Wavelet feature extraction for image pattern recognition

John Scott Smokelin

doi:10.1117/12.241999

7 June 1996 Wavelet feature extraction for image pattern recognition

John Scott Smokelin

Author Affiliations +

Proceedings Volume 2751, Hybrid Image and Signal Processing V; (1996) https://doi.org/10.1117/12.241999
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

A method of extracting improved features for object identification by correlating with a wavelet filter is described. The wavelet filter used is a linear combination of Gabor wavelets, which is designed by a neural network algorithm to extract features that are useful for discriminating different classes of objects. The neural network algorithm achieves this by iteratively adapting the filter parameters and linear combination weights of the wavelet filter so that the features extracted maximize the Fisher ratio between the classes. The algorithm thus provides an automated technique of designing a filter which extracts improved features for identification. Results are presented which show the ability of these improved features to increase the classification performance of a pattern recognition system.

Citation Download Citation

John Scott Smokelin "Wavelet feature extraction for image pattern recognition", Proc. SPIE 2751, Hybrid Image and Signal Processing V, (7 June 1996); https://doi.org/10.1117/12.241999

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE