An Approach to Rotation Invariant Texture Classification and Some Experimental Results.

Abstract

This paper presents a new feature extraction method for classifying a texture image into one of the n possible classes, C sub i, i = 1,...,n. The extracted features are invariant under rotation or gray scale changes. Two types of random field models namely, Circular Auto Regressive model, and Simultaneous Auto Regressive model are used to extract these features. These models are fitted to a given MxM digitized image and their parameters are estimated. These estimated parameters and some functions of them constitute the desired rotation invariant feature vector. The classification power of this feature vector is demonstrated through experimental results obtained with twelve different classes of natural textures including both macrotextures and microtextures. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1983
Accession Number
ADA134197

Entities

People

  • A. Khotanzad
  • R. L. Kashyap

Organizations

  • Purdue University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Classification
  • Databases
  • Electrical Engineering
  • Engineering
  • Errors
  • Feature Extraction
  • Gray Scale
  • Grids
  • Information Science
  • Intensity
  • Maximum Likelihood Estimation
  • Military Research
  • Order Statistics
  • Orientation (Direction)
  • Statistics

Readers

  • Child and Adolescent Substance Abuse Science in Autism Spectrum Disorders.
  • Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference