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Hidden Markov model for human to computer interaction: A study on human hand gesture recognition

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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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)
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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