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Related Experiment Video

Updated: Jul 7, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Image modeling using inverse filtering criteria with application to textures.

T E Hall¹, G B Giannakis

¹Dept. of Inf. Technol. and Commun., Virginia Univ., Charlottesville, VA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 1, 1996

Summary

This summary is machine-generated.

This study introduces advanced statistical methods for image modeling, moving beyond second-order statistics to capture complex phase properties in asymmetric autoregressive moving-average models. This enables more accurate texture analysis and synthesis.

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Area of Science:

Digital Image Processing
Statistical Signal Processing
Texture Analysis

Background:

Traditional image modeling relies on first- and second-order statistics, limiting the capture of non-Gaussian random field phase properties.
This often necessitates symmetric model parameters and spatial reversibility, assumptions not always valid for texture images.

Purpose of the Study:

To develop and implement novel inverse filtering criteria for parameter estimation of asymmetric noncausal autoregressive moving-average (ARMA) image models.
To utilize higher-than-second-order statistics for more comprehensive image modeling.

Main Methods:

Derivation of two classes of inverse filtering criteria using higher-than-second-order statistics.
Employment of Finite Impulse Response (FIR) inverse filters to handle models with zeros on the unit bicircle.
Identification of the minimal set of cumulant lags for model identifiability.

Main Results:

Successful parameter estimation for asymmetric noncausal ARMA image models.
Demonstration that the proposed FIR inverse filters can handle models with zeros on the unit bicircle.
Establishment of estimator consistency and evaluation of performance through simulations, texture classification, and synthesis.

Conclusions:

The proposed methods effectively estimate parameters for asymmetric noncausal ARMA image models, overcoming limitations of traditional approaches.
Higher-order statistics and FIR inverse filters provide a more robust framework for image modeling, especially for textures.
The study advances texture analysis and synthesis capabilities through improved statistical modeling.