Title :
Hybrid calibration of CCD cameras using artificial neural nets
Author :
Wen, J. ; Schweitzer, G.
Author_Institution :
Inst. of Robotics, ETH Zurich, Switzerland
Abstract :
The authors first discuss the physical and mathematical model of CCD (charge coupled device) cameras on which the standard photogrammetric calibration of the cameras is based. Then they introduce artificial neural networks in order to improve the classical calibration of the CCD cameras, and thus develop a new method to calibrate CCD cameras. In this set-up, a feedforward artificial neural network is used. Three advantages of the hybrid calibration are discussed: feasibility, applicability, and efficiency. In order to judge the quality of the calibration, the calibration error of a camera is defined. It is shown experimentally that the accuracy of the image frame coordinates has been improved by a factor two through the hybrid calibration. It appears to be a new idea to add an artificial neural network to the physical and mathematical model of a system in order to improve the overall description of the system
Keywords :
CCD image sensors; calibration; computer vision; neural nets; CCD cameras; calibration; image frame coordinates; machine vision; neural networks; Artificial neural networks; Calibration; Cameras; Charge coupled devices; Charge-coupled image sensors; Lenses; Machine vision; Mathematical model; Robot vision systems; Solid modeling;
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
DOI :
10.1109/IJCNN.1991.170424