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Proceedings Paper

Cumulant slices for two-dimensional autoregressive signal modeling
Author(s): Tania Stathaki; Anthony G. Constantinides
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Paper Abstract

The research work reported in this paper is concerned with the use of higher order spectral estimation techniques as a means to deriving the parameters of 2D autoregressive (AR) models. Image analysis is examined from a higher order statistical perspective and in the context of noise. The objective is to develop analysis techniques through which robust autoregressive parameter estimation is accomplished. The approach taken involves the use of 2D AR models derive from third order cumulants. The directionality of the cumulant space influences the AR parameter estimation in a decisive manner. The specific application of the developed methods is in mammography, an area in which it is very difficult to discern the appropriate features. The results show significant discriminating gains through such techniques.

Paper Details

Date Published: 28 October 1994
PDF: 10 pages
Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); doi: 10.1117/12.190893
Show Author Affiliations
Tania Stathaki, Imperial College (United Kingdom)
Anthony G. Constantinides, Imperial College (United Kingdom)


Published in SPIE Proceedings Vol. 2296:
Advanced Signal Processing: Algorithms, Architectures, and Implementations V
Franklin T. Luk, Editor(s)

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