Type of Document Master's Thesis Author renkjumnong, wasuta - Author's Email Address wasuta4051@hotmail.com URN etd-07142007-031112 Title SVD and PCA in image processing Degree Master of Science Department Mathematics and Statistics Advisory Committee
Advisor Name Title Marina Arav Committee Chair Frank Hall Committee Member Michael Stewart Committee Member Saeid Belkasim Committee Member Zhongshan Li Committee Member Keywords
- Principal component analysis
- Singular value decomposition
- Image
Date of Defense 2007-06-22 Availability unrestricted Abstract The Singular Value Decomposition is one of the most useful matrix factorizationsin applied linear algebra, the Principal Component Analysis has been called
one of the most valuable results of applied linear algebra. How and why principal
component analysis is intimately related to the technique of singular value decomposition
is shown. Their properties and applications are described. Assumptions
behind this techniques as well as possible extensions to overcome these limitations
are considered. This understanding leads to the real world applications, in particular,
image processing of neurons. Noise reduction, and edge detection of neuron
images are investigated.
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