| Operational Gesture Segmentation and Recognition |
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| 作者姓名： | 马赓宇 林学訚 |
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| 作者单位： | Institute of Human Computer Interface and Media Integration，Department of Computer Science and Technology，Tsinghua University，Beijing 100084，China，Institute of Human Computer Interface and Media Integration，Department of Computer Science and Technology，Tsinghua University，Beijing 100084，China |
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| 基金项目： | Supported by the National Natural Science Foundation of China(No.69975009) |
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| 摘 要： | Gesture analysis by computer is an important part of the human computer interface (HCI) and a gesture analysis method was developed using a skin-color-based method to extract the area representing the hand in a single image with a distribution feature measurement designed to describe the hand shape in the images. A hidden Markov model (HMM) based method was used to analyze the temporal variation and segmentation of continuous operational gestures. Furthermore, a transition HMM was used to represent the period between gestures, so the method could segment continuous gestures and eliminate non-standard gestures. The system can analyze 2 frames per second, which is sufficient for real time analysis.
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Operational Gesture Segmentation and Recognition |
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| MA Gengyu,LIN Xueyin
Institute of Human Computer Interface and Media Integration.Operational Gesture Segmentation and Recognition[J].Tsinghua Science and Technology,2003,8(2). |
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| Authors: | MA Gengyu LIN Xueyin
Institute of Human Computer Interface and Media Integration |
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| Institution: | MA Gengyu,LIN Xueyin
Institute of Human Computer Interface and Media Integration,Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China |
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| Abstract: | Gesture analysis by computer is an important part of the human computer interface (HCI) and a gesture analysis method was developed using a skin-color-based method to extract the area representing the hand in a single image with a distribution feature measurement designed to describe the hand shape in the images. A hidden Markov model (HMM) based method was used to analyze the temporal variation and segmentation of continuous operational gestures. Furthermore, a transition HMM was used to represent the period between gestures, so the method could segment continuous gestures and eliminate non-standard gestures. The system can analyze 2 frames per second, which is sufficient for real time analysis. |
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| Keywords: | operational gesture gesture recognition hidden Markov model transition model |
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