Automatic machine vision calibration using statistical and neural network methods

• Published in: Image and vision computing Vol. 23; no. 10; pp. 887 - 899
• Main Authors: Smith, Lyndon N., Smith, Melvyn L.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 20.09.2005
• Subjects: Artificial neural network; Automatic calibration; Linear regression; Radial lens distortion; Radial lens distortion; Automatic calibration; Artificial neural network; Linear regression
• ISSN: 0262-8856; 1872-8138
• DOI: 10.1016/j.imavis.2005.03.009

A methodology is presented for camera calibration that is designed to improve the accuracy of machine vision based object measurement systems. The regression and artificial neural network techniques studied are considered to be complimentary rather than competitive. Neural networks have been identified as being particularly useful for the precise modelling of non-linear response, and offer the additional benefits of being non-prescriptive and generally applicable to factors such as radial lens distortion, manufacturing errors and minor camera misalignments. The combination of these modelling techniques within automated program control strategy is suggested as a new approach for straightforward and accessible machine vision calibration. The method has particularly good application to vision metrology and reverse engineering tasks. A demonstrator system has been constructed, employing a scanning laser line and vision system for object measurement in three-dimensions. Experimental results are presented along with a demonstration of the reduction in measurement error that can be attained through the application of regression analysis and artificial neural network modelling.