ROBUST METHOD FOR CAMERA CALIBRATION WITH VARYING PARAMETERS USING HYBRID MODIFIED GENETIC SIMPLEX ALGORITHM

In this paper, we present a new method of camera ca libration with varying parameters by a hybrid optimization algorithm that combines simplex algori thm and the modified genetic algorithm. Comparing to traditional optimization methods, the cameras calib ration with varying parameters by this approach can avoid being trapped in a local minimum and converge quickly to the optimal solution without initial estimation of the cameras parameters. Several exper iments are implemented using a 3D grid characterize d by these coordinates which are known, to demonstrate the validity and performance of the proposed technique. The results show that this approach is a ccurate and robust to single optimization methods.

[1]  H. Tairi,et al.  Fast Stereo Matching Using Rectification and Correlation Techniques , 2006 .

[2]  Genping Fu,et al.  Camera self-calibration method based on GA-PSO algorithm , 2011, 2011 IEEE International Conference on Cloud Computing and Intelligence Systems.

[3]  Wahyu Kusuma,et al.  Journal of Theoretical and Applied Information Technology , 2012 .

[4]  Hongxun Yao,et al.  Effective and Automatic Calibration Using Concentric Circles , 2008, Int. J. Pattern Recognit. Artif. Intell..

[5]  Qiang Ji,et al.  Camera calibration with genetic algorithms , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[6]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[7]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

[8]  Srinivasan Alavandar,et al.  GENETIC ALGORITHM BASED ROBOT MASSAGE , 2007 .

[9]  Anikó Ekárt,et al.  Using Genetic Algorithms in Computer Vision: Registering Images to 3D Surface Model , 2007, Acta Cybern..

[10]  Jing Sun,et al.  Camera Calibration Based on Improved Genetic Algorithm , 2007, 2007 IEEE International Conference on Automation and Logistics.

[11]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[12]  Andreas Zell,et al.  Automatic Calibration of Camera to World Mapping in RoboCup using Evolutionary Algorithms , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[13]  N. Sukavanam,et al.  Stereo camera calibration using real coded genetic algorithm , 2008, TENCON 2008 - 2008 IEEE Region 10 Conference.

[14]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[16]  Sarawat Anam,et al.  Face Recognition Using Genetic Algorithm and Back Propagation Neural Network , 2009 .

[17]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Xiangju Jiang.  A New Binocular Camera Calibration Method Based on an Improved Genetic Algorithm , 2011 .

[19]  Kumar Chellapilla,et al.  Combining mutation operators in evolutionary programming , 1998, IEEE Trans. Evol. Comput..

[20]  G. Savii.  CAMERA CALIBRATION USING COMPOUND GENETIC-SIMPLEX ALGORITHM , 2004 .

[21]  Ye San,et al.  Hybrid Simplex-improved Genetic Algorithm for Global Numerical Optimization , 2007 .

[22]  Bruno Sareni,et al.  Fitness sharing and niching methods revisited , 1998, IEEE Trans. Evol. Comput..

[23]  John Yen,et al.  A hybrid approach to modeling metabolic systems using a genetic algorithm and simplex method , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[24]  T. Clarke,et al.  The Development of Camera Calibration Methods and Models , 1998 .