Paper
16 September 1994 Segmentation and modeling of textured images through combined second- and third-order statistical models
Tania Stathaki, Anthony G. Constantinides
Author Affiliations +
Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994) https://doi.org/10.1117/12.185960
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States
Abstract
In this paper the problem of texture image analysis in the presence of noise is examined from a higher-order statistical perspective and in the context of noise. The objective is to develop analysis techniques through which robust texture characteristics are extracted and used for texture modelling and segmentation. The approaches taken involve the use of autoregressive models whose parameters derived first from joint and weighted second and third order cumulants and secondly as a solution to a weighted overdetermined set of equations in which the weights are appropriate functions of the eigenvalue spread. The required segmentation of such 2D random fields is effected through the additional stage of a neural network having as inputs the extracted autoregressive parameters. The paper describes the fundamental issues of the various components of the approach.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tania Stathaki and Anthony G. Constantinides "Segmentation and modeling of textured images through combined second- and third-order statistical models", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); https://doi.org/10.1117/12.185960
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KEYWORDS
Autoregressive models

Image segmentation

Statistical analysis

Niobium

Modeling

Signal to noise ratio

Statistical modeling

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