A lattice network for signal representation using Gaussian basis functions and max-energy paradigm
- 9 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Describes two novel schemes for efficient representation of 1-D and 2-D signals using Gaussian basis functions (BFs). Special methods are required since the Gaussian functions are nonorthogonal. The first method employs a paradigm of maximum energy reduction interlaced with the A* heuristic search. The second method uses an adaptive lattice system to find the optimal projections of the BFs onto the signal, and a lateral-vertical suppression network to select the most efficient representation in terms of data compression Author(s) Ben-Aire, J. Dept. of Electr. & Comput. Eng., Illinois Inst. of Technol., Chicago, IL, USA Rao, K.R.Keywords
This publication has 4 references indexed in Scilit:
- The generalized Gabor scheme of image representation in biological and machine visionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1988
- Complete discrete 2-D Gabor transforms by neural networks for image analysis and compressionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
- A Computational Approach to Edge DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1986
- A Formal Basis for the Heuristic Determination of Minimum Cost PathsIEEE Transactions on Systems Science and Cybernetics, 1968