Machine Vision

Machine vision, also referred to as computer or robot vision, is a term that describes the many techniques by which machines visually sense the physical world. These techniques, used primarily for monitoring industrial manufacturing, are becoming increasingly popular as today's manufacturing environments become more automated and quality control standards increase. Whether the task is to sort and assemble a group of machined parts, to determine if a label has been placed properly on a soda bottle, or to check for microscopic defects in an automotive door panel, machine vision plays an essential role.

The most common type of machine vision system is one which is responsible for examining situations twodimensionally. These two-dimensional systems view a scene in much the same way that a person views a photograph. Cues such as shapes, shadows, textures, glares, and colors within the scene allow this type of vision system to be very good at making decisions based on what essentially amounts to a flat picture.

Shape

Like humans, most machine vision systems are designed to use shape as the defining characteristic for an object. For these systems then, it is important to make an object's shape as easy to isolate as possible. Both proper illumination of the object and efficient computer processing of the image of that object are necessary.

Illuminating from behind is the most straightforward optical way to make an object's shape stand out. The resulting silhouette effect is the same as that which occurs when a moth is seen flying in front of a bright light. To an observer a few feet away, the moth's colors pale, and the contrast between the moth and the background is enhanced so its shape and size become the only discernable characteristics. For a machine vision system, an image of this silhouette is much easier to process than a conventional image.

Oftentimes, unfortunately, optical techniques alone do not make an object's shape stand out clearly enough. For these situations, computer software-based techniques are generally employed. These routines perform mathematical operations on the electronic image of the scene to convert it into an image that is easier to interpret. Commonly used software routines can enhance the contrast of an image, trace out the edges of objects within an image, and group objects within an image.

Surface texture

Another defining characteristic for an object is its surface reflectivity. This cue is most often used for distinguishing between objects made from different materials and for distinguishing between objects of the same material but with different surface finish (such as painted or unpainted objects). At the extremes, an object is considered either a specular reflector or a diffuse reflector. If it is specular, it tends to act like a mirror, with most of the light bouncing off at the same angle with which it struck the surface (with respect to a surface normal). This is the case for a finely polished piece of metal, a smooth pool of water, or even oily skin to some extent. If, on the other hand, the surface is diffuse, light is reflected more or less evenly in all directions. This effect is caused by roughness or very slight surface irregularities, and is the reason objects made from materials like wood or cloth generally appear softer in tone and can be distinguished from those made from metal.

Color

Often an object's color or color pattern can serve as its identifying feature. Every object has a color signature that is determined by its material and its surface coating. Spectroscopic (color sensing) machine vision systems are cued to make decisions based on this feature and typically operate in one of two ways. Both techniques illuminate an object with white light, but one looks at the light reflected by the object while the other looks at the light transmitted through the object for identification.

The simplest color sensing systems are responsible for monitoring only one color across a scene. These are typically used in quality control applications such as monitoring of paints, to ensure consistency between batches made at different times. More sophisticated color sensors look at the color distribution across a twodimensional image. These systems are capable of complex analysis and can be used for checking multi-colored labels or for identifying multi-colored objects by their color patterns.