1 November 1991 Morphological algorithms for modeling Gaussian image features
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Abstract
Morphological algorithms for the parallel quantification and modeling of Gaussian image features are described. These algorithms are applicable to any image generation process which distributes the gray-scale 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 parameters 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.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chakravarthy Bhagvati, Chakravarthy Bhagvati, Peter Marineau, Peter Marineau, Michael M. Skolnick, Michael M. Skolnick, Stanley R. Sternberg, Stanley R. Sternberg, } "Morphological algorithms for modeling Gaussian image features", Proc. SPIE 1606, Visual Communications and Image Processing '91: Image Processing, (1 November 1991); doi: 10.1117/12.50352; https://doi.org/10.1117/12.50352
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