Statistical Grey-Level Models for Object Location and Identification

This paper presents a new method for modelling and locating objects in images for applications such as Printed Circuit Board (PCB) inspection. Objects of interest are assumed to exhibit little variation in size or shape from one example to the next, but may vary considerably in grey-level appearance. Simple correlation based approaches perform poorly on such examples. We demonstrate how a statistical model based approach combined with a multi-resolution search can accurately locate objects and reliably distinguish between good and bad components. We describe a 'bootstrap' approach to training and a method of automatically refining the final model to improve its performance. We demonstrate the method on PCB inspection, showing the approach is robust enough for use in a real production environment.