Scale-Space Image Analysis Based on Hermite Polynomials Theory

Abstract

The Hermite transform allows to locally approximate an image by a linear combination of polynomials. For a given scale ? and position ?, the polynomial coefficients are closely related to the differential jet (set of partial derivatives of the blurred image) for the same scale and position. By making use of a classical formula due to Mehler (late 19th century), we establish a linear relationship linking the differential jets at two different scales ? and positions ? involving Hermite polynomials. For multi-dimensional images, anisotropic excursions in scale-space can be handled in this way. Pattern registration and matching applications are suggested. We introduce a Gaussian windowed correlation function K (?) for locally matching two images. When taking the mutual translation parameter ? as an independent variable, we express the Hermite coefficients of K (?)interms of the Hermite coefficients of the two images being matched. This new result bears similarity with the Wiener-Khinchin theorem which links the Fourier transform of the conventional (flat-windowed) correlation function with the Fourier spectra of the images being correlated. Compared to the conventional correlation function, ours is more suited for matching localized image features. Numerical simulations using 2D test images illustrate the potentials of our proposals for signal and image matching in terms of accuracy and algorithmic complexity.