Abstract: A method for automatically classifying test images based on their similares with a dictionary of example target and non-target images. The method operates by receiving a test image and then initializing variables for an iteration count and for the linear expansion of the test image. The test image is then projected onto each one of the target and non-target images in the dictionary, wherein a maximum scaling coefficient is selected for each iteration. A residue is then generated, and the linear expansion of the test image is increased until a predetermined number of iterations have been performed. Once this predetermined number of iterations have been performed, the sum of the scaling coefficients belonging to the target examples in the dictionary is compared to the sum of the scaling coefficients belonging to the non-target examples in the dictionary to determine whether the image is a target signal or a non-target signal.

Abstract: A data compression system includes an image preprocessor for generating a digitized, formatted set of pixels which is passed to an image transformer. The image transformer generates a set of wavelet coefficients from the formatted set of pixels via a series of one-dimensional wavelet transforms. A compression processor selects a subset of the wavelet coefficients for retention based on areas of interest in the original image and the position of coefficients within the set of coefficients. The compression processor then builds a compressed image file using a coefficient location identifier to reduce the size of the addresses that indicate the position of the selected coefficients within the set.