Referenced Items are not available for this document.
In this paper an approach to classify hand shapes into different classes according to the similarity measures between features is proposed. We show how to use an Exploratory Data Analysis to extract novel, single feature of hand from images. Based on the obtained curve-like shape of the feature, hands are classified into one of 21 possible classes of Croatian sign language using Dynamic Time Warping and Longest Common Subsequence as similarity measures. Performance of the system was evaluated with 1260 images. Results show that high classification accuracy can be obtained from a single feature recognition and a small number of training sample.
Published in:
EUROCON, 2007. The International Conference on "Computer as a Tool"
Date of Conference: 9-12 Sept. 2007
- Page(s):
- 264 - 269
- E-ISBN :
- 978-1-4244-0813-9
- Print ISBN:
- 978-1-4244-0813-9
- INSPEC Accession Number:
- 9810580
- Conference Location :
- Warsaw
- Digital Object Identifier :
- 10.1109/EURCON.2007.4400350
- Product Type:
- Conference Publications
About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
