Comparison of model-based segmentation algorithms for color images
Therrien, Charles W.
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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.
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