Detecting blobs in multispectral electro-optical imagery using wavelet techniques

Brian A. Telfer; Harold H. Szu; Abinash C. Dubey; Ned H. Witherspoon

doi:10.1117/12.150975

27 August 1993 Detecting blobs in multispectral electro-optical imagery using wavelet techniques

Brian A. Telfer, Harold H. Szu, Abinash C. Dubey, Ned H. Witherspoon

Proceedings Volume 1961, Visual Information Processing II; (1993) https://doi.org/10.1117/12.150975
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

Citation Download Citation

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); https://doi.org/10.1117/12.150975

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