Statistical grey-level models for object location and identification

摘要

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. To deal with variation we build statistical models of the grey levels across the structure in a set of training examples. A multi-resolution search technique is used to locate the best match to the model in an area of a new image to sub-pixel accuracy. A fit measure with predictable statistical properties can then be used to determine the probability that best match is a valid example of the model. 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.