On Two-Dimensional ARMA Models for Image Analysis.

Abstract

The two-dimensional autoregressive moving-average (2-D ARMA) models have been useful for image coding and compression, statistical image modeling, image feature extraction and segmentation, texture characterization, and image restoration and enhancement. This paper provides a critical review of the 2-D ARMA models for image analysis. Particular emphasis is placed on restoration of noisy images using 2-D ARMA models. Computer results are presented for comparative evaluation study. Problem areas such as order determination are examined. Although there are shortcomings and unsolved problems it is concluded that the models are very effective linear models for image analysis. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 24, 1980
Accession Number
ADA083094

Entities

People

  • Chia‐Hung Chen

Organizations

  • University of Massachusetts Dartmouth

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Classification
  • Compression
  • Computer Programming
  • Computer Vision
  • Computers
  • Data Science
  • Electrical Engineering
  • Engineering
  • Feature Extraction
  • Image Restoration
  • Information Science
  • Pattern Recognition
  • Probability
  • Recognition
  • Signal Processing
  • Statistics
  • Two Dimensional

Readers

  • Computer Vision.
  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference