Content-based image classification with circular harmonic wavelets

Giovanni Jacovitti; Alessandro Neri

doi:10.1117/12.316541

8 July 1998 Content-based image classification with circular harmonic wavelets

Giovanni Jacovitti, Alessandro Neri

Proceedings Volume 3389, Hybrid Image and Signal Processing VI; (1998) https://doi.org/10.1117/12.316541
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

Classification of an image on the basis of contained patterns is considered in a context of detection and estimation theory. To simplify mathematical derivations, image and reference patterns are represented on a complex support. This allows to convert the four positional parameters into two complex numbers: complex displacement and complex scale factor. The latter one represents isotropic dilations with its magnitude, and rotations with its phase. In this context, evaluation of the likelihood function under additive Gaussian noise assumption allows to relate basic template matching strategy to wavelet theory. It is shown that using circular harmonic wavelets simplifies the problem from a computational viewpoint. A general purpose pattern detection/estimation scheme is introduced by decomposing the images on a orthogonal basis formed by complex Laguerre-Gauss Harmonic wavelets.

Citation Download Citation

Giovanni Jacovitti and Alessandro Neri "Content-based image classification with circular harmonic wavelets", Proc. SPIE 3389, Hybrid Image and Signal Processing VI, (8 July 1998); https://doi.org/10.1117/12.316541

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