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Plane-based calibration algorithm for multi-camera systems via factorization of homography matrices

机译:基于平面校正校准算法,通过定位矩阵分解

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A new calibration algorithm for multicamera systems using a planar reference pattern is proposed. The algorithm is an extension of Sturm-Maybank-Zhang style plane-based calibration technique for use with multiple cameras. Rigid displacements between the cameras are recovered as well as the intrinsic parameters only by capturing with the cameras a model plane with known reference points placed at three or more locations. Thus the algorithm yields a simple calibration means for stereo vision systems with an arbitrary number of cameras while maintaining the handiness and flexibility of the original method. The algorithm is based on factorization of homography matrices between the model and image planes into the camera and plane parameters. To compensate for the indetermination of scaling factors, each homography matrix is rescaled by a double eigenvalue of a planar homology defined by two views and two model planes. The obtained parameters are finally refined by a nonlinear maximum likelihood estimation (MLE) process. The validity of the proposed technique was verified through simulation and experiments with real data.
机译:提出了一种新的使用平面参考图案的多轨体系的校准算法。该算法是与多个摄像机一起使用的基于STURM-Maybank-Zhang风格平面校准技术的延伸。仅通过用相机模型平面捕获具有所公知的参考点,恢复相机之间的刚性位移以及内在参数,其中包含放置在三个或更多个位置的已知参考点。因此,该算法为立体声视觉系统产生简单的校准装置,其具有任意数量的相机,同时保持原始方法的交易和灵活性。该算法基于模型和图像平面之间的同字矩阵的分解,进入相机和平面参数。为了补偿缩放因子的不确定,每个定义矩阵由由两个视图和两个模型平面定义的平面同源的双重特征值重新缩放。最终通过非线性最大似然估计(MLE)过程来改进所获得的参数。通过使用实际数据的模拟和实验验证了所提出的技术的有效性。

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