Computational imaging technology has found its way into industrial automated inspection, where the creative use of illumination components has emerged as an enabling technology providing valuable imaging capabilities.

As the field of machine vision grows, it becomes increasingly important to understand what makes a machine vision system work well.

Part 1 of this three-part series examined how to identify characteristics of the object and the background you can use to create contrast with the illumination source for your machine vision application. This second part looks at how you go about choosing a light source to take advantage of the characteristics that create contrast. 

You have probably heard, and perhaps experienced, that lighting is a big challenge in applying machine vision and a vital key to its successful application. 

Much of the latest news surrounding machine vision is about machine learning and the innovations regarding algorithms. But those algorithms need data to perform correctly. The data in this case is the images. It is imperative to capture the best image possible so that the algorithms can perform at their highest level.

Whether an imaging system measures dimensions, verifies colors, or determines shape, the purpose of machine vision is to distinguish an object from its background. 

Machine vision systems are used throughout industry for critical inspection and measurement, frequently as part of a quality control approach.