Matching pursuit filter design

A method has been devised of using localized information to detect objects with varying signatures without prior segmentation. The detection is performed by a new class of nonlinear filters called matching pursuit filters, which are trained on multiple examples of the object of interest. Matching pursuit filters are designed through a generalization of the matching pursuit algorithm that allows for the simultaneous decomposition of multiple images. The matching pursuit algorithm decomposes a signal into an adapted linear combination of wavelets. There are two implementations of the matching pursuit filter design algorithm. The first implementation detects objects by correlating an image with a kernel designed by the decomposition of an observation of the object of interest. The second method directly compares the coefficients of the decomposition of the training set with coefficients produced by the decomposition of an observed image. The algorithm has been used for detecting features on human faces, identifying faces and searching for man-made objects in infrared imagery.