DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kim, Jin-Hyung | - |
dc.contributor.advisor | 김진형 | - |
dc.contributor.author | Lee, Hyeon-Kyu | - |
dc.contributor.author | 이현규 | - |
dc.date.accessioned | 2011-12-13T05:24:41Z | - |
dc.date.available | 2011-12-13T05:24:41Z | - |
dc.date.issued | 1998 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=143507&flag=dissertation | - |
dc.identifier.uri | http://hdl.handle.net/10203/33120 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 전산학과, 1998.2, [ viii, 86 p. ] | - |
dc.description.abstract | This paper proposes a new method of spotting meaningful gestures from continuous hand motion in real-time. As the gesturer switches from one gesture to another, his hand makes an intermediate move linking the two gestures. A gesture recognizer may attempt to recognize this inevitable intermediate motion as a meaningful one (segmentation problem). Furthermore, the same gesture varies dynamically in shape and duration; instance by instance even of the same gesturer (spatio-temporal variability problem). The proposed method can solve the problems of segmentation and spatio-temporal variability of gestures using Hidden Markov Model (HMM). To handle non-gesture patterns between gestures, we make use of the internal segmentation property of the HMM and introduce a threshold model that consists of the state copies of all trained gesture models. The internal segmentation property implies that the states and the transitions of a trained HMM represent sub-patterns of a gesture and their sequential order. The threshold model calculates the likelihood threshold of the input pattern and is used to qualify an input pattern as a gesture. However, the threshold model, a weak model of all gestures, has a large number of states, so we reduce the number of states by the use of relative entropy. Through a set of experiments, it has been shown that the proposed method can successfully extract meaningful gestures from continuous hand motion with 93.14% reliability. The proposed method has been incorporated into PowerGesture to provide gestural commands for browsing the slide of $PowerPoint^TM$. | eng |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | State reduction | - |
dc.subject | Relative entropy | - |
dc.subject | Pattern recognition | - |
dc.subject | Segmentation | - |
dc.subject | Hidden Markov model | - |
dc.subject | Gesture spotting | - |
dc.subject | Hand gesture | - |
dc.subject | Threshold model | - |
dc.subject | 임계치 모델 | - |
dc.subject | 상태 줄이기 | - |
dc.subject | 상대성 엔트로피 | - |
dc.subject | 패턴인식 | - |
dc.subject | 구분 | - |
dc.subject | 은닉 마르코프 모델 | - |
dc.subject | 손 제스처 | - |
dc.subject | 제스처 적출 | - |
dc.title | (A) study on the gesture spotting from continuous hand motion with a threshold model | - |
dc.title.alternative | 임계치 모델을 이용한 연속적인 손 동작으로부터의 제스처 추출에 관한 연구 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 143507/325007 | - |
dc.description.department | 한국과학기술원 : 전산학과, | - |
dc.identifier.uid | 000955311 | - |
dc.contributor.localauthor | Kim, Jin-Hyung | - |
dc.contributor.localauthor | 김진형 | - |
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