PCA based feature extraction for cell image retrieval

In this Dissertation work a Software programme has been developed which implement Principal Component Analysis for Cell Image retrieval:Principal component analysis, based on information theory concepts, seek a computational model that best describes a cell image, by extracting the most relevant inf...

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Main Author: Ananthagunan Muthukumaran
Other Authors: Mao Kezhi
Format: Theses and Dissertations
Language:English
Published: 2009
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Online Access:http://hdl.handle.net/10356/18755
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-187552023-07-04T15:28:17Z PCA based feature extraction for cell image retrieval Ananthagunan Muthukumaran Mao Kezhi School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing In this Dissertation work a Software programme has been developed which implement Principal Component Analysis for Cell Image retrieval:Principal component analysis, based on information theory concepts, seek a computational model that best describes a cell image, by extracting the most relevant information contained In that image. The Eigenimage approach is a principal component analysis method, in which a small set of characteristic pictures are used to describe the variation between cell images. The goal is to find out the eigenvectors (eigenimage) of the covariance matrix of the distribution, spanned by a training sot of cell mages. Later, every cell image is represented by a linear combination of these eigenvectors. Evaluations of these eigenvectors are quite difficult for typical image sizes but an approximation that is suitable for practical purposes is also presented. Recognition is performed by projecting a new image into the subspace spanned by the Eigen images and then classifying the image by comparing its position in Eigen space with the positions of known individuals. The Euclidian Distance method is adapted to find out the position of the new image in the subspace After Calculating the Euclidian Distance the images, which are stored, are retrieved back in the order of closeness with that of the test image. This is done by using the feature vector and Eigen images which are derived using PCA. Master of Science (Computer Control and Automation) 2009-07-17T06:28:21Z 2009-07-17T06:28:21Z 2008 2008 Thesis http://hdl.handle.net/10356/18755 en 81 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Ananthagunan Muthukumaran
PCA based feature extraction for cell image retrieval
description In this Dissertation work a Software programme has been developed which implement Principal Component Analysis for Cell Image retrieval:Principal component analysis, based on information theory concepts, seek a computational model that best describes a cell image, by extracting the most relevant information contained In that image. The Eigenimage approach is a principal component analysis method, in which a small set of characteristic pictures are used to describe the variation between cell images. The goal is to find out the eigenvectors (eigenimage) of the covariance matrix of the distribution, spanned by a training sot of cell mages. Later, every cell image is represented by a linear combination of these eigenvectors. Evaluations of these eigenvectors are quite difficult for typical image sizes but an approximation that is suitable for practical purposes is also presented. Recognition is performed by projecting a new image into the subspace spanned by the Eigen images and then classifying the image by comparing its position in Eigen space with the positions of known individuals. The Euclidian Distance method is adapted to find out the position of the new image in the subspace After Calculating the Euclidian Distance the images, which are stored, are retrieved back in the order of closeness with that of the test image. This is done by using the feature vector and Eigen images which are derived using PCA.
author2 Mao Kezhi
author_facet Mao Kezhi
Ananthagunan Muthukumaran
format Theses and Dissertations
author Ananthagunan Muthukumaran
author_sort Ananthagunan Muthukumaran
title PCA based feature extraction for cell image retrieval
title_short PCA based feature extraction for cell image retrieval
title_full PCA based feature extraction for cell image retrieval
title_fullStr PCA based feature extraction for cell image retrieval
title_full_unstemmed PCA based feature extraction for cell image retrieval
title_sort pca based feature extraction for cell image retrieval
publishDate 2009
url http://hdl.handle.net/10356/18755
_version_ 1772828768010764288