Target versus background characterization: second-generation wavelets and support vector machines

David J. Gorsich; Robert E. Karlsen; Grant R. Gerhart; Marc G. Genton

doi:10.1117/12.352950

14 July 1999 Target versus background characterization: second-generation wavelets and support vector machines

David J. Gorsich, Robert E. Karlsen, Grant R. Gerhart, Marc G. Genton

Proceedings Volume 3699, Targets and Backgrounds: Characterization and Representation V; (1999) https://doi.org/10.1117/12.352950
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

The problem of determining the difference between a target and the background is a very difficult and ill-posed problem, yet it is a problem constantly faced by engineers working in target detection and machine vision. Terms like target, background, and clutter are not well defined and are often used differently in every context. Clutter can be defined as a stationary noise process, anything non-target, or anything that looks like a target but is not. Targets can be defined by deformable templates, models, or by specific feature vectors. Models, templates and features must be defined before classification begins. Both models and feature vectors somehow hold the defining characteristics of the target, for example the gun barrel of a tank. Most importantly, feature vectors and models reduce the dimensionality of the problem making numerical methods possible. This paper explores several fairly recent techniques that provide promising new approaches to these old problems. Wavelets are used to de-trend images to eliminate deterministic components, and a trained support vector machine is used to classify the remaining complicated or stochastic components of the image. Ripely's K-function is used to study the spatial location of the wavelet coefficients. The support vector machine avoids the choice of a model or feature vector, and the wavelets provide a way to determine the non-predictability of the local image components. The K-function of the wavelet coefficients serves as a new clutter metric. The technique is tested on the TNO image set through several random simulations.

Citation Download Citation

David J. Gorsich, Robert E. Karlsen, Grant R. Gerhart, and Marc G. Genton "Target versus background characterization: second-generation wavelets and support vector machines", Proc. SPIE 3699, Targets and Backgrounds: Characterization and Representation V, (14 July 1999); https://doi.org/10.1117/12.352950

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