Bilal, Sara Mohammed Osman Saleh and Akmeliawati, Rini and Shafie, Amir Akramin and Salami, Momoh Jimoh Emiyoka (2013) Hidden Markov model for human to computer interaction: A study on human hand gesture recognition. Artificial Intelligence Review, 40 (4). pp. 495-516. ISSN 0269-2821
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Abstract
Human hand recognition plays an important role in a wide range of applications ranging from sign language translators, gesture recognition, augmented reality, surveillance and medical image processing to various Human Computer Interaction (HCI) domains. Human hand is a complex articulated object consisting of many connected parts and joints. Therefore, for applications that involve HCI one can find many challenges to establish a system with high detection and recognition accuracy for hand posture and/or gesture. Hand posture is defined as a static hand configuration without any movement involved. Meanwhile, hand gesture is a sequence of hand postures connected by continuous motions. During the past decades, many approaches have been presented for hand posture and/or gesture recognition. In this paper, we provide a survey on approaches which are based on Hidden Markov Models (HMM) for hand posture and gesture recognition for HCI applications.
Item Type: | Article (Journal) |
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Additional Information: | 5806/16246 |
Uncontrolled Keywords: | HCI applications · HMM · Artificial intelligence · Hand posture recognition · Hand gesture recognition |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Engineering > Department of Mechatronics Engineering |
Depositing User: | Prof. Dr. Rini Akmeliawati |
Date Deposited: | 01 Feb 2012 15:48 |
Last Modified: | 14 Feb 2014 09:56 |
URI: | http://irep.iium.edu.my/id/eprint/16246 |
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