Physically-Accurate Synthetic Images for Machine Vision Design

Abstract

In machine vision applications, accuracy of the image far outweighs image appearance. This paper presents physically-accurate image synthesis as a flexible, practical tool for examining a large number of hardware/software configuration combinations for a wide range of parts. Synthetic images can efficiently be used to study the effects of vision system design parameters on image accuracy, providing insight into the accuracy and efficiency of image-processing algorithms in determining part location and orientation for specific applications, as well as reducing the number of hardware prototype configurations to be built and evaluated. We present results illustrating that physically accurate, rather than photo-realistic, synthesis methods are necessary to sufficiently simulate captured image gray-scale values. The usefulness of physically-accurate synthetic images in evaluating the effect of conditions in the manufacturing environment on captured images is also investigated. The prevalent factors investigated in this study are the effects of illumination, the sensor non-linearity and the finite-size pinhole on the captured image of retroreflective vision sensing and, therefore, on camera calibration was shown; if not fully understood, these effects can introduce apparent error in calibration results. While synthetic images cannot fully compensate for the real environment, they can be efficiently used to study the effects of ambient lighting and other important parameters, such as true part and environment reflectance, on image accuracy. We conclude with an evaluation of results and recommendations for improving the accuracy of the synthesis methodology.