Visual Recognition of Static/Dynamic Gesture: Gesture-Driven Editing System |
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| Affiliation: | 2. Artificial Intelligence Department, Kyushu Institute of Technology, (KIT), Iizuka, 820, Japan;1. Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia;2. Faculty of IT, INTI International University, 71800, Nilai, Negeri Sembilan, Malaysia;3. Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, 76100, Melaka, Malaysia;4. Department of Physics, Faculty of Science and Technology, Airlangga University, Surabaya, 60115, Indonesia;1. Department of Earth Science and Engineering, Imperial College London, SW7 2BP, UK;2. School of GeoSciences, University of Edinburgh, EH9 3FE, UK;3. Geological Survey of Norway, N-7491, Norway;1. Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. Department of Physics, Faculty of Science, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia;3. Faculty of Vocational Studies, Airlangga University, Surabaya, Indonesia;4. Department of Physics, Faculty of Science and Technology, Airlangga University, Surabaya, Indonesia;5. Faculty of Information Technology & Sciences, Inti International University, Perdana BBN, Putra Nilai, Nilai 71800, Negeri Sembilan, Malaysia;6. Department of Physics, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia;1. Department of Energy and Process Engineering, Norwegian University of Science and Technology, Trondheim, NO-7491, Norway;2. School of Architecture, Tsinghua University, Beijing, 100084, China |
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| Abstract: | This paper presents the visual recognition of static gesture (SG) or dynamic gesture (DG). Gesture is one of the most natural interface tools for human–computer interaction (HCI) as well as for communication between human beings. In order to implement a human-like interface, gestures could be recognized using only visual information such as the visual mechanism of human beings; SGs and DGs can be processed concurrently as well. This paper aims at recognizing hand gestures obtained from the visual images on a 2D image plane, without any external devices. Gestures are spotted by a task-specific state transition based on natural human articulation. SGs are recognized using image moments of hand posture, while DGs are recognized by analyzing their moving trajectories on the hidden Markov models (HMMs). We have applied our gesture recognition approach to gesture-driven editing systems operating in real time. |
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