为了解决当前人体运动识别方法受到复杂背景、可变光照及视角变化的影响,无法准确识别人体运动轨迹的问题,通过特征匹配研究人体运动轨迹识别问题。通过背景提取与差分二级化对人体运动区域进行分割,在此基础上,把人体运动空间描述转换...为了解决当前人体运动识别方法受到复杂背景、可变光照及视角变化的影响,无法准确识别人体运动轨迹的问题,通过特征匹配研究人体运动轨迹识别问题。通过背景提取与差分二级化对人体运动区域进行分割,在此基础上,把人体运动空间描述转换至人体运动关节空间坐标系。通过归一化位移向量序列标识关节活动幅度轨迹,将Fisher向量作为特征,为人体运动轨迹识别提供依据。关节活动幅度轨迹识别选用DTW(Dynamic Time Warping,动态时间归整)方法,获取参考模板与测试模板间的最小累积失真量,将测试模板归类于全部累积失真量最小的一类中,以实现对不同人体运动轨迹长度模板的匹配。结果表明:所提方法识别的人体运动轨迹和实际轨迹基本吻合,受外界环境的影响较小;所提方法与其它方法相比识别率较高,且识别时间较短。可见所提方法识别结果准确,有较强的可行性。展开更多
We use conditional nonlinear optimal perturbation （CNOP） to investigate the optimal precursory disturbances in the Zebiak- Cane El Nino-Southern Oscillation （ENSO） model. The conditions of the CNOP-type precursors...We use conditional nonlinear optimal perturbation （CNOP） to investigate the optimal precursory disturbances in the Zebiak- Cane El Nino-Southern Oscillation （ENSO） model. The conditions of the CNOP-type precursors are highly likely to evolve into El Nino events in the Zebiak-Cane model. By exploring the dynamic behaviors of these nonlinear El Nino events caused by the CNOP-type precursors, we find that they, as expected, tend to phase-lock to the annual cycles in the Zebiak-Cane model with the SSTA peak at the end of a calendar year. However, E1 Nino events with CNOPs as initial anomalies in the linearized Zebiak-Cane model are inclined to phase-lock earlier than nonlinear E1 Nino events despite the existence of annual cycles in the model. It is clear that nonlinearities play an important role in El Nino＇s phase-locking. In particular, nonlinear temperature advection increases anomalous zonal SST differences and anomalous westerlies, which weakens anomalous upwelling and acts on the increasing anomalous vertical temperature difference and, as a result, enhances E1 Nino and then delays the peak SSTA. Finally, we demonstrate that nonlinear temperature advection, together with the effect of the annual cycle, causes El Nino events to peak at the end of the calendar year.展开更多
文摘为了解决当前人体运动识别方法受到复杂背景、可变光照及视角变化的影响,无法准确识别人体运动轨迹的问题,通过特征匹配研究人体运动轨迹识别问题。通过背景提取与差分二级化对人体运动区域进行分割,在此基础上,把人体运动空间描述转换至人体运动关节空间坐标系。通过归一化位移向量序列标识关节活动幅度轨迹,将Fisher向量作为特征,为人体运动轨迹识别提供依据。关节活动幅度轨迹识别选用DTW(Dynamic Time Warping,动态时间归整)方法,获取参考模板与测试模板间的最小累积失真量,将测试模板归类于全部累积失真量最小的一类中,以实现对不同人体运动轨迹长度模板的匹配。结果表明:所提方法识别的人体运动轨迹和实际轨迹基本吻合,受外界环境的影响较小;所提方法与其它方法相比识别率较高,且识别时间较短。可见所提方法识别结果准确,有较强的可行性。
基金sponsored by the Knowledge Innovation Program of the Chinese Academy of Sciences(Grant No.KZCX2-YW-QN203)the National Basic Research Program of China(Grant Nos.2010CB950400&2012CB955202)the National Natural Science Foundation of China(Grant No.41176013)
文摘We use conditional nonlinear optimal perturbation （CNOP） to investigate the optimal precursory disturbances in the Zebiak- Cane El Nino-Southern Oscillation （ENSO） model. The conditions of the CNOP-type precursors are highly likely to evolve into El Nino events in the Zebiak-Cane model. By exploring the dynamic behaviors of these nonlinear El Nino events caused by the CNOP-type precursors, we find that they, as expected, tend to phase-lock to the annual cycles in the Zebiak-Cane model with the SSTA peak at the end of a calendar year. However, E1 Nino events with CNOPs as initial anomalies in the linearized Zebiak-Cane model are inclined to phase-lock earlier than nonlinear E1 Nino events despite the existence of annual cycles in the model. It is clear that nonlinearities play an important role in El Nino＇s phase-locking. In particular, nonlinear temperature advection increases anomalous zonal SST differences and anomalous westerlies, which weakens anomalous upwelling and acts on the increasing anomalous vertical temperature difference and, as a result, enhances E1 Nino and then delays the peak SSTA. Finally, we demonstrate that nonlinear temperature advection, together with the effect of the annual cycle, causes El Nino events to peak at the end of the calendar year.
