Articles liés à Histogram Equalization & PCA based Face Recognition...

Histogram Equalization & PCA based Face Recognition on MATLAB Platform: MATLAB based Histogram tech. & Face detection using Principle Component Analysis with detailed flow of Analysis & coding - Couverture souple

K. S., Naveen

 
9783659103667: Histogram Equalization & PCA based Face Recognition on MATLAB Platform: MATLAB based Histogram tech. & Face detection using Principle Component Analysis with detailed flow of Analysis & coding
Couverture souple
ISBN 10 : 3659103667 ISBN 13 : 9783659103667
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

This books deals with the implementation of histogram equalization and histogram matching in a simple step by step approach using Probability Distributive Function (PDF) and Cumulative Distributive Function (CDF) in MATLAB with detailed coding steps using suitable examples. Next is implementation of Principle Component Analysis based face detection. From “Principle Analysis Technique” we calculate covariance matrix & then we can calculate eigenvectors & eigenvalues for this matrix. The eigenvector with the highest eigenvalue is the principle component of data set.Unknown face reorganization is our next concern. By following the data base obtained from earlier face reorganization system, we can easily classify any test image. For implementation of this part first eigenfaces of both original images & testing images are calculated & an average value of the distance between each is calculated. A threshold is calculated from a fixed procedure. If the distance is less than threshold then the tested image is considered as face or else it is discarded. All techniques explained as implemented in MATLAB & complete working code has been given in book to help a Knowledge Thirst driven student.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

This books deals with the implementation of histogram equalization and histogram matching in a simple step by step approach using Probability Distributive Function (PDF) and Cumulative Distributive Function (CDF) in MATLAB with detailed coding steps using suitable examples. Next is implementation of Principle Component Analysis based face detection. From “Principle Analysis Technique” we calculate covariance matrix & then we can calculate eigenvectors & eigenvalues for this matrix. The eigenvector with the highest eigenvalue is the principle component of data set.Unknown face reorganization is our next concern. By following the data base obtained from earlier face reorganization system, we can easily classify any test image. For implementation of this part first eigenfaces of both original images & testing images are calculated & an average value of the distance between each is calculated. A threshold is calculated from a fixed procedure. If the distance is less than threshold then the tested image is considered as face or else it is discarded. All techniques explained as implemented in MATLAB & complete working code has been given in book to help a Knowledge Thirst driven student.

Biographie de l'auteur

Author received his U.G. degree B.E. in E&C from MVJCE,Bangalore in 2008 & P.G. degree M.Sc in Signal Processing & Communication Technology from MSRSAS,Bangalore in 2011. He joined the Dept. of R&D at System Controls, Bangalore in 2011 as a Design Engineer. His research areas include Signal & Image Processing, Embedded Design, Digital Wireless Comm

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3659103667
ISBN 13 
9783659103667
Reliure
Broché
Nombre de pages
60
Coordonnées du fabricant
non disponible
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