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Title: | Hidden Markov model for human to computer interaction: a study on human hand gesture recognition |
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 anymovement 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/20.500.12398/567 |
Appears in Collections: | Research Articles |
Files in This Item:
File | Description | Size | Format | |
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Hidden Markov model for human to computer interaction a study on human hand gesture recognition.pdf | Abstract | 60.71 kB | Adobe PDF | View/Open |
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