
Proceedings Paper
Gaussian smoothing of sparse spatial distributions as applied to informational differenceFormat | Member Price | Non-Member Price |
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Paper Abstract
The characterization of separation between object spectral distributions by the use of any divergence-evolved
method, such as Informational Difference is problematic due to the relative sparsity of said
distributions. The existence of zero-probability points renders the calculation result irrelevant as the
separation is either infinite or undefined. A method to surmount this problem using available
experimental data is proposed.
We consider the statistical nature of measurement for all available visual data, e.g. pixel values, and
model the spectral distributions of these pixels as a congregate of Gaussian statistic measurements. The
inherent nature of Gaussian distributions smoothes over the zero-probability points of the original
discrete distribution, solving the divergence problem. The parameters of the Gaussian smoothing are experimentally determined.
Paper Details
Date Published: 7 May 2007
PDF: 5 pages
Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 656519 (7 May 2007); doi: 10.1117/12.718362
Published in SPIE Proceedings Vol. 6565:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)
PDF: 5 pages
Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 656519 (7 May 2007); doi: 10.1117/12.718362
Show Author Affiliations
Y. Ultchin, Institute for Advanced Research and Development (Israel)
Weizmann Institute of Science (Israel)
Weizmann Institute of Science (Israel)
D. Sheffer, Institute for Advanced Research and Development (Israel)
Published in SPIE Proceedings Vol. 6565:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)
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