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Static gesture quantization and DCT based sign language generation

  • School of Computer Science and Technology, Harbin Institute of Technology
  • CAS - Institute of Computing Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

To collect data for sign language recognition is not a trivial task. The lack of training data has become a bottleneck in the research of singer independence and large vocabulary recognition. A novel sign language generation algorithm is introduced in this paper. The difference between signers is analyzed briefly and a criterion is introduced to distinguish the same gesture words of different signers. Basing on that criterion we propose a sign word generation method combining the static gesture quantization and Discrete Cosine Transform (DCT), which can generate the new signers' sign words according to the existed signers' sign words. The experimental result shows that not only the data generated are distinct with the training data, they are also demonstrated effective.

Original languageEnglish
Title of host publicationAffective Computing and Intelligent Interaction - First International Conference, ACII 2005, Proceedings
PublisherSpringer Verlag
Pages168-178
Number of pages11
ISBN (Print)3540296212, 9783540296218
DOIs
StatePublished - 2005
Externally publishedYes
Event1st International Conference on Affective Computing and Intelligent Interaction, ACII 2005 - Beijing, China
Duration: 22 Oct 200524 Oct 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3784 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Conference on Affective Computing and Intelligent Interaction, ACII 2005
Country/TerritoryChina
CityBeijing
Period22/10/0524/10/05

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