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Proceedings Paper

Best Fit Edge Detection For Meteorological Data
Author(s): Douglas DeMasters; Michael Andrews
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Paper Abstract

The texture, contrast and "noisiness" of meteorological data belongs to the class of visual images that requires unconventional and non-classical processing techniques. For instance, the digital Laplacian operator cannot be directly applied to these images without further modification. This is vividly portrayed in the application of classical edge, detection techniques to visual images that are very "busy", which tends to amplify the granularity of an image rather than generate useful edge detection. In this paper, a comparative study of classical edge detection techniques is described with actual atmospheric data obtained from geostationary satellite data. These results are compared to novel techniques developed at the Image Processing Center for Atmospheric Studies at Colorado State University.

Paper Details

Date Published: 9 January 1979
PDF: 7 pages
Proc. SPIE 0155, Image Understanding Systems and Industrial Applications I, (9 January 1979); doi: 10.1117/12.956725
Show Author Affiliations
Douglas DeMasters, Colorado State University (United States)
Michael Andrews, Colorado State University (United States)

Published in SPIE Proceedings Vol. 0155:
Image Understanding Systems and Industrial Applications I
Ram Nevatia, Editor(s)

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