Self-Calibration of a Moving Camera from PointCorrespondences and Fundamental Matrices
International Journal of Computer Vision
Linear N-Point Camera Pose Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - 1998 Marr Prize
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Implicit and Explicit Camera Calibration: Theory and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Note on the Number of Solutions of the Noncoplanar P4P Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera Calibration from Surfaces of Revolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nonparametric approach for camera calibration
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Complete Solution Classification for the Perspective-Three-Point Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real Time Pattern Matching Using Projection Kernels
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Camera Calibration with One-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally Convergent Autocalibration Using Interval Analysis
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
Camera calibration with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Camera calibration is to identify a model that infers 3-D space measurements from 2-D image observations. In this paper, the nonlinear mapping model of the camera is approximated by a series of linear input-output models defined on a set of local regions called receptive fields. Camera calibration is thus a learning procedure to evolve the size and shape of every receptive field as well as parameters of the associated linear model. Since the learning procedure can also provide an approximation extent measurement for the linear model on each of the receptive fields, calibration model is consequently obtained from a fusion framework integrated with all linear models weighted by their corresponding approximation measurements. Since each camera model is composed of several receptive fields, it is feasible to unitedly calibrate multiple cameras simultaneously. The 3-D measurements of a multi-camera vision system are produced from a weighted regression fusion on all receptive fields of cameras. Thanks to the fusion strategy, the resultant calibration model of a multi-camera system is expected to have higher accuracy than either of them. Moreover, the calibration model is very efficient to be updated whenever one or more cameras in the multi-camera vision system need to be activated or deactivated to adapt to variable sensing requirements at different stages of task fulfillment. Simulation and experiment results illustrate effectiveness and properties of the proposed method. Comparisons with neural network-based calibration method and Tsai's method are also provided to exhibit advantages of the method.