The existence of non response damages the precision of estimators in survey severely. The common countermeasure is imputation and weighting, the former makes use of the auxiliary information, the latter estimates by r...The existence of non response damages the precision of estimators in survey severely. The common countermeasure is imputation and weighting, the former makes use of the auxiliary information, the latter estimates by response rate. Each of them has merits as well as weakness. In order to incorporate the merits of the methods mentioned above, we put forward calibration estimation, which suggests adjusting the preliminary weights by auxiliary information at the stage of estimating. Marke the best of the relations between the independent variables and the dependent variable, use appropriate estimation method, and you’ll get a good estimator for the sum of the target variable.展开更多
Feature-based image matching algorithms play an indispensable role in automatic target recognition （ATR）. In this work, a fast image matching algorithm （FIMA） is proposed which utilizes the geometry feature of ext...Feature-based image matching algorithms play an indispensable role in automatic target recognition （ATR）. In this work, a fast image matching algorithm （FIMA） is proposed which utilizes the geometry feature of extended centroid （EC） to build affine invariants. Based on at-fine invariants of the length ratio of two parallel line segments, FIMA overcomes the invalidation problem of the state-of-the-art algorithms based on affine geometry features, and increases the feature diversity of different targets, thus reducing misjudgment rate during recognizing targets. However, it is found that FIMA suffers from the parallelogram contour problem and the coincidence invalidation. An advanced FIMA is designed to cope with these problems. Experiments prove that the proposed algorithms have better robustness for Gaussian noise, gray-scale change, contrast change, illumination and small three-dimensional rotation. Compared with the latest fast image matching algorithms based on geometry features, FIMA reaches the speedup of approximate 1.75 times. Thus, FIMA would be more suitable for actual ATR applications.展开更多
文摘The existence of non response damages the precision of estimators in survey severely. The common countermeasure is imputation and weighting, the former makes use of the auxiliary information, the latter estimates by response rate. Each of them has merits as well as weakness. In order to incorporate the merits of the methods mentioned above, we put forward calibration estimation, which suggests adjusting the preliminary weights by auxiliary information at the stage of estimating. Marke the best of the relations between the independent variables and the dependent variable, use appropriate estimation method, and you’ll get a good estimator for the sum of the target variable.
基金Projects(2012AA010901,2012AA01A301)supported by National High Technology Research and Development Program of ChinaProjects(61272142,61103082,61003075,61170261,61103193)supported by the National Natural Science Foundation of ChinaProjects(B120601,CX2012A002)supported by Fund Sponsor Project of Excellent Postgraduate Student of NUDT,China
文摘Feature-based image matching algorithms play an indispensable role in automatic target recognition （ATR）. In this work, a fast image matching algorithm （FIMA） is proposed which utilizes the geometry feature of extended centroid （EC） to build affine invariants. Based on at-fine invariants of the length ratio of two parallel line segments, FIMA overcomes the invalidation problem of the state-of-the-art algorithms based on affine geometry features, and increases the feature diversity of different targets, thus reducing misjudgment rate during recognizing targets. However, it is found that FIMA suffers from the parallelogram contour problem and the coincidence invalidation. An advanced FIMA is designed to cope with these problems. Experiments prove that the proposed algorithms have better robustness for Gaussian noise, gray-scale change, contrast change, illumination and small three-dimensional rotation. Compared with the latest fast image matching algorithms based on geometry features, FIMA reaches the speedup of approximate 1.75 times. Thus, FIMA would be more suitable for actual ATR applications.
