Hidden Markov model for human to computer interaction: A study on human hand gesture recognition
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 co...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerLink
2013
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/16246/1/10.1007_s10462-011-9292-0.pdf http://irep.iium.edu.my/16246/ http://dx.doi.org/10.1007/s10462-011-9292-0 |
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| Summary: | 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. |
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