(A) study on the gesture spotting from continuous hand motion with a threshold model임계치 모델을 이용한 연속적인 손 동작으로부터의 제스처 추출에 관한 연구

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dc.contributor.advisorKim, Jin-Hyung-
dc.contributor.advisor김진형-
dc.contributor.authorLee, Hyeon-Kyu-
dc.contributor.author이현규-
dc.date.accessioned2011-12-13T05:24:41Z-
dc.date.available2011-12-13T05:24:41Z-
dc.date.issued1998-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=143507&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/33120-
dc.description학위논문(박사) - 한국과학기술원 : 전산학과, 1998.2, [ viii, 86 p. ]-
dc.description.abstractThis 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.languageeng-
dc.publisher한국과학기술원-
dc.subjectState reduction-
dc.subjectRelative entropy-
dc.subjectPattern recognition-
dc.subjectSegmentation-
dc.subjectHidden Markov model-
dc.subjectGesture spotting-
dc.subjectHand gesture-
dc.subjectThreshold model-
dc.subject임계치 모델-
dc.subject상태 줄이기-
dc.subject상대성 엔트로피-
dc.subject패턴인식-
dc.subject구분-
dc.subject은닉 마르코프 모델-
dc.subject손 제스처-
dc.subject제스처 적출-
dc.title(A) study on the gesture spotting from continuous hand motion with a threshold model-
dc.title.alternative임계치 모델을 이용한 연속적인 손 동작으로부터의 제스처 추출에 관한 연구-
dc.typeThesis(Ph.D)-
dc.identifier.CNRN143507/325007-
dc.description.department한국과학기술원 : 전산학과, -
dc.identifier.uid000955311-
dc.contributor.localauthorKim, Jin-Hyung-
dc.contributor.localauthor김진형-
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CS-Theses_Ph.D.(박사논문)
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