NEURAL NETWORKS FOR PRECISE MEASUREMENT IN COMPUTER VISION SYSTEMS

Abstract

Although computer vision systems have been successfully applied to some inspection tasks, they were generally not considered as precise measurement tools due to dimensional distortion and errors. This paper presents procedures to correct these errors for precise measurement. The first step is to formulate calibration models for image coordinate systems using neural networks. Then neural networks to model dimensional errors from the initial measurement are structured in a learning stage using standard parts. Finally these models are used to correct measurement errors in measurement tasks. These proposed procedures are implemented as an example.