By Ole Krogh Jørgensen | January 18, 2019
A lot of applications use color imaging as this is required to inspect and differentiate objects. The majority of these applications fall into three broad categories: color inspection, color sorting and color detection and matching. The degree to which a color machine vision system can perform in any of these categories depends on its ability to measure up to three key challenges.
Machine vision color cameras provide a host computer with pixel-level data generated by the reflected or incident light coming from the scene. While two cameras may both produce “pleasing” images, the specific pixel values may be different depending on the type, quality, and/or age of the cameras. The goal is to produce values that most closely match the “true” color value that might be calculated using precise laboratory equipment under the same lighting conditions.
Depending on the application, it might be crucial that the machine vision camera can distinguish subtle variances of the same color. For example, when multiple pieces of leather are used to create a purse or jacket, it is important that all the pieces for a single item have the same shade of color. Pieces with slightly different shades can be used together on other jackets or purses, but mixing different shades on a single item would not be acceptable.
Achieving high levels of differentiation requires color accuracy that can be calculated with a high degree of precision and repeatability. This is particularly challenging when light levels are lower because color values become compressed into a smaller range of possible values. The more sensitive the color camera, the better equipped it is to provide color differentiation, though other limiting factors may come into play.
As an example, color accuracy/color differentiation is very important in machine vision inspection of for example tablets in the pharmaceutical industry.
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Spatial resolution in color cameras
Just like in monochrome applications, many color applications must distinguish small image details in order to perform their task. They may need to read barcodes or QR-codes, or may need to exactly identify the edge of an object to perform measurements or to determine shapes and positions.
Cameras that rely on color filter arrays and Bayer interpolation to derive color information, create soft or blurry edges resulting from the process used to estimate the color value for each pixel. While for some applications this loss of detail may be acceptable or can be overcome by using higher resolution cameras, other applications may demand the use of prism camera technology in order to achieve the necessary combination of color accuracy and spatial precision.
Are you familiar with color imaging in machine vision cameras?
Despite the different challenges, more and more machine vision systems are using color to perform specific inspection tasks. How strong is your knowledge about color critical machine vision applications? Join our quiz and identify 10 machine vision applications where color is critical.