Masters Theses

Yupeng Zhang

Date of Award

8-2001

Degree Type

Thesis

Degree Name

Master of Science

Major

Electrical Engineering

Major Professor

Philip W. Smith

Committee Members

Marshall Pace, Hairong Qi

Abstract

Image segmentation is one of most difficult tasks in computer vision. It plays a critical role in object recognition of natural images. Unsupervised classification, or clustering, represents one promising approach for solving the image segmentation problem in typical application environments. The K-Means and Bayesian Learning algorithms are two well-known unsupervised classification methods. The K-Means approach is computationally efficient, but assumes imaging conditions which are unrealistic in many practical applications. While the Bayesian learning technique always produces a theoretically optimal segmentation result, the large, computational burden it requires is often unacceptable for many industrial tasks. A novel clustering algorithm, called Bayesian Weighted K-Means, is proposed in this thesis. Through simplification of the Bayesian learning approach's decision-making process using cluster weights, the new technique is able to provide approximately optimal segmentation results while maintaining the computational efficiency generally associated with the K-means algorithm. The capabilities of this new algorithm are demonstrated using both synthetic images with controlled amounts of noise, and real color images of cotton lint contaminated with non-lint material.

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