Comparison of Model-Based Segmentation Algorithms for Color Images.

Abstract

The objective of this thesis is to develop segmentation methods for multichannel and single channel images, and compare these methods. The segmentation algorithms are based on linear model for the image textures and on inverse filtering to estimate the image textures and their regions. Two specific methods are compared 1) A multichannel filtering algorithm that simultaneously models the three separate signals representing the intensity of red, green, and blue as a function of spatial position and 2) A single channel model applied to a combined image resulting from performing a Karhunen-Loeve transformation on the three signal components. Results of the multichannel image segmentation and the Karhunen-Loeve transformed one-channel image segmentation are presented and compared. Keywords: Maximum likelihood; Markov random fields; Computer programs; Theses. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1987
Accession Number
ADA181609

Entities

People

  • Timur Kupeli

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Computer Programs
  • Computer Vision
  • Computers
  • Covariance
  • Eigenvalues
  • Electrical Engineering
  • Engineering
  • Image Processing
  • Image Restoration
  • Image Segmentation
  • Maximum Likelihood Estimation
  • Probability
  • Probability Density Functions
  • Signal Processing
  • Statistical Analysis
  • Two Dimensional
  • White Noise

Fields of Study

  • Engineering

Readers

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