| Abstract: | ![]()
This article has as its goal the development and test and evaluation of wavelet-based algorithms for automatically detecting unknown anomalies in two-dimensional images. The general idea behind the work is that the class of wavelet transforms induces a so-called multiresolution analysis (MRA) in image space whereby the image of interest is naturally decomposed into a sequence of images of varying resolution (from coarse to fine resolution) in a computationally efficient manner. The anomaly detection can then be performed sequentially beginning at a coarse scale (low resolution) and proceeding to finer scales as needed. The wavelet representation thus effectively allows the user to zoom in on particular areas of interest and thus detect image anomalies in a very efficient manner. The article includes results from computer simulations testing the proposed approach against a standard energy-detection algorithm for the unknown anomalies embedded in additive Gaussian white noise. © 1996 John Wiley & Sons, Inc. |
