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.
"synopsis" may belong to another edition of this title.
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
"About this title" may belong to another edition of this title.
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9783659103667
Seller: moluna, Greven, Germany
Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: K. S. NaveenAuthor 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, Ban. Seller Inventory # 5131534
Quantity: Over 20 available
Seller: Studibuch, Stuttgart, Germany
paperback. Condition: Sehr gut. 60 Seiten; 9783659103667.2 Gewicht in Gramm: 500. Seller Inventory # 1465608
Seller: Mispah books, Redhill, SURRE, United Kingdom
Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book. Seller Inventory # ERICA79636591036676
Quantity: 1 available