Testing a measure of image quality for acquisition control

Abstract Previous work by the author has shown that the entropy of an image's histogram can be used to control the acquisition variables (brightness, contrast, shutter speed) of a camera/digitiser combination in situations where the imaging conditions are changing. Although the control leads to histograms that satisfy pragmatic expectations of what a ‘good’ histogram should look like (i.e. filling the dynamic range of the digitiser without too much saturation), it avoids the problem of what we mean by a good histogram in the machine vision context and whether the control produces images that have these histograms. In this work a good image is defined to be one where the subsequent analysis algorithms work well. Three different algorithms, each containing many diverse components, are tested on sets of images with different acquisition parameters. As well as acquiring at different parameters, a simulation of the image acquisition process is derived and validated to assist evaluation. Test results show that near-optimal performance is obtained with maximum entropy and it is concluded that this measure is a suitable one for control of image acquisition.

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