SVD and PCA in Image Processing

SVD and PCA in Image Processing

The Singular Value Decomposition is one of the most useful matrix factorizations in 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...

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Main Author: Renkjumnong, Wasuta -
Format: Others
Published: Digital Archive @ GSU 2007
Subjects:
Principal component analysis
Singular value decomposition
Image
Online Access:http://digitalarchive.gsu.edu/math_theses/31
http://digitalarchive.gsu.edu/cgi/viewcontent.cgi?article=1030&context=math_theses
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spelling ndltd-GEORGIA-oai-digitalarchive.gsu.edu-math_theses-10302013-07-23T06:15:09Z SVD and PCA in Image Processing Renkjumnong, Wasuta - The Singular Value Decomposition is one of the most useful matrix factorizations in 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. 2007-07-16T07:00:00Z text application/pdf http://digitalarchive.gsu.edu/math_theses/31 http://digitalarchive.gsu.edu/cgi/viewcontent.cgi?article=1030&context=math_theses Mathematics Theses Digital Archive @ GSU Principal component analysis Singular value decomposition Image
collection NDLTD
format Others
sources NDLTD
topic Principal component analysis
Singular value decomposition
Image
spellingShingle Principal component analysis
Singular value decomposition
Image
Renkjumnong, Wasuta -
SVD and PCA in Image Processing
description The Singular Value Decomposition is one of the most useful matrix factorizations in 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.
author Renkjumnong, Wasuta -
author_facet Renkjumnong, Wasuta -
author_sort Renkjumnong, Wasuta -
title SVD and PCA in Image Processing
title_short SVD and PCA in Image Processing
title_full SVD and PCA in Image Processing
title_fullStr SVD and PCA in Image Processing
title_full_unstemmed SVD and PCA in Image Processing
title_sort svd and pca in image processing
publisher Digital Archive @ GSU
publishDate 2007
url http://digitalarchive.gsu.edu/math_theses/31
http://digitalarchive.gsu.edu/cgi/viewcontent.cgi?article=1030&context=math_theses
work_keys_str_mv AT renkjumnongwasuta svdandpcainimageprocessing
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