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.
Whether or not a specific machine vision color camera is suitable for your application depends on a variety of factors. All these factors need to be considered when developing the most suitable color machine vision system for your application. This blog is part of a two-part series where we will cover the factors you need to consider when developing a color machine vision system. This blog is the first part that mentions four factors. Be sure to read Part 2 where we will discuss the other factors you’ll need to consider.
The first consideration that should be taken into account is the level of color accuracy and differentiation that is necessary for your application. In certain applications, it is crucial that the machine vision camera can distinguish how far off the detected color is from the target value. Machine vision users who require a high level of precision in this area need a more advanced camera than users for whom a lower level of precision and differentiation is acceptable.
Moreover, interpolation and low sensitivity are the two biggest obstacles standing in the way of reaching higher levels of color accuracy and differentiation. Interpolation can cause subtle differences in color detection since it takes the average of surrounding pixels to determine the color value of each pixel. Because of that, when your machine vision system attempts to differentiate subtle color variances, you may not know if the shades of color are actually different, or if they are just variations in the Bayer interpolation.
High degrees of color crosstalk influence the level of color accuracy that the machine vision camera can produce. High levels of crosstalk are the result of the considerable overlap between the spectral responses of the red, blue and green channels, as defined by either the Bayer color filters or the dichroic prism coatings. When there is a lot of overlap between channels, a significant amount of uncertainty is created for certain color families, particularly those in the yellow or teal families.
When your machine vision system needs to distinguish different shades of these colors, colour crosstalk can be very problematic. Therefore, when developing a color machine vision system, it is important to consider what color families are essential for your analyses, and what levels of color crosstalk would be acceptable in your machine vision system.
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Light level and sensitivity
Depending on your application, your machine vision system will require a specific level of sensitivity to light. Bayer, trilinear and prism cameras all transmit light differently and therefore vary in light sensitivity.
Bayer filters, for instance, not only are made of a material that provides lower light transmittance than the high-grade glass used in optical prisms, but the mosaic methodology also causes each pixel to be sensitive to only one-third of the wavelengths which might fall upon it. Depending on the exact color of a given pixel, this might result in over half of the light striking the filter never reaching the sensor. Based on the light levels under which your system will operate, and the levels of gain/noise that can be tolerated, you can choose the most suitable camera for your application.
White balancing and noise
White balancing is required for every machine vision application in which color is used. Without a well-defined baseline adjusted to the spectrum of the lighting that is being used by the system, there is no way to capture true color values accurately. Different methods of white balancing may be utilized, depending on the type of machine vision camera selected.
Bayer and trilinear cameras, for example, can only be white balanced by adding gain (amplification) to two of the three color channels in order to match the channel with the highest response. However, adding gain not only multiplies the signal, but it also multiplies the noise in your image. Any additional gain required due to overall low-light conditions would then be added to this baseline. If ultra-low noise is a requirement, this factor may need to be addressed, either by increasing the amount of available light or by switching to a different camera type.
A prism camera, by contrast, provides independent control over each sensor, including shutter speed as well as gain. This creates the option to use shutter speed for white balancing – either by lengthening the exposure time for the two channels with a lower response or shortening the exposure time for the two channels with the highest response. While noise may increase slightly if longer exposures are used, the increase is far less than when gain is applied. For some applications, this reduction in noise can be one of several justifications for the use of prism camera technology.
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