A philosopher or scientist may ask, “Is the red you see the same as the red I see?” or “What is yellow?” But the etymologist may ask, “Why have some cultures called green and blue the same color?”
Perceptions and definitions of color have long been in flux. For example, the medieval term sinople, which is derived from the source of red pigment at Sinople on the Black Sea, could refer to red or green. The Latin flavus, meaning yellow, is etymologically linked to English blue, French bleu, and German blau. The Japanese awo can mean green, blue, or dark, depending on the context.
How could this color ambiguity arise? One explanation may be that the concept of color itself has varied over time. The ancient Greeks saw a different spectrum than Sir Isaac Newton. To them, white (or light) was at one end and black (or dark) was at the other. All other colors lay along the scale between these extremes and were mixtures of the two.
Engineers who hope to use color to inspect, sort, or verify parts may be interested in such profound questions. However, they will also need practical models of color and algorithms with which to build a machine-vision system.
In his Product Focus feature on this topic, editor Andy Wilson skirts the profundity and focuses on explaining human color vision and how the multiple scientific models of color space may be applied to machine vision. Understanding the challenges of color machine vision begins with understanding the three kinds of color-sensitive cones in the retina of the eye. Translating the language of human vision into comparable computer code requires the proper image sensors and image-processing software.
Other articles in this issue show how machine vision is helping translate natural resources into electricity. Contributing editor Charlie Masi describes a system that uses vision and a robot to inspect and sort solar cells, while carbon-based energy is the subject of Andy Wilson’s article on how an infrared-based machine-vision system is improving the thermal efficiency of coal.
Translating technical information into a form of energy that can be used by the machine-vision industry is our mission at Vision Systems Design, and we are continually exploring new methods of presenting this information to ur audience. Our web site, for example, holds numerous videos of systems—including those that use color, robots, and infrared systems. Taken during the recent International Robots, Vision & Motion Control Show, these videos are linked to related technical articles on our site.
Just as color can be interpreted in many different ways, so too machine-vision and image-processing systems can use many different technologies and products. Creating a common, perhaps less colorful language and standards around these technologies and products will help OEM vendors, system integrators, and end users more easily develop vision systems.
W. Conard Holton, Editor in Chief
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