Markov Random Field Textures and Applications in Image Processing

Abstract

In the field of image compression, transmission and reproduction, the foremost objective is to reduce the amount of information which must be transmitted. Currently the methods used to limit the amount of data which must be transmitted are compression algorithms using either lossless or lossy compression. Both of these methods start with the entire initial image and compress it using different techniques. This paper will address the use of Markov Random Field Textures in image processing. If there is a texture region in the initial image, the concept is to identify that region and match it to a suitable texture which can then be represented by a Markov random field. Then the region boundaries and the identifying parameters for the Markov texture can be transmitted in place of the initial or compressed image for that region.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1997
Accession Number
ADA331947

Entities

People

  • Christopher A. Korn

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Boundaries
  • Compression
  • Computer Graphics
  • Computer Vision
  • Data Compression
  • Detection
  • Electrical Engineering
  • Hidden Markov Models
  • Image Processing
  • Mathematics
  • Neural Networks
  • Probability
  • Probability Distributions
  • United States Naval Academy

Readers

  • Materials Science and Engineering.
  • Radar Systems Engineering.
  • Statistical inference.