Abstract
Machine vision calibration is an important step to obtaining usable measurements in inspection and automation operations. Conventional calibration techniques require the development of elaborate mathematical models and have prior knowledge of many parameters. Creating a suitable model and obtaining reasonable values for some calibration parameters is often difficult and error prone. In this article, a neural network approach is presented as an indirect non-linear optimization method to machine vision calibration. This approach does not require a mathematical model be developed nor any prior knowledge about the setup or calibration parameters. A universal calibration approach is developed and utilized in various applications. The applications are discussed and results from experiments are presented. It is shown that a neural network approach can provide an accurate machine vision calibration.
Recommended Citation
M. B. Lynch et al., "Use of Feedforward Neural Networks for Machine Vision Calibration," International Journal of Production Economics, vol. 60, pp. 479 - 489, Elsevier, Apr 1999.
The definitive version is available at https://doi.org/10.1016/S0925-5273(98)00199-6
Department(s)
Engineering Management and Systems Engineering
International Standard Serial Number (ISSN)
0925-5273
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
20 Apr 1999
