The first part of this series covered the basic principles that make high-accuracy measurements possible for machine vision. The second part of the series looked at challenges to accurate measurements due to the application conditions. This third part looks at characteristics and components of the vision system that can limit the achievable accuracy.

The first part of this three-part series covered the principles that allow machine vision to make high-accuracy measurements. This second part examines challenges that the measurement environment imposes on machine vision and gives approaches to mitigate the effects and retain much of the high-accuracy capability.

Machine vision can measure with greater precision and accuracy than human vision. This series starts by exploring techniques for high-precision measurements in vision systems. The next installments will examine challenges and solutions for maintaining this precision and accuracy. First, we'll clarify key terms related to measurement accuracy.

Before discussing the trends, let’s lay some groundwork by discussing the market drivers for machine vision that ultimately power the trends.

Progress in machine vision processing is charted in major developments that prove significant staying power.

How small can pixels on an image sensor become? We probably don’t know the answer except that they might become very, very small.

You’ve been tasked with integrating a machine vision system. What does integration entail? This article covers the activities you will typically have to handle when integrating a vision system.

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. 

You’ve learned about light sources, lenses, cameras, camera interfaces, and image processing software. Now, you may be wondering exactly how to design and implement a complete, successful machine vision system.