A robust self-calibration method is presented, which can efficiently discard the outliers based on a Weighted Iteration Method (WIM). The method is an iterative process in which the projective reconstruction is obtain...A robust self-calibration method is presented, which can efficiently discard the outliers based on a Weighted Iteration Method (WIM). The method is an iterative process in which the projective reconstruction is obtained based on the weights of all the points, whereas the weights are defined in inverse proportion to the re- ciprocal of the re-projective errors. The weights of outliers trend to zero after several iterations, and the accu- rate projective reconstruction is determined. The location of the absolute conic and the camera intrinsic pa- rameters are obtained after the projective reconstruction. The theory and experiments with both simulate and real data demonstrate that the proposed method is very efficient and robust.展开更多
基金Supported by the National Natural Science Foundation of China (No.60473119 and No.60372043).
文摘A robust self-calibration method is presented, which can efficiently discard the outliers based on a Weighted Iteration Method (WIM). The method is an iterative process in which the projective reconstruction is obtained based on the weights of all the points, whereas the weights are defined in inverse proportion to the re- ciprocal of the re-projective errors. The weights of outliers trend to zero after several iterations, and the accu- rate projective reconstruction is determined. The location of the absolute conic and the camera intrinsic pa- rameters are obtained after the projective reconstruction. The theory and experiments with both simulate and real data demonstrate that the proposed method is very efficient and robust.
