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Frequently Asked Questions |
Why color machine vision?
Monochrome (black and white or gray scale) machine vision is now well established as a tool for alignment, gauging, and optical character recognition. Color is often useful to simplify a monochrome problem by improving contrast or separation. However, the greatest potential uses of color vision, human or machine, are for cost-effective object recognition, classification and assembly inspection. For a few of the many applications.What do we mean by color?
Color is a visual object attribute which results from the combined output of three sets of retinal cones each sensitive to different portions of the visible part of the electromagnetic spectrum. The cones have peak sensitivities in the red, green and blue portions of the spectrum respectively. Any perceived color may usually be created by a variety of sets of "primary" colors when combined in the correct proportions.What is a color space?
A color space is a means of representing the three components of a color in terms of a position in a (usually) three dimensional space. RGB, HSI, LAB and CIE are some of the many color spaces which may be used, depending on the particular purpose of the analysis. A very complete discussion of color spaces for video and computer graphics may be found at Rick Davis's website as well as many other fine sites on the internet.What is RGB color space?
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Most video monitors use red, green, and blue color sources as the primary colors for color image generation. RGB color space uses a rectangular coordinate system with one coordinate axis assigned to each of three color components, red, green, and blue. RGB coordinates are commonly used for specifying and coding computer generated images. |
What is HSI color space?
For most practical machine vision purposes,
HSI may be regarded as the same space as RGB but represented in a different coordinate system.
This diagram shows how the RGB cube would appear viewed from the black end, looking in the direction of the black-white diagonal.The HSI coordinate system is cylindrical with the intensity (I) axis coinciding with the black-white diagonal of the RGB system. Saturation (S) is the radius from the intensity axis and hue (H) is the angle with respect to (usually) the red direction. When we assign a single name to a color it is usually the hue to which we refer. A number of slightly different representations of the exact transformation between HSI and RGB are in common use. |
I've been told that it is very important to work in HSI space rather than RGB space. Why does it matter what color space is used?
Many traditional applications of color machine vision are aimed at differentiating single color objects from the background for alignment and gauging purposes. As long as the colors in the image are reasonably well saturated hue will tend to remain relatively constant in the presence of shadows and other lighting variations. In such cases an image based on hue alone may work better with standard alignment and gauging tools than traditional gray scale analysis.Unfortunately, when colors have low saturation (lie near the black-gray-white axis) hue may be difficult to determine accurately; when saturation is zero hue is undefined. For systems which must be able to differentiate all colors, saturated and unsaturated, HSI representation can introduce significant problems. We have found that for general recognition and classification of objects which may be multicolored, contrary to conventional wisdom, the disadvantages of HSI space will almost always outweigh any possible advantages.
Which color space does WAY-2C use?
WAY-2C, like the human visual system, uses RGB space. Our patented analysis methods use the probability distribution of the complete 3-dimensional color vector rather than the one-component-at-a-time approach of most other systems. Although our methods are equally applicable to RGB or HSI space, experience and theory both show that, in contrast to conventional wisdom, RGB is almost always preferable.What is supervised classification?
Supervised classification is classification in which a system, living or machine, learns to recognize objects or situations by the example of a trainer who knows the "correct answers". In contrast, unsupervised training involves the person or machine learning on their own without guidence from a trainer. WAY-2C uses supervised classification.What is information theory?
Information (or communication) theory is based on the pioneering work (1) of Claude Shannon of Bell Labs and MIT during the 1940's. It is concerned with understanding, measuring, and optimizing the efficiency of information transfer. Information theory is central not only to modern communication technology, but also to the understanding of natural languages, and the communication of information by other natural information processing systems. Organisms which transfer information with less than optimum efficiency from sense organs such as the eyes to the portions of the brain which must take appropriate action may have a significantly lowered chance of long term survival.What is Minimum Description Classification?
Minimum description is an information theory based approach to supervised pattern recognition which handles complex data distributions well. Its unifying theme is data analysis based on minimizing the amount of information necessary to describe a set of observations (2). Based on comparison of probability distributions, minimum description is consistent with both maximum likelihood estimation and the time honored philosophy of the simplest explanation being the best. It also lends itself to simple geometrical interpretations.I've heard a lot about the need for complex and specialized algorithms for color based recognition. Don't I need very powerful computers or special expertise for this type of application?
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Our experience shows that most practical color based classifications can be performed with the simple, consistent, minimum description approach and are easily handled by modern personal computers. After all, birds, bees and other creatures with small brains and little formal mathematical training are adept at recognizing objects based on color distributions. |
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Can WAY-2C recognize textures?
Since textures in images affect the color distribution statistics, much textural information is automatically included in all WAY-2C training and classification without any additional effort. This has been adequate for all practical applications we have encountered to date. However, should we ever encounter an application which challenges the standard WAY-2C, we have even more sophisticated tools awaiting the call.Why does WAY-2C succeed where the competition fails?
All competing systems of which we are aware offer color as an extension to their traditional monochrome machine vision products. In contrast, WAY-2C is designed specifically to recognize objects based on their color distributions. It is the product of over a decade of research by a team of Ph.D's with expertise in optical physics, information theory, and data analysis.Most existing color machine vision systems which offer statistical matching analyze the three color components separately. However both common sense and a rigorous analysis show that this approach throws away critical color information thus dramatically decreasing the number of color patterns which can be distinguished and increasing the probability of errors.
Also, traditional statistical matching methods for color machine vision are based on simplifying assumptions about the nature of the color distribution. These assumptions are valid in only a tiny fraction of the potential applications of color machine vision; when they are invalid the classification methods based on them are more likely to produce the wrong answers.
By analyzing all the color components simultaneously, and using a more appropriate patented matching criterion based on information theory, WAY-2C generally produces classifications very consistent with those of human inspectors.
WAY-2C has been proving itself since 1992 in a wide variety of challenging industrial, government, and military inspection and process control applications.
If you are disappointed with the performance of your present color machine vision system, and are serious about wanting to improve it, contact us. We'll give you an honest, no obligation, appraisal of whether WAY-2C can help. Then, if you choose, we'll work with you to see how a WAY-2C system can be implemented with the minimum possible disruption of your existing inspection process and the maximum use of your present hardware and software.
Does WAY-2C require tedious and expensive threshold setting?
NO. After all, humans and animals don't normally use thresholds for color-based classification. Why should your color machine vision system? Some old-fashioned systems which rely on separate analysis of the color components try to classify complex color distributions by a few manually set thresholds. Instead, WAY-2C learns almost instantly by example, automatically setting hundreds of parameters to be used in the identification. Experience shows that WAY-2C is more reliable over the long term than human inspectors, and is is at least as good, if not better than humans in recognizing subtle color differences in side by side comparisons.In the verification/anomaly detection mode, when only a single reference class is used, WAY-2C does allow adjustment of a threshold indicating the how far the object being inspected can differ from the reference before being rejected. However, even here, a theoretically defined default threshold value handles most cases.
How can I tell whether WAY-2C will work in my application?
Demonstrations, including prototype scripts for new applications, can usually be arranged. We can also hook you up with actual users who can share their experiences. Ask us about your application.Can I write my own GUI to interface to WAY-2C ?
Yes. Most integrators and OEM's do.Why do you use that ugly gray background for the WAY-2C website?
For technical reasons, which we won't go into here, images captured from machine vision video cameras often appear much darker on computer monitors than they do on a normal TV monitor for which such cameras were originally designed. This is particularly true when viewed against a very light or white background on the monitor. We could of course lighten the images with almost any image editing software before placing them on our website. Unfortunately, if we did this, the images we show on the website would no longer represent undoctored examples of WAY-2C's performance. So, as a compromise we use a somewhat darker background to lessen the contrast with the already dark images.