Wavelet-based rotationally invariant target classification

Victoria T. Franques; David Alan Kerr

doi:10.1117/12.280791

28 July 1997 Wavelet-based rotationally invariant target classification

Victoria T. Franques, David Alan Kerr

Proceedings Volume 3068, Signal Processing, Sensor Fusion, and Target Recognition VI; (1997) https://doi.org/10.1117/12.280791
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

In this paper, a novel approach to feature extraction for rotationally invariant object classification is proposed based directly on a discrete wavelet transformation. This form of feature extraction is equivalent to retaining information features while eliminating redundant features from images, which is a critical property when analyzing large, high dimensional images. Usually, researchers have resorted to a data pre-processing method to reduce the size of the feature space prior to classification. The proposed method employs statistical features extracted directly from the wavelet coefficients generated from a three-level subband decomposition system using a set of orthogonal and regular Quadrature Mirror Filters. This algorithm has two desirable properties: (1) It reduces the number of dimensions of the feature space necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; (2) Regardless of the target orientation, the algorithm can perform classification with low error rates. Furthermore, the filters used have performed well in the image compression regime, but they have not been applied to applications in target classification which will be demonstrated in this paper. The results of several classification experiments on variously oriented samples of the visible wavelength targets will be presented.

Citation Download Citation

Victoria T. Franques and David Alan Kerr "Wavelet-based rotationally invariant target classification", Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997); https://doi.org/10.1117/12.280791

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

Includes PDF, HTML & Video, when available