荷兰汉学家高罗佩(R.H.van G ulik)之《琴道》一书是近现代以来开启西方人认识中国古琴的先驱之作。其中对古琴谱的"调意"、"指法手势图"与"减字谱"的研究,乃是中国古琴音乐理论的重要课题。然而,高罗佩...荷兰汉学家高罗佩(R.H.van G ulik)之《琴道》一书是近现代以来开启西方人认识中国古琴的先驱之作。其中对古琴谱的"调意"、"指法手势图"与"减字谱"的研究,乃是中国古琴音乐理论的重要课题。然而,高罗佩本乎其对中国文化的认知,不采用西方的乐谱作参照,而是发前人之所未发,写下别具开创性的论述,但其论述中也留下不少有待商榷与开拓的课题。展开更多
This paper addresses the application of hand gesture recognition in monocular image sequences using Active Appearance Model （AAM）, For this work, the proposed algorithm is composed of constricting AAMs and fitting t...This paper addresses the application of hand gesture recognition in monocular image sequences using Active Appearance Model （AAM）, For this work, the proposed algorithm is composed of constricting AAMs and fitting the models to the interest region. In training stage, according to the manual labeled feature points, the relative AAM is constructed and the corresponding average feature is obtained. In recognition stage, the interesting hand gesture region is firstly segmented by skin and movement cues. Secondly, the models are fitted to the image that includes the hand gesture, and the relative features are extracted. Thirdly, the classification is done by comparing the extracted features and average features. 30 different gestures of Chinese sign language are applied for testing the effectiveness of the method. The Experimental results are given indicating good performance of the algorithm.展开更多
文摘荷兰汉学家高罗佩(R.H.van G ulik)之《琴道》一书是近现代以来开启西方人认识中国古琴的先驱之作。其中对古琴谱的"调意"、"指法手势图"与"减字谱"的研究,乃是中国古琴音乐理论的重要课题。然而,高罗佩本乎其对中国文化的认知,不采用西方的乐谱作参照,而是发前人之所未发,写下别具开创性的论述,但其论述中也留下不少有待商榷与开拓的课题。
文摘This paper addresses the application of hand gesture recognition in monocular image sequences using Active Appearance Model （AAM）, For this work, the proposed algorithm is composed of constricting AAMs and fitting the models to the interest region. In training stage, according to the manual labeled feature points, the relative AAM is constructed and the corresponding average feature is obtained. In recognition stage, the interesting hand gesture region is firstly segmented by skin and movement cues. Secondly, the models are fitted to the image that includes the hand gesture, and the relative features are extracted. Thirdly, the classification is done by comparing the extracted features and average features. 30 different gestures of Chinese sign language are applied for testing the effectiveness of the method. The Experimental results are given indicating good performance of the algorithm.
