针对摄像机内部参数的不确定性和投影平面选择难的问题,提出一种新的投影深度算法用于视角不变的动作识别,该算法采用对称镜面平面提取(plane extraction from mirror symmetry,PEMS)策略,有效解决了投影平面选择难的问题。首先通过摄...针对摄像机内部参数的不确定性和投影平面选择难的问题,提出一种新的投影深度算法用于视角不变的动作识别,该算法采用对称镜面平面提取(plane extraction from mirror symmetry,PEMS)策略,有效解决了投影平面选择难的问题。首先通过摄像机组观察获得3D动作姿势,然后运用PEMS策略从场景中提取平面,相对于提取平面估计身体点的投影深度,最后使用这个信息进行动作识别。该算法的核心是投影平面的提取和投影深度组成向量的求解。利用该算法在CMU Mo Cap数据集、TUM数据集和多视图IXMAS数据集上进行测试,精度可分别高达94%、91%和90%,且在较少动作实例情况下,仍然能够准确定义新动作。比较表明,该算法的人体动作识别性能明显优于其他几种较新的算法。展开更多
For classical orthogonal projection methods for large matrix eigenproblems, it may be much more difficult for a Ritz vector to converge than for its corresponding Ritz value when the matrix in question is non-Hermitia...For classical orthogonal projection methods for large matrix eigenproblems, it may be much more difficult for a Ritz vector to converge than for its corresponding Ritz value when the matrix in question is non-Hermitian. To this end, a class of new refined orthogonal projection methods has been proposed. It is proved that in some sense each refined method is a composite of two classical orthogonal projections, in which each refined approximate eigenvector is obtained by realizing a new one of some Hermitian semipositive definite matrix onto the same subspace. A priori error bounds on the refined approximate eigenvector are established in terms of the sine of acute angle of the normalized eigenvector and the subspace involved. It is shown that the sufficient conditions for convergence of the refined vector and that of the Ritz value are the same, so that the refined methods may be much more efficient than the classical ones.展开更多
The principal component analysis （PCA） is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit （PP）, a more general class of dimension-reduction techn...The principal component analysis （PCA） is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit （PP）, a more general class of dimension-reduction techniques. However, the application of this extended procedure is often hampered by its complexity in computation and by lack of some appropriate theory. In this paper, by use of the empirical processes we established a large sample theory for the robust PP estimators of the principal components and dispersion matrix.展开更多
The literature on landslide susceptibility is rich with examples that span a wide range of topics.However,the component that pertains to the extension of the susceptibility framework toward space–time modeling is lar...The literature on landslide susceptibility is rich with examples that span a wide range of topics.However,the component that pertains to the extension of the susceptibility framework toward space–time modeling is largely unexplored.This statement holds true,particularly in the context of landslide risk,where few scientific contributions investigate risk dynamics in space and time.This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained via a binomial Generalized Additive Model whose inventories span nine years(from 2013 to 2021).For the analyses,the data cube is organized with a mapping unit consisting of 26,333 slope units repeated over an annual temporal unit,resulting in a total of 236,997 units.This phase already includes several interesting modeling experiments that have rarely appeared in the landslide literature(e.g.,variable interaction plots).However,the main innovative effort is in the subsequent phase of the protocol we propose,as we used climate projections of the main trigger(rainfall)to obtain future estimates of yearly susceptibility patterns.These estimates are then combined with projections of urban settlements and associated populations to create a dynamic risk model,assuming vulnerability=1.Overall,this manuscript presents a unique example of such a modeling routine and offers a potential standard for administrations to make informed decisions regarding future urban development.展开更多
文摘针对摄像机内部参数的不确定性和投影平面选择难的问题,提出一种新的投影深度算法用于视角不变的动作识别,该算法采用对称镜面平面提取(plane extraction from mirror symmetry,PEMS)策略,有效解决了投影平面选择难的问题。首先通过摄像机组观察获得3D动作姿势,然后运用PEMS策略从场景中提取平面,相对于提取平面估计身体点的投影深度,最后使用这个信息进行动作识别。该算法的核心是投影平面的提取和投影深度组成向量的求解。利用该算法在CMU Mo Cap数据集、TUM数据集和多视图IXMAS数据集上进行测试,精度可分别高达94%、91%和90%,且在较少动作实例情况下,仍然能够准确定义新动作。比较表明,该算法的人体动作识别性能明显优于其他几种较新的算法。
基金Project supported by the China State Major Key Projects for Basic Researchesthe National Natural Science Foundation of China (Grant No. 19571014)the Doctoral Program (97014113), the Foundation of Excellent Young Scholors of Ministry of Education
文摘For classical orthogonal projection methods for large matrix eigenproblems, it may be much more difficult for a Ritz vector to converge than for its corresponding Ritz value when the matrix in question is non-Hermitian. To this end, a class of new refined orthogonal projection methods has been proposed. It is proved that in some sense each refined method is a composite of two classical orthogonal projections, in which each refined approximate eigenvector is obtained by realizing a new one of some Hermitian semipositive definite matrix onto the same subspace. A priori error bounds on the refined approximate eigenvector are established in terms of the sine of acute angle of the normalized eigenvector and the subspace involved. It is shown that the sufficient conditions for convergence of the refined vector and that of the Ritz value are the same, so that the refined methods may be much more efficient than the classical ones.
基金The researcb was partially supported by the National Natural Science Foundation of China under Grant No.19631040.
文摘The principal component analysis （PCA） is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit （PP）, a more general class of dimension-reduction techniques. However, the application of this extended procedure is often hampered by its complexity in computation and by lack of some appropriate theory. In this paper, by use of the empirical processes we established a large sample theory for the robust PP estimators of the principal components and dispersion matrix.
基金This research was supported by the National Natural Science Foundation of China-Young Scientist Funds(No.42207174)。
文摘The literature on landslide susceptibility is rich with examples that span a wide range of topics.However,the component that pertains to the extension of the susceptibility framework toward space–time modeling is largely unexplored.This statement holds true,particularly in the context of landslide risk,where few scientific contributions investigate risk dynamics in space and time.This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained via a binomial Generalized Additive Model whose inventories span nine years(from 2013 to 2021).For the analyses,the data cube is organized with a mapping unit consisting of 26,333 slope units repeated over an annual temporal unit,resulting in a total of 236,997 units.This phase already includes several interesting modeling experiments that have rarely appeared in the landslide literature(e.g.,variable interaction plots).However,the main innovative effort is in the subsequent phase of the protocol we propose,as we used climate projections of the main trigger(rainfall)to obtain future estimates of yearly susceptibility patterns.These estimates are then combined with projections of urban settlements and associated populations to create a dynamic risk model,assuming vulnerability=1.Overall,this manuscript presents a unique example of such a modeling routine and offers a potential standard for administrations to make informed decisions regarding future urban development.
