Two-step camera calibration method based on the SPGD algorithm.

Given the rapid convergence characteristic of the stochastic parallel gradient descent (SPGD) algorithm, this study proposes a method that applies the algorithm to a two-step camera calibration method to resolve the frequent iteration and long calibration time deficiencies that exist under the traditional two-step camera calibration method, thereby achieving rapid calibration. The method first uses image coordinates obtained with subpixel positioning technology as initial values of control variables, in addition to positive disturbances produced on a two-dimensional plane, then uses two-step theory to calculate the average value of aberrations. Based on the same rationale, negative disturbances are then produced and the average value of the aberrations is calculated. Finally if, after assessing whether to continue with further iterations based on the difference in these values, continued iterations confirm new control variables based on the SPGD algorithm iteration formula, a new cycle is started until the results satisfy requirements. Theoretical analysis and experimental results show that the proposed rapid calibration method using the SPGD algorithm in the two-step camera calibration method is 3-4 times faster than the traditional two-step calibration method, and that it has significant potential value for use in certain time-constrained projects.

[1]  Richard I. Hartley,et al.  Projective Reconstruction and Invariants from Multiple Images , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Paul R. Cohen,et al.  Camera Calibration with Distortion Models and Accuracy Evaluation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Mikhail A. Vorontsov,et al.  Parallel perturbation gradient descent algorithm for adaptive wavefront correction , 1997, Optics & Photonics.

[4]  Mikhail A. Vorontsov,et al.  Adaptive active imaging system based on radiation focusing for extended targets , 1997, Optics & Photonics.

[5]  Olivier D. Faugeras,et al.  A theory of self-calibration of a moving camera , 1992, International Journal of Computer Vision.

[6]  Gert Cauwenberghs,et al.  Adaptive wavefront correction: a hybrid VLSI/optical system implementing parallel stochastic gradient descent , 1999, Remote Sensing.

[7]  Richard I. Hartley,et al.  Self-Calibration of Stationary Cameras , 1997, International Journal of Computer Vision.

[8]  Imaging with an array of adaptive subapertures. , 2008 .

[9]  T Weyrauch,et al.  Microscale Adaptive Optics: Wave-Front Control with a mu-Mirror Array and a VLSI Stochastic Gradient Descent Controller. , 2001, Applied optics.

[10]  Peter John Bryanston-Cross,et al.  Two sub-pixel processing algorithms for high accuracy particle centre estimation in low seeding density particle image velocimetry , 1996 .

[11]  M A Vorontsov,et al.  Adaptive phase-distortion correction based on parallel gradient-descent optimization. , 1997, Optics letters.

[12]  Mikhail A Vorontsov,et al.  Atmospheric compensation with a speckle beacon in strong scintillation conditions: directed energy and laser communication applications. , 2005, Applied optics.

[13]  Mikhail A. Vorontsov,et al.  Fiber coupling with adaptive optics for free-space optical communication , 2002, SPIE Optics + Photonics.

[14]  Mikhail A. Vorontsov,et al.  Phase-locking of tiled fiber array using SPGD feedback controller , 2005, SPIE Optics + Photonics.

[15]  J. Spall.  Multivariate stochastic approximation using a simultaneous perturbation gradient approximation , 1992 .

[16]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[17]  Songde Ma,et al.  A self-calibration technique for active vision systems , 1996, IEEE Trans. Robotics Autom..