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Proceedings Paper

Calculation of fractal dimension in the presence of nonfractal clutter
Author(s): Kenneth A. Herren; Don A. Gregory
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Paper Abstract

The area of information processing has grown dramatically over the last 50 years. In the areas of image processing and information storage the technology requirements have far outpaced the ability of the community to meet demands. The need for faster recognition algorithms and more efficient storage of large quantities of data has forced the user to accept less than lossless retrieval of that data for analysis. In addition to clutter which is not the object of interest in the data set, often the throughput requirements forces the user to accept 'noisy' data and to tolerate the clutter inherent in that data. It has been shown that some of this clutter, both the unavoidable clutter (clouds, trees, etc.) as well as the noise introduced on the data by processing requirements can be modeled as fractal or fractal-like. Traditional methods, using Fourier deconvolution on these sources of noise in frequency space, lead to loss of signal and can, in many cases, completely eliminate the target of interest. One parameter used to characterize fractal-like noise, the fractal dimension, has been investigated and fractal dimension images are presented.

Paper Details

Date Published: 9 March 1999
PDF: 12 pages
Proc. SPIE 3715, Optical Pattern Recognition X, (9 March 1999); doi: 10.1117/12.341303
Show Author Affiliations
Kenneth A. Herren, NASA Marshall Space Flight Ctr. (United States)
Don A. Gregory, Univ. of Alabama in Huntsville (United States)

Published in SPIE Proceedings Vol. 3715:
Optical Pattern Recognition X
David P. Casasent; Tien-Hsin Chao, Editor(s)

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