![]() However, there are some problems with this system, too. Machine vision vendors often provide the ability to automatically convert an image from RGB to HSI coordinates. In the HSI coordinate system, even when lighting intensity changes, the hue coordinate of an object should stay relatively constant. Of course, we don't always have ideal lighting conditions, and machine vision manufacturers have addressed this problem by changing from the rectangular RGB coordinate system to the more cylindrical HSI (hue, saturation, intensity) coordinate system. The three components of the vector may be identified with the level of excitation of each phosphor of a CRT monitor necessary to generate the desired color, or the amount of current that would be generated by a pixel in a CCD camera after incident light has passed through a filter of the corresponding color. One of the most common spaces is RGB (red, green, blue). The particular space we choose to represent a color may vary with the problem at hand. Indeed, the traditional methods discussed above are applicable to individual pixels and groups of nearby pixels representing the same featureless color field under ideal uniform lighting conditions. We can also reduce the uncertainty in any color measurement by taking the mean of nearby pixels representing the same color (perhaps forgetting that the objective of the process may be to determine whether these same pixels actually represent the same color). To allow for uncertainty in measurements, we can measure the standard deviation and set a threshold based on this standard deviation for differentiating colors. If they are identical the distance will be zero. It should be easy enough to compare two color vectors by comparing their individual components, or by measuring the distance between them in the three-dimensional space. So we may wonder how there can be any problem performing color-based recognition. We know that every pixel of every image displayed on our computer screen consists of one of these vectors just waiting to be processed. We're told by textbooks and machine vision manufacturers that our familiar vector techniques can be applied to comparing colors. We also have formulas that use standard deviation and variance to represent our uncertainty about individual values, and we have correlation coefficients to measure similarity. ![]() We have algorithms for measuring the distances and angles between vectors and for converting vectors from one coordinate space to another depending on the requirements of the problem. We call them vectors, and we know how to identify them with a three-dimensional space. We engineers are comfortable with sets of three independent variables. Therefore, any single color visible to a human can be represented by three numbers. And, should a family crisis or daydream about our vacation intrude, we can become downright unreliable.įor most practical recognition purposes, machine vision systems represent colors as vectors in a three-dimensional "space." Color machine vision analysis starts with the fact that the normal human eye relies on the differential response to three pigments, each sensitive to a different portion of the electromagnetic spectrum, to provide us with the sensation of color. Another is that no matter how dedicated we are, few of us can maintain the concentration necessary to perform such a boring task for extended time periods. Of course, human inspectors have several drawbacks. Some reference examples can be stuck to the wall in case we need a refresher. Even if we have no special training, we're ready to inspect an object after seeing just a couple examples with the correct colors. If a trained human inspector with good color vision cannot see the problem, consumers are unlikely to notice it either. Machine Although inroads have been made in automating color-based assembly verification, manufacturers still rely on human inspectors, especially when subtle differences are involved. It’s much easier to detect the presence of a copper ring in a color image (left) than a monochrome image (right).
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