Colour Profiling Using Multiple Colour Spaces

This paper presents an original method using colour histograms for flaw detection in automated industrial inspection. The colour histogram of an image is constructed by mapping the pixels into a colour space composed of discrete 3D colour intervals called colour bins. Swain [9] demonstrated the use of colour histograms to locate a target object in an image. The colour histograms of the target object and of the image are used to create a ratio histogram. The ratio histogram is back projected onto the image by replacing each pixel in the image by the value of the ratio histogram bin into which it falls. The resulting image is smoothed to reduce the effects of noise. The peak is found and taken to be the location of the target object. The method presented in this paper also uses histogram back projection; its novelty is in the construction of the colour profile, which is back projected in the place of the ratio histogram. The colour profile is the result of presenting the system with a set of training images; some known to contain flaws some without flaws. The colour profile is constructed by comparing the colour histograms of the images of flawed components to those of non-flawed components. This results in an array of weights, proportional to the frequency of each discrete colour occurring in flawed versus non-flawed images. The correct choice of colour space and the quantisation of the colour space are very important. These issues are thoroughly explored in this paper and a solution is presented which maximises both accuracy and speed, by using the RGB and HSV colour spaces in the training phase, and combining them into a single RGB based profile for real-time flaw detection. Using colour profiles has the following advantages: they are accurate, real time, and very importantly can be trained by the end user. Colour profiling could be applied successfully to other automated inspection problems.