Adding Color to Machine Vision

Machine vision has evolved to become a fast and reliable tool for quality inspection. In many cases, a machine vision system can perform inspections more quickly and accurately than humans and at a lower cost. However, can a machine see in color? And, does introducing color into the equation help improve the quality of the inspection?

A machine vision system acquires images of an object with a camera and then uses computers to process, analyze, and measure various characteristics of that object so decisions can be made. One of the characteristics analyzed can be an object's color.

In the past, color was not widely used in optical inspection because of the cost and processing power required. However, as costs decrease and processing power ceases to be an issue, solutions providers are beginning to incorporate color into machine vision optical inspection systems to provide higher quality.

Color is Better, Right?

The basic assumption is that color is more advanced than black and white or monochrome so it must be better. However, this is not always true in machine vision.

DALSA offers the Genie, Trillium, and Piranha Cameras for machine vision, each based on a variety of distinct color imaging technologies including Bayer color filter array, beam splitter prism, and trilinear sensor.

There are some things that monochrome cameras do better than color cameras. Take resolution and speed, for example. There are more choices in high resolution and high speed when shopping for monochrome cameras.

In many cases, color images do not offer any advantage over monochrome images in resolving a machine vision problem, such as optical character recognition, optical character verification, bar-code reading, gauging, and applications dependent on high-resolution spatial information. For example, in typical inspection applications where defects like cracks or scratches are detected, the use of color is not necessary because the goal is to discern a difference in lightness on the object's surface.

How Machines See in Color

A machine cannot actually see in color. Machines use mathematical models to approximate human color detection and can be calibrated against the average human response to see color because it gives consistent responses to colors observed in a controlled setting. This calibrated color vision is useful for measuring and matching colorants such as in paint, plastics, and fabrics.

We don't think of this as seeing color like a human. It is important to make the distinction between the relative measurements that can be made with a machine vision system vs. absolute measurements that are only possible with devices such as photospectrometers.

Human color vision evolved to reliably extract information about the material properties of objects seen under huge variations of illumination and view. For example, fruit color has to be reliably determined despite varying illumination to pick ripe from unripe or bad fruit.

Human color vision has mechanisms to factor out variations in illumination and view that we don't know how or don't bother to put into machine color vision. It also is relative: Nearby colors influence the perception of a color, human color vision has low resolution, and there are wide differences between individuals.

As a result, human color vision is not a good measuring tool. Machine color vision is not influenced by nearby colors, can have high resolution, and does not vary much from machine to machine, making it a good measuring tool.

Types of Color Machine Vision Systems

Most color machine vision systems use a mix of hardware and software to detect colors. For point or spot color measures, a mostly hardware solution is fine. Sophisticated detection systems rely more heavily on software to give flexibility to the designer and user.

The main types of color cameras used in machine vision applications are 3CCD, trilinear, and Bayer pattern. These can be either area-scan or line-scan cameras depending upon the type of color incorporated.

3CCD has excellent color registration and can be applied to the majority of applications; however, the cost is higher. In a 3CCD color camera, color is selected using a prism-based interference filter that splits the incoming light into red (R), green (G), and blue (B) components.

Each of the three primary colors then is detected by a CCD, respectively, and the final color image is reconstructed by combining the outputs from the three CCDs. All three color images are captured at the same object spot and at the same time.

Trilinear provides high performance and has advantages in terms of its low cost. It can be used in many applications such as 100% print inspection. However, spatial correction cannot be achieved properly in certain applications that involve rotating or randomly moving objects.

In a trilinear color camera, three linear arrays are fabricated on one single die and coated with RGB color filters, respectively. These are absorbing filters using dye or pigment. In the trilinear camera, the three linear arrays detect a slightly different field of view (FOV) of the object, and spatial correction is needed in the reconstruction of a color image.

Bayer pattern cameras offer the lowest cost solution. These cameras tend to be used in lower-end applications and have reduced color precision compared to 3CCD and trilinear cameras. However, there is broad understanding of the Bayer pattern, and many algorithms exist to optimize its color performance.

When to Use Color

Aside from the obvious applications where the color of an object needs to be evaluated in some way, sometimes color can help make an inspection situation easier by facilitating the identification of objects, such as in the verification of fuse values in car fuse boxes. But should color eventually be used in all machine vision applications? No, because there are some things that monochrome cameras will always do better than color cameras.

When faced with an application that instills some doubt, ask yourself the following questions:
• Are the object's color quality and consistency key factors in the overall quality of the product?
• Can the object's color help you ascertain the relative quality of the product?
• Will color facilitate detection of the object?
• If the answer to any of these questions is yes, then take a serious look at the color side of machine vision.


Let's look at a few real-world applications that will help facilitate the decision of whether or not color should be part of your application.

Food probably is the one application that everyone understands the best. As daily consumers, we are constantly judging the quality and consistency of the food we buy. For fruit, color allows us to ascertain ripeness and grade product quality. In the case of grains and legumes, color helps to distinguish foreign matter in a steady stream of product. In meat processing, color can detect spoilage and discriminate areas of fat, bone, and gristle for automatic trimming.

Color machine vision even is used to inspect the build quality of frozen pizza. With a monochrome image, you might be able to tell if the density of ingredients is correct. But you will have a great deal of trouble identifying some of the chopped ingredients, such as orange, red, and green peppers. In color, they are easy to tell apart; in monochrome, not so much.

Color machine vision also is used in automotive inspection. Although you may think that exterior paint would be where machine vision is used, the bulk of the effort goes into inspecting the fine visual details that make up the user interface, such as ensuring the consistency and evenness of the instrument panel. This is important because the look and overall quality of the dashboard go a long way toward contributing to a driver's impression of a car.

Obviously, there are many other applications that could require color, like print quality and registration, pharmaceutical label verification, part presence and detection, and PCB assembly. In addition, there's a slew of quality and grading applications that involve color and texture classification for things like wood, textiles, and ceramic tile.

Software and Color Detection

Most color machine vision systems use a mix of hardware and software to detect colors. Major differences in the software approaches focus on the classifier that detects colors and assigns color pixels to a class such as good or bad. A good classifier has some tolerance to illumination changes, is quick to train and run, and reliably assigns pixels to their correct classes. Classifiers are an area of continuing development and competition among vendors.

The Future

Market studies by the Automated Imaging Association show that only about 25% of the cameras sold for imaging applications in 2005 and 2006 were color cameras; the rest were monochrome. But the trend is upward for sales of color cameras.

Most experts in the field believe the use of color will expand in machine vision. Color provides much more visual detail than monochrome grayscale and adds a new dimension in analyzing data in the real world.

For example, PCB inspection applications use color cameras to identify oxidized copper wires that would be difficult to see in a monochrome system. Color machine vision also is growing in popularity in bank-note inspection applications for scanning, processing, and confirming authenticity. In some Asian countries, color inspection is required by the government because people use seals rather than signatures when issuing personal checks. The seals are used generally with red ink, which has poor contrast in a monochrome system.

Better color fidelity, lower cost, and ease of use are the primary drivers in the market. And new technologies are continually being developed to address these needs.

About the Author

Robert Howison is project leader for OEM custom projects at DALSA. He holds a bachelor's and a master's of engineering from Ecole de Technologie Superieure and has more than 12 years experience in machine vision applications. DALSA Montreal, 7075 Place Robert-Joncas, Suite #142, St. Laurent, Quebec Canada, H4M 2Z2, 514-333-1301, e-mail: [email protected]

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