Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Nonlinear Synchronization for Automatic Learning of 3D Pose Variability in Human Motion Sequences M. Mozerov, I | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 507247 10 pages doi 2010 507247 Research Article Nonlinear Synchronization for Automatic Learning of 3D Pose Variability in Human Motion Sequences M. Mozerov I. Rius X. Roca and J. Gonzalez Computer Vision Center and Departament d lnformatica Universitat Aut Onoma de Barcelona Campus UAB Edifici O 08193 Cerdanyola Spain Correspondence should be addressed to M. Mozerov mozerov@ Received 1 May 2009 Revised 31 July 2009 Accepted 2 September 2009 Academic Editor Joao Manuel R. S. Tavares Copyright 2010 M. Mozerov et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences which show different speeds and accelerations is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally an optimal sequence is automatically selected from the input dataset to be a time-scale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. The model is trained using the public CMU motion capture dataset for the walking action and a mean walking performance is automatically learnt. Additionally statistics about the observed variability of the postures and motion direction are also computed at each time step. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes. 1. Introduction Analysis of human motion in activities remains one of the most challenging open problems in computer vision 1-3 . The nature of the open problems and techniques used in human
