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SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
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Proceedings of the 12th annual ACM international conference on Multimedia
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Proceedings of the 2nd ACM international workshop on Multimedia databases
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Kernel-based sparse representation for gesture recognition
Pattern Recognition
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Fast and accurate recognition of motion data streams from gesture sensing and motion capture devices has many applications and is the focus of this article. Based on the analysis of the geometric structures revealed by singular value decompositions (SVD) of motion data, a similarity measure is proposed for simultaneously segmenting and recognizing motion streams. A direction identification approach is explored to further differentiate motions with similar data geometric structures. Experiments show that the proposed similarity measure can segment and recognize motion streams of variable lengths with high accuracy, without knowing beforehand the number of motions in a stream.