When you are developing a color machine vision system, you will have a lot of options to choose from. First of all, you will have to choose between an area scan or line scan color camera. Based on this choice, you will need to choose between a bayer color camera, trilinear camera or prism technology.
This blog is part of a three-part series where we will cover the factors you need to consider when developing a color machine vision system. This blog is the second part that mentions four factors. Be sure to read part 1 and 2 where we discuss other factors you’ll need to consider.
Color space & color space conversion
When developing a machine vision system, you need to decide which color space is best for your particular application. The exact color space depends on what the application is intended to do and how the color information will be analyzed.
For example, applications that simply display objects on a screen, would naturally make use of standard RGB color spaces since that is how all monitors construct the color of their pixels. But if you are dealing with printed material instead, a slightly modified color space like Adobe RGB might be a better choice because it offers a slightly wider selection of colors that are tailored to digital printing.
Other color spaces like HSI (hue, saturation, intensity) and the CIE XYZ or CIE L*a*b * color spaces use mathematical coordinates to describe colors in such a way that it is easier for certain applications to calculate color matches and color variances in terms of both degree and direction.
In most applications, you will use algorithms and processing resources on the host computer to convert the RGB data coming from your camera(s) to the color space that is best suited to your application. However, in some situations, you may prefer the camera to to perform this conversion while your host processing resources focus on other tasks. For these cases, it is worth selecting cameras which have built-in color space conversion capabilities.
Color enhancement & color optimization
In some cases you may find it valuable to intentionally change the accuracy of your color. If so, color enhancement and optimization capabilities are worth considering when developing your machine vision system.
For example, if you want to detect a particular deviation in the image or distinguish two objects from each other, it can sometimes help to enhance a specific color in your image. Distinguishing blood cells from tissue, for example, can be done more easily when the red color in the image is enhanced.
You can enhance colors in your image after it is captured by using an algorithm on the host computer. However, post-processing enhancements may be limited by the saturation or contrast of the raw image. Some cameras are equipped with color optimization capabilities that allow users to enhance specific primary or complementary colors by as much as 200 percent. System builders should consider whether such a capability can add value to their application or help to differentiate it from competitive systems.