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Proceedings Paper

Morphological algorithms for modeling Gaussian image features
Author(s): Chakravarthy Bhagvati; Peter Marineau; Michael M. Skolnick; Stanley R. Sternberg
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Paper 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.

Paper Details

Date Published: 1 November 1991
PDF: 8 pages
Proc. SPIE 1606, Visual Communications and Image Processing '91: Image Processing, (1 November 1991); doi: 10.1117/12.50352
Show Author Affiliations
Chakravarthy Bhagvati, Rensselaer Polytechnic Institute (United States)
Peter Marineau, Rensselaer Polytechnic Institute (United States)
Michael M. Skolnick, Rensselaer Polytechnic Institute (United States)
Stanley R. Sternberg, Shoshona, Inc. (United States)

Published in SPIE Proceedings Vol. 1606:
Visual Communications and Image Processing '91: Image Processing
Kou-Hu Tzou; Toshio Koga, Editor(s)

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