imaging and machine vision europe february/march 2011
www.imveurope.com
10
colour imaging
Does it come in puce...?
Technical progress and increased demand is seeing colour imaging advance from its established print inspection market to a host of other applications. Stephen Mounsey looks at the challenges of reproducing colours in machine vision
Colour is often said to be a matter of taste, and in some applications of colour imaging this can be taken literally. What colour is a ripe banana? It’s a simple question, and it usually has a simple answer: yellow. Ask a scientist or engineer working with colour machine vision, however, and the answer would probably be more complicated. In the rigid, physical terms of machine vision, colour ceases to be an intuitive property of an object, and becomes a complex interplay between the source of illumination, the absorption and scatter properties of the subject, the angles of illumination and viewing, and the response characteristics of the sensor. If you asked such a scientist or engineer what colour a banana is, the response should be ‘under what conditions?’ The Mk.1 Eyeball (or the human eye as we
know it) sets a high standard when it comes to colour perception, boasting a dynamic range many orders of magnitude greater than that of any electronic sensor. In addition, the visual cortex of the brain is able to gauge illumination conditions, subconsciously adjusting our perception of colour in order to maintain colour constancy – a term used by psychophysicists (those who study human perception). Colour constancy means that a ripe banana looks yellow to the human eye, whether it’s viewed sideways or upright, in bright sunlight or under the glow of a fluorescent tube. Imaging systems are not yet able to perform
this on-the-fly adjustment, and so many factors must be controlled if a true-colour representation is required, as Ben Dawson, director of strategic development at Dalsa, explains: ‘Colour machine vision doesn’t have colour constancy, so we must artificially limit the lighting, the illumination and the view
geometry.’ Sunlight, he says, is the standard to which illumination for colour imaging is compared, as it is spectrally uniform, although difficult to use in a controlled environment. ‘We always have to start with a white light,’ he says, ‘but not all white lights are the same; if you have a “white” LED, it actually tends to be quite blue because of the way the phosphors are generated. Likewise, compact fluorescent tubes have spikes at certain frequencies. Incandescent bulbs do have a good spectrum, but their
‘We have learned that colour reproduction is dependent on the light source. When you’re using candle light, objects appear very red’
output varies with respect to their drive current, so they are not often used.’ Incandescent bulbs and sunlight are both examples of black body radiators, meaning that their output colours can be characterised by the single value of colour temperature – around 5,500K for sunlight and around 3,000K (more red) for a halogen bulb. Michael Schwaer, senior product manager at Basler Vision Technologies, confirms that selecting illumination for colour imaging applications can be a challenge: ‘We have learnt that colour reproduction is very dependent on the light source we use,’ he explains. ‘You can imagine that, when you’re using candle
light, it makes the objects you’re looking at appear very red. If you’re using xenon light or a halogen bulb, the object appears differently. We therefore have to compensate our colour correction for whatever light source we’re using.’
Schwaer says different applications put
different demands on the imaging system in terms of the quality of the colour reproduction required, and the calibration requirements for each may be different. ‘To separate greens, from yellows, from reds, in a traffic light application for example, is not so difficult, but when we have an application in food inspection, or medical device manufacture, it is important to reproduce the natural colours as closely as possible.’ The human eye is particularly sensitive to
green colours, and with good reason: ‘During the evolution of the eye, early humans would have gained an advantage from being able to discern subtle differences in the colour of plants. In nature there is not so much red or blue,’ says Schwaer. In contrast, the highest sensitivity of CCD or CMOS sensors used in machine vision cameras is at red wavelengths. ‘We know that the sensitivities of the cameras we are using are at a different range to those of the human eye – and so, in order to reproduce colours as the eye sees them, colour cameras must be calibrated to the sensitivity curve of the human eye.’ This sensitivity curve is defined by the International Commission on Illumination (CIE) – the ‘standard observer’ curve. For each light source in use, the image processing software (or the camera itself) will apply a calibration matrix, adjusting the intensities recorded by the sensor to correspond to the colours in the standard observer curve.
Dalsa’s Boa cameras are suited to comparative colour imaging
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