A new calibration algorithm for multi-camera systems using 1D calibration objects is proposed. The algorithm inte- grates the rank-4 factorization with Zhang (2004)'s method. The intrinsic parameters as well as th...A new calibration algorithm for multi-camera systems using 1D calibration objects is proposed. The algorithm inte- grates the rank-4 factorization with Zhang (2004)'s method. The intrinsic parameters as well as the extrinsic parameters are re- covered by capturing with cameras the 1D object's rotations around a fixed point. The algorithm is based on factorization of the scaled measurement matrix, the projective depth of which is estimated in an analytical equation instead of a recursive form. For more than three points on a 1D object, the approach of our algorithm is to extend the scaled measurement matrix. The obtained parameters are finally refined through the maximum likelihood inference. Simulations and experiments with real images verify that the proposed technique achieves a good trade-off between the intrinsic and extrinsic camera parameters.展开更多
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the National Natural Science Foundation of China (No. 60675017) the National Basic Research Program of China (No. 2006CB303103)
文摘A new calibration algorithm for multi-camera systems using 1D calibration objects is proposed. The algorithm inte- grates the rank-4 factorization with Zhang (2004)'s method. The intrinsic parameters as well as the extrinsic parameters are re- covered by capturing with cameras the 1D object's rotations around a fixed point. The algorithm is based on factorization of the scaled measurement matrix, the projective depth of which is estimated in an analytical equation instead of a recursive form. For more than three points on a 1D object, the approach of our algorithm is to extend the scaled measurement matrix. The obtained parameters are finally refined through the maximum likelihood inference. Simulations and experiments with real images verify that the proposed technique achieves a good trade-off between the intrinsic and extrinsic camera parameters.
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.
