Performing Machine Vision Tasks

This section describes how to perform many common machine vision inspection tasks. The most common inspection tasks are detecting the presence or absence of parts in an image and measuring the dimensions of parts to determine if they meet specifications.

Measurements are based on characteristic features of the object represented in the image. Image processing algorithms traditionally classify the type of information contained in an image as edges, surfaces and textures, or patterns. Different types of machine vision algorithms leverage and extract one or more types of information.

Edge detectors and derivative techniques—such as rakes, concentric rakes, and spokes—use edges represented in the image. They locate, with high accuracy, the position of the edge of an object. You can use edge detection to make such measurements as the width of the part, which is a technique called clamping. You also can combine multiple edge locations to compute intersection points, projections, circles, or ellipse fits.

Pattern matching algorithms use edges and patterns. Pattern matching can locate, with very high accuracy, the position of fiducials or characteristic features of the part under inspection. You can combine those locations to compute lengths, angles, and other object measurements.

The robustness of your measurement relies on the stability of your image acquisition conditions. Sensor resolution, lighting, optics, vibration control, part fixture, and general environment are key components of the imaging setup. All elements of the image acquisition chain directly affect the accuracy of the measurements.

The following figure illustrates the basic steps involved in performing machine vision.

Note  Diagram items enclosed with dashed lines are optional steps.