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Feature Extraction Using Wavelet-PCA and Neural Network for Application of Object Classification & Face Recognition

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2 Author(s)
Chitaliya, N.G. ; Electron. & Commun. Eng. Dept., Sardar Vallabhbhai Patel Inst. of Technol., Vasad, India ; Trivedi, A.I.

With the increasing demands of visual surveillance systems, vehicle & people identification at a distance has gained more attention for the researchers recently. Extraction of Information from images and image sequences are vary important for the analysis according to the application. This research proposes feature extraction and classification method using Wavelet. The DWT is used to generate the feature images from individual wavelet sub bands. The feature images constructed from Wavelet Coefficients are used as a feature vector for the further process. The Principal Component Analysis (PCA) /Fisher Linear Discrimination analysis is used to reduce the dimensionality of the feature vector. Reduced feature vector are used for further classification using Euclidian distance classifier and neural network Classifier.

Published in:

Computer Engineering and Applications (ICCEA), 2010 Second International Conference on  (Volume:1 )

Date of Conference:

19-21 March 2010