Matrox Imaging is one such machine vision
Comparative colour imaging is used to read the codes on these tyres. Accurate colour reproduction is not required for all applications. Image courtesy of Dalsa
A given calibration matrix will only last so
long, however, as even the most stable of LED light sources will degrade slightly over time. In colour-sensitive applications, colour reference samples known as ‘colour checkers’ can be used to recalibrate the machine vision system in situ. Colour checkers are just a piece of card with highly standardised colours painted onto them (not printed). Basler cameras, Schwaer says, allow the camera to calculate a new correction matrix automatically simply by focusing on a colour checker. ‘It’s a nice feature, and we like being able to allow our customers to do this on their site,’ says Schwaer.
Machines with human vision Dalsa’s Ben Dawson splits colour machine vision into two categories: human referenced colour, and comparative colour imaging. Human referenced, he says, refers to any imaging system that attempts to mimic how a human would judge colour, e.g. any system calibrated to the standard observer curve. ‘The complexity and variability of human vision make this kind of machine vision a challenging problem,’ he says. Applications for human referenced colour include print inspection, and matching of paints, pigments, and colorants – areas in which subtle differences are important. Comparative colour, on the other hand, asks
the machine vision system to learn certain colours and report on the presence of a colour, or how close a measured colour is to a specified colour. ‘There is a large market for this kind of machine vision, including agriculture, colour code reading, colour search (finding an object with a certain range of colours), industrial inspection, medical devices, and print inspection, such as the labels on cans or boxes or the printing on bottle caps,’ he says, adding that Dalsa has a significant presence in this market, along with other machine vision vendors.
vendor working on colour imaging products, and is particularly active in developing software-based tools for colour imaging. Arnaud Lina, head of the company’s software tool development team, explains that colour matching algorithms are the starting point for many, but not all comparative colour applications: ‘When the colours of the product you are inspecting are easy to match, you do not need a matching [software] tool,’ he says, adding that distinguishing between a red, a green, or a yellow would be an example of an easy matching task. ‘You would simply cluster the RGB in the hue-space, and that is enough… but when it’s time to deal with shadings or nuances of colour, or when looking at subtle changes of colour to determine if a product is fresh or not, for example, we are no longer
‘When the colours of the product you are inspecting are easy to match, you do not need a matching tool’
looking at large colour changes. Now we need to go with more advanced colour analysis algorithms.’ Since the first release of colour software tools
as part of the Matrox Imaging Library (MIL) in 2008, Matrox Imaging has added functionalities to deal with non-uniform colours, such as textures or mixtures of colours. In the food industry in particular, says Lina, a product does not possess a single colour, but it is more like a texture of mixed colours. While the company’s colour imaging
products use the comparative method for the time being, Matrox is aiming towards systems that can be more perfectly calibrated in the future. ‘This means learning the properties of the illumination light, and learning the properties of the camera sensor’s response to colour,’ says Lina. ‘So far the applications we’re working in do not require us to gain such an accurate knowledge of the colour system – that is the lighting, the product and the camera’s response – but more mature matching techniques will one day require this accurate colour matching.’ For truly accurate colour matching, however,
no camera can be as accurate as a spectrometer, as Lina is quick to acknowledge, noting that the two technologies are employed in different circumstances. ‘While the goal is ultimately to become as accurate as a spectrometer, it
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