Morphological algorithms for modeling Gaussian image features

Abstract

Morphological algorithms1 for the parallel quantification and modeling of Gaussian image features are described. These algorithms are applicable to any image generation process which disthbutes the greyscale values according to a normal distribution. Morphological operators can be applied to the image data to obtain two parameter images, one consisting of mean positions and amplitudes and the other consisting of estimates of standard deviations, which are then used to "grow" (in parallel) the predicted Gaussian surfaces. Two methods to decompose and modulate the growth process (using the parameter images) are considered. One method grows the predicted Gaussian surface in terms of an approximating binomial distribution. The other method grows the desired Gaussian from smaller Gaussians of varying standard deviations.