Gram-Gabor approach to optimal image representation

Moshe Porat; Yehoshua Y. Zeevi

doi:10.1117/12.24161

1 September 1990 Gram-Gabor approach to optimal image representation

Moshe Porat, Yehoshua Y. Zeevi

Proceedings Volume 1360, Visual Communications and Image Processing '90: Fifth in a Series; (1990) https://doi.org/10.1117/12.24161
Event: Visual Communications and Image Processing '90, 1990, Lausanne, Switzerland

Abstract

The Gram determinant technique is applied to signal representation by non-orthogonal bases. A special case of image representation in biological and machine vision using Gabor elementary functions (GEFs) is considered. It is shown that, in general, the Gram determinant is a better approach to computation of the expansion coefficients than the one using bi-orthonormal auxiliary functions. An optimal representation by finite sets of coefficients is attained without a significant computational effort and the resultant reconstruction error converges monotonically with the addition of basis’ components to the reconstruction set. The Gram approach appears to be in a better accord with biological findings, regarding information processing along the visual pathway, compared to the conventional bi-orthogonal scheme.

Citation Download Citation

Moshe Porat and Yehoshua Y. Zeevi "Gram-Gabor approach to optimal image representation", Proc. SPIE 1360, Visual Communications and Image Processing '90: Fifth in a Series, (1 September 1990); https://doi.org/10.1117/12.24161

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