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27 August 1993 Detecting blobs in multispectral electro-optical imagery using wavelet techniques
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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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Brian A. Telfer, Harold H. Szu, Abinash C. Dubey, and Ned H. Witherspoon "Detecting blobs in multispectral electro-optical imagery using wavelet techniques", Proc. SPIE 1961, Visual Information Processing II, (27 August 1993);

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