Abstract
Image Analysis combined with multivariate regression on Angle Measure Technique (AMT) transformed imagery and the Theory of Sampling (TOS) is here presented as a comprehensive for Image Analysis Sampling (IAS), which takes all aspects of sampling representativity into account — especially 2-dimensional image versus 3-dimensioanl bulk compositions issues. Every IAS application has to be based on optimized image acquisition parameters: camera and illumination type, illumination angle, sample thickness as well as image post-processing, which are all examined here in order to delineate the general requirements for optimal prediction models for particle size distribution of natural and industrial bulk solid aggregates. We present a complete optimization study in order to show its intrinsic problem-dependent nature. This optimization allowed an original 60-sample data set to be compressed to an essential 22 natural coastal sands array with equally varying composition ranges — which was subjected to IAS in order to characterize the specific particle size distribution curves. In addition to D 50 (50%-tile), six other size classes were successfully predicted, while extreme size classes (extreme low or high particle sizes) showed a too narrow training data set span, illustrating a critical grain size contrast which will always bracket successful models of particulate matter being imaged for grain size characterization. All classes with a satisfactory (representative) calibration interval span can be quantitatively predicted due to the powerful scale-dependency of the central AMT feature extraction combined with PLS multivariate calibration. The present application to natural sand aggregate size distributions forms a vehicle to illustrate the full potential of image analysis in general, IAS in particular, also for technological/industrial manufacturing on-line product and process monitoring applications, or quality control purposes, with similar grain-size prediction objectives. There is a significant carrying-over potential to parallel industrial scenarios.
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