Comparison of model-based segmentation algorithms for color images
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Authors
Kupeli, Timur
Subjects
Maximum likelihood
Markov random field
Karhunen-Loeve Transformation
Markov random field
Karhunen-Loeve Transformation
Advisors
Therrien, Charles W.
Date of Issue
1987-03
Date
March 1987
Publisher
Language
en_US
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 a linear model for the image textures and on inverse filtering to estimate the image textures and their regions. Two specific methods are compered 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.
Type
Thesis
Description
Series/Report No
Department
Electrical and Computer Engineering
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
70 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
Copyright is reserved by the copyright owner