. Trmal, M. Hrúz, J. Zelinka, P. Campr, and L. Müller. Feature space transforms for czech sign-language recognition. In In ICSLP, 2008.
Index Terms
- Automatic sign categorization using visual data
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Published In
October 2011
348 pages
Copyright © 2011 Author.
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Association for Computing Machinery
New York, NY, United States
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Published: 24 October 2011
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ASSETS '11
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ASSETS '11: The 13th International ACM SIGACCESS Conference on Computers and Accessibility
October 24 - 26, 2011
Dundee, Scotland, UK
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Overall Acceptance Rate 436 of 1,556 submissions, 28%
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