Grade Differentiation Measure of Images
This paper describes application of newly developed grade differentiation measure between two datasets to images processing. Pixels of an image are transformed into a dataset of records describing pixels. Each pixel is characterized by its gray level, gradient magnitude and a family of variables n 1,...,n k , where k has been arbitrarily chosen. Grade Correspondence Cluster Analysis procedure implemented in program GradeStat allows reorder a sequence of records and divides pixels onto similar groups/subimages. Procedure GCCA takes a significant amount of time in the case of large images. Comparison of grade differentiation measures between variables allows to decrease the number of variables and the same to decrease processing time.
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