Accession Number:

ADA181609

Title:

Comparison of Model-Based Segmentation Algorithms for Color Images.

Descriptive Note:

Master's thesis,

Corporate Author:

NAVAL POSTGRADUATE SCHOOL MONTEREY CA

Personal Author(s):

Report Date:

1987-03-01

Pagination or Media Count:

71.0

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

Subject Categories:

  • Cybernetics
  • Optical Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE