Please use this identifier to cite or link to this item: http://repository.elizadeuniversity.edu.ng/jspui/handle/20.500.12398/595
Title: Hidden Markov model for human to computer interaction: a study on human hand gesture recognition
Other Titles: 2013/12/1
Authors: Bilal, Sara
Akmeliawati, Rini
Shafie, Amir A.
Salami, Momoh-Jimoh E.
Keywords: HCI applications
HMM
Artificial intelligence
Hand posture recognition
Hand gesture recognition
Issue Date: 1-Dec-2013
Publisher: Springer Netherlands
Citation: Bilal, S., Akmeliawati, R., Shafie, A. A., & Salami, M. J. E. (2013). Hidden Markov model for human to computer interaction: a study on human hand gesture recognition. Artificial Intelligence Review, 40(4), 495-516.
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.
URI: 10.1007/s10462-011-9292-0
http://repository.elizadeuniversity.edu.ng/jspui/handle/20.500.12398/595
Appears in Collections:Research Articles

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