Parameterization of Mottle Textures.

Abstract

Parameterization of textures can be useful for detection of textual similarities and matching. In this project we have developed stochastic model to generate a set of parameters from the texture image domain and frequency domain. This model is aimed at quantification of textures for detection of similarities differences. Our attention has been concentrated on the parameterization of mottled textures. To test the model we have used it to generate texture images back from the parameters obtained from image analysis. The similarities and differences between the generated image and original images are used to refine and test the parametric model. Keywords: Textures, Parameterization, Stochastic model, Image domain, Texture synthesis, Texture classification, Power spectrum.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1987
Accession Number
ADA189570

Entities

People

  • C. M. Brown
  • Elizabeth Hinkelman
  • Sanjay K Jain

Organizations

  • University of Rochester

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Classification
  • Computer Science
  • Correlation Techniques
  • Data Science
  • Detection
  • Distribution Functions
  • Dynamic Range
  • Frequency
  • Frequency Domain
  • Orientation (Direction)
  • Power Spectra
  • Probability
  • Probability Distribution Functions
  • Probability Distributions
  • Statistical Analysis

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Image Processing and Computer Vision.
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers