Implicit and Explicit Camera Calibration: Theory and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constructive Incremental Learning from Only Local Information
Neural Computation
Multi-camera calibration, object tracking and query generation
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Adaptive mixtures of local experts
Neural Computation
Incremental learning with balanced update on receptive fields for multi-sensor data fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The camera model could be approximated by a set of linear models defined on a set of local receptive fields regions. Camera calibration could then be a learning procedure to evolve the size and shape of every receptive field as well as parameters of the associated linear model. For a multi-camera system, its unified model is obtained from a fusion procedure integrated with all linear models weighted by their corresponding approximation measurements. The 3-D measurements of the multi-camera vision system are produced from a weighted regression fusion on all receptive fields of cameras. The resultant calibration model of a multi-camera system is expected to have higher accuracy than either of them. Simulation and experiment results illustrate effectiveness and properties of the proposed method. Comparisons with the Tsai's method are also provided to exhibit advantages of the method.