Backpropagation Neural Network For Stereoscopic Vision Calibration
Abstract
Calibration is the process of establishing the relationship between a camera and a global coordinate systems. In the case of stereoscopic vision, the relationship between two cameras and a global coordinate system must be established. Many techniques have been proposed to perform the calibration process most requiring a substantial amount of programming and special test fixtures. This paper proposes a backpropagation neural network to estimate the transformation between two camera systems and a global coordinate system. The approach requires minimum programming and no special test fixtures. This paper describes the artificial neural network architecture along with the procedures used in training. Encouraging results are obtained from preliminary test runs.
Recommended Citation
M. B. Lynch and C. H. Dagli, "Backpropagation Neural Network For Stereoscopic Vision Calibration," Proceedings of SPIE - The International Society for Optical Engineering, vol. 1615, pp. 289 - 298, Society of Photo-optical Instrumentation Engineers, Mar 1992.
The definitive version is available at https://doi.org/10.1117/12.58798
Department(s)
Engineering Management and Systems Engineering
International Standard Serial Number (ISSN)
1996-756X; 0277-786X
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2023 Society of Photo-optical Instrumentation Engineers, All rights reserved.
Publication Date
01 Mar 1992
