This paper presents a new image representation method based on anisotropic refinement. It has been shown that wavelets are not optimal to code 2-D objects which need true 2-D dictionaries for efficient approximation. We propose to use rotations and anisotropic scaling to build a real bi-dimensional dictionary. Matching Pursuit then stands as a natural candidate to provide an image representation with an anisotropic refinement scheme. It basically decomposes the image as a series of basis functions weighted by their respective coefficients. Even if the basis functions can a priori take any form bi-dimensional dictionaries are almost exclusively composed of two-dimensional Gabor functions. We present here a new dictionary design by introducing orientation and anisotropic refinement of a gaussian generating function. The new dictionary permits to efficiently code 2-D objects and more particularly oriented contours. It is shown to clearly outperform common non-oriented Gabor dictionaries.

Projection pursuit learning networks (PPLNs) have been used in many fields of research but have not been widely used in image processing. In this paper we demonstrate how this highly promising technique may be used to connect edges and produce continuous boundaries. We also propose the application of PPLN to deblurring a degraded image when little or no apriori information about the blur is available. The PPLN was successful at developing an inverse blur filter to enhance blurry images. Theory and background information on projection pursuit regression (PPR) and PPLN are also presented.

Fast hierarchical image segmentation through irregular pyramids