Segmentation of Noisy Images Using Nonstationary Markov Fields.

Abstract

The purpose of this thesis is to develop an algorithm for segmenting images corrupted by a high level of noise with different characteristics. In particular the images considered are composed of several regions describing different objects and background. The algorithm described is based on a Markov Random Field model of the image and uses Kalman Filtering techniques and Dynamic Programming in order to smooth within the regions. The theoretical background for one dimensional and two dimensional data which have different characteristics and simulation results are presented, with examples of synthetic data and underwater images. Keywords: Markov Random field; Dynamic programming; Kalman filtering techniques.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1987
Accession Number
ADA195229

Entities

People

  • Kani Hacipasaoglu

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computer Programming
  • Computer Programs
  • Computer Vision
  • Computers
  • Data Science
  • Dynamic Programming
  • Filtration
  • Gaussian Noise
  • Image Processing
  • Image Segmentation
  • Kalman Filtering
  • Kalman Filters
  • Simulations
  • Statistical Analysis
  • Statistical Estimation

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.