Feature extraction: hand shape, hand position and hand trajectory path
Vision-based hand posture detection and tracking is an important issue for Human to Computer Interaction applications. The performance of recognition system fIrst depends on the process of getting effIcient features to represent pattern characteristics [1]. There is no algorithm which shows how to...
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IIUM Press
2011
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| Online Access: | http://irep.iium.edu.my/21640/ http://irep.iium.edu.my/21640/ http://irep.iium.edu.my/21640/1/Chapter_11.pdf |
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iium-216402013-11-27T09:11:59Z http://irep.iium.edu.my/21640/ Feature extraction: hand shape, hand position and hand trajectory path Bilal, Sara Mohammed Osman Saleh Akmeliawati, Rini TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Vision-based hand posture detection and tracking is an important issue for Human to Computer Interaction applications. The performance of recognition system fIrst depends on the process of getting effIcient features to represent pattern characteristics [1]. There is no algorithm which shows how to select the representation or choose the features [2] so the selection of features will depend on the application. There are many different methods to represent 2-D images such as boundary, topological, shape grammar, description of similarity etc. [2-4]. Features should be chosen so that they are intensive to noise-like variation in pattern and keep the number of feature small for easy computation [5]. Hand posture shape features, motion trajectory feature and hand position with respect to other human upper body parts play an important role within the preparation stage of the gesture before recognition. In this chapter, features have been extracted from hand posture closed contours, hand posture trajectory and hand position has been identifIed. Algorithms have been developed for extracting these features after segmenting the head and the two hands. These extracted features can be attached to a recognizer such as Support Vector machine, Hidden Markov Model, etc. for hand gesture recognition. IIUM Press 2011 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/21640/1/Chapter_11.pdf Bilal, Sara Mohammed Osman Saleh and Akmeliawati, Rini (2011) Feature extraction: hand shape, hand position and hand trajectory path. In: Human Behaviour Recognition,Iidentification and Computer Interaction. IIUM Press, Kuala Lumpur, pp. 85-91. ISBN 978-967-418-516-7 http://rms.research.iium.edu.my/bookstore/default.aspx |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
| spellingShingle |
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Bilal, Sara Mohammed Osman Saleh Akmeliawati, Rini Feature extraction: hand shape, hand position and hand trajectory path |
| description |
Vision-based hand posture detection and tracking is an important issue for Human to Computer Interaction applications. The performance of recognition system fIrst depends on the process of getting effIcient features to represent pattern characteristics [1]. There is no
algorithm which shows how to select the representation or choose the features [2] so the selection of features will depend on the application. There are many different methods to represent 2-D images such as boundary, topological, shape grammar, description of similarity etc. [2-4]. Features should be chosen so that they are intensive to noise-like variation in pattern and keep the number of feature small for easy computation [5]. Hand posture shape
features, motion trajectory feature and hand position with respect to other human upper body parts play an important role within the preparation stage of the gesture before recognition. In this chapter, features have been extracted from hand posture closed contours, hand posture trajectory and hand position has been identifIed. Algorithms have been developed for extracting these features after segmenting the head and the two hands. These extracted features can be attached to a recognizer such as Support Vector machine, Hidden Markov Model, etc. for hand gesture recognition. |
| format |
Book Chapter |
| author |
Bilal, Sara Mohammed Osman Saleh Akmeliawati, Rini |
| author_facet |
Bilal, Sara Mohammed Osman Saleh Akmeliawati, Rini |
| author_sort |
Bilal, Sara Mohammed Osman Saleh |
| title |
Feature extraction: hand shape, hand position and hand trajectory path |
| title_short |
Feature extraction: hand shape, hand position and hand trajectory path |
| title_full |
Feature extraction: hand shape, hand position and hand trajectory path |
| title_fullStr |
Feature extraction: hand shape, hand position and hand trajectory path |
| title_full_unstemmed |
Feature extraction: hand shape, hand position and hand trajectory path |
| title_sort |
feature extraction: hand shape, hand position and hand trajectory path |
| publisher |
IIUM Press |
| publishDate |
2011 |
| url |
http://irep.iium.edu.my/21640/ http://irep.iium.edu.my/21640/ http://irep.iium.edu.my/21640/1/Chapter_11.pdf |
| first_indexed |
2023-09-18T20:32:57Z |
| last_indexed |
2023-09-18T20:32:57Z |
| _version_ |
1777408857673302016 |