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Proceedings Paper

Detecting blobs in multispectral electro-optical imagery using wavelet techniques
Author(s): Brian A. Telfer; Harold H. Szu; Abinash C. Dubey; Ned H. Witherspoon
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Paper Abstract

Wavelet processing followed by a neural network classifier is shown to give higher blob detection rate and lower false alarm rate than simply classifying single pixels by their spectral characteristics. An on-center, off-surround wavelet is shown to be highly effective in removing constant-mean background areas, as well as ramping intensity variations that can occur due to camera nonuniformities or illumination differences. Only a single wavelet dilation is tested in a case study, but it is argued that wavelets at different scales will play a useful role in general. Adaptive wavelet techniques are discussed for registration and sensor fusion.

Paper Details

Date Published: 27 August 1993
PDF: 9 pages
Proc. SPIE 1961, Visual Information Processing II, (27 August 1993); doi: 10.1117/12.150975
Show Author Affiliations
Brian A. Telfer, Naval Surface Warfare Ctr. (United States)
Harold H. Szu, Naval Surface Warfare Ctr. (United States)
Abinash C. Dubey, Naval Surface Warfare Ctr. (United States)
Ned H. Witherspoon, Naval Surface Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 1961:
Visual Information Processing II
Friedrich O. Huck; Richard D. Juday, Editor(s)

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