基于子空间的鲁棒射影重建方法  

A Robust Subspace Algorithm for Projective Reconstruction from Multiple Images

作　　者：郭继东[1,2,3] 李学庆[2] 杨成伟[1] 

机构地区：[1]山东财经大学管理科学与工程学院,济南250014 [2]山东大学计算机科学与技术学院,济南250100 [3]中创软件工程股份有限公司,济南250014

出　　处：《计算机学报》2013年第12期2560-2576,共17页Chinese Journal of Computers

基　　金：国家科技支撑计划项目(2006BAF01A44)资助~~

摘　　要：现有基于子空间的方法不但要求测量矩阵中所有特征点在所有图像上是可见的,而且不能含有错误对齐的数据项(局外点).文中提出基于子空间的鲁棒射影重建方法,该方法使用施加秩约束的广义Lagrange乘子法(ALM)极小化一个由核范数和L1范数组合的凸规划,求出测量矩阵所位于的低维子空间,消除局外点和丢失数据项对因子化方法的影响.文中算法在同一个子空间框架中,对同一个目标函数关于2组不同参量交替估计射影结构和射影深度,由此确保迭代解的收敛.实验证明,与Tang子空间方法比较,文中算法的有效性和可靠性都有所提高.All the points are visible in all views and mismatched datum （outliers） are not presen- ted in measure matrix are necessary conditions of the existing subspace method. To eliminate the harmful effect of outliers and missing datum to factorization method, a robust subspace projective reconstruction method is proposed in this paper. Augmented Lagrange multipliers （ALM） imposed rank constraints can be used for solving a convex optimization problem. By minimizing a combination of nuclear norm and Li-norm, low-dimension subspaces of measure matrix can be obtained in this convex optimization. Projective shape and projective depths are alternatively esti- mated in the subspace projective reconstruction method. The two sub-problems are formulated in a subspace framework and same objective function is iteratively minimized on two independent variable sets. The above improvements can effectively ensure the convergence of the iterative process. Comparing with Tang＇s subspace method, experimental results are provided to illustrate the validity and reliability of the proposed algorithm in this paper.

关 键 词：局外点 丢失数据项 子空间方法 广义Lagrange乘子 因子化方法 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]