.png){kind=icon}
Return
Automatic machine vision calibration using statistical and neural network methods
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2005.03.009.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (c) 2005 Elsevier B.V. All rights reserved.
Keywords:
automatic calibration
radial lens distortion
linear regression
artificial neural network
Journal
IF:
4.2
Papers:
4.1K
Citations:
6.7K
Organization
No organization information available
Cited Papers
ShareSaveShareSaveShareSaveShareSaveBiological Effects and Health Hazards of RF and MW Energy: Fundamentals and Overall Phenomenology
PREAI
ShareSave
ShareSaveShareSaveInvestigation on Microstructural Characterization and Mechanical Behaviour of Aluminium 6061 – CSFA / Sicp Hybrid Metal Matrix Composites.png)
Silicon
SiliconIF0
PREAI
ShareSave
View more