Quality 101: Machine Vision Fundamentals

A machine vision system is a group of components that can acquire an image on demand, and can be configured or programmed to perform extensive analysis of that image to extract useful data about the object being inspected. The image may be full color, but most applications work well with a gray-scale image, which may be processed more quickly and usually at higher resolution with respect to cost.

Machine vision systems interface with other automation components via discrete I/O signals, Ethernet, device net, serial and other communication schemes. For certain applications, a machine vision system can control the entire inspection process as a standalone operation without external communication. However, one true value of machine vision as a quality tool is its ability to collect and archive discrete and statistical data about a process, providing the quality engineer with information that can help improve a production process.

A common misconception surrounding machine vision is that it “takes a picture” of a good part and then compares subsequent part images to that picture. While this analysis technique is one capability of some systems, most machine vision image features are extracted by recognizing and processing individual geometric objects in an image. Some of the common algorithms that may be incorporated in an application include edge extraction, contrast measurement, blob analysis and pattern matching—although modern machine vision processors offer dozens of analysis and processing tools. These tools range from simple to complex, and are usually combined to form an inspection process suitable for the target application.

It is important to have a thorough understanding of how each tool works with an image and produces data in order to select the proper set of algorithms to achieve the desired inspection results. However, there is a much more critical aspect to machine vision implementation that impacts each and every application from specification to integration