Advanced Color Sensors Using Color Space

May 5, 2009
The color-space system assigns numeric coordinates to each parameter of a color, uniquely identifying the color and defining its position within the color space.

Advanced color sensors detect and sort products by color in factory-automation applications for quality-control purposes. These sensors identify and distinguish between multiple colors, hues, saturations, brightness, and other parameters using a technique called color space.

There are many color-space models, though most color spaces are laid out in three dimensions. Each axis in a color space quantifies a different color parameter. The color-space system assigns a numeric coordinate to the color for each parameter, uniquely identifying the color and defining its position within the color space. One advantage to this technique is that it helps gauge how one color compares to another by calculating the absolute distance between the two color points within the 3D space.

The choice of color-space model depends upon the particular application. One application may need a specific color-space model, while another application may need an entirely different set of parameters.

One of the most common color spaces is called Lab space. The “L” axis of this color space identifies the lightness or darkness of the color. That is, one end of the axis represents lighter and lighter colors going to total white while the other end handles darker and darker colors to black. The “a” and “b” axes of this color space identify the amount of red-green and blue-yellow hues that make up the color, respectively. So all three coordinates in Lab color space combine to identify the color, much like a home-improvement store combines different amounts of base color pigments when creating a new color of paint. The colorspace coordinate is the unique “recipe” of the color.

Other color spaces, such as XYZ, Luv, and u’v’L, map colors using different attributes. Some color spaces define their coordinates in ways that may not be as straightforward to visualize, but may be helpful for certain applications. For example, u’v’L color space builds on more amorphous color attributes such as saturation and hue angle. But this color space can be most useful in identifying colors that actively emit light, such as the color of lit LEDs.

Pepperl+Fuchs ( supplied information for this column.

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