Computational Models for Texture Analysis and Synthesis

Abstract

Numerous computational methods for generating and simulating binary and grey-level natural digital-image textures are proposed using a variety of stochastic models. Pictorial results of each method are given and various aspects of each approach are discussed. The quality of the natural texture simulations depends on the computation time for data collection, computation time for generation, and storage used in each process. In most cases, as computation time and data storage increase, the visual match between the texture simulation and the parent texture improves. Many textures are adequately simulated using simple models thus providing a potentially great information compression for many applications.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1981
Accession Number
ADA102470

Entities

People

  • David D. Garber

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computational Science
  • Computer Programming
  • Computers
  • Data Science
  • Electrical Engineering
  • Gray Scale
  • Image Processing
  • Information Processing
  • Information Science
  • Pattern Recognition
  • Probabilistic Models
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Two Dimensional
  • Word Processors

Fields of Study

  • Computer science

Readers

  • Computational Fluid Dynamics (CFD)
  • Computer Vision.
  • Systems Analysis and Design