Title :
Camera self-calibration method based on GA-PSO algorithm
Author :
Li, Jing ; Yang, Yimin ; Fu, Genping
Author_Institution :
Sch. of Autom., Guangdong Univ. of Technol., Guangzhou, China
Abstract :
This paper proposes a new algorithm(GA-PSO) by combining genetic algorithm and particle swarm optimization to improve the accuracy of camera self-calibration based on the Kruppa equation. Firstly, the simplified Kruppa equations based on the SVD of the fundamental matrix is converted into the optimized cost function. Secondly, the minimum value of the optimized cost function is calculated by GA-PSO. Finally, the intrinsic parameters of the camera is obtained. The experimental results show that it is accurate, and the accuracy of the proposed method is obviously improved compared with the single optimization methods.
Keywords :
calibration; cameras; computer vision; genetic algorithms; image sensors; particle swarm optimisation; singular value decomposition; GA-PSO algorithm; Kruppa equation; SVD; active vision system; camera self-calibration method; fundamental matrix; genetic algorithm; optimized cost function; particle swarm optimization; single optimization methods; Accuracy; Calibration; Cameras; Cost function; Equations; Genetic algorithms; Mathematical model; Camera self-calibration; GA-PSO; Kruppa equation; genetic algorithm; particle swarm optimization;
Conference_Titel :
Cloud Computing and Intelligence Systems (CCIS), 2011 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-61284-203-5
DOI :
10.1109/CCIS.2011.6045050