Feature space trajectory distorted object representation for classification and pose estimation

David P. Casasent; Leonard Neiberg; Michael A. Sipe

doi:10.1117/1.602017

1 March 1998 Feature space trajectory distorted object representation for classification and pose estimation

David P. Casasent, Leonard Neiberg, Michael A. Sipe

Author Affiliations +

Optical Engineering, Vol. 37, Issue 3, (March 1998). https://doi.org/10.1117/1.602017

Abstract

The feature space trajectory (FST) neural net is used for classification and pose estimation of the contents of regions of interest. The FST provides an attractive representation of distorted objects that overcomes problems present in other classifiers. We discuss its use in rejecting clutter inputs, selecting the number and identity of the aspect views most necessary to represent an object, and to distinguish between two objects, temporal image processing, automatic target recognition, and active vision.

Citation Download Citation

David P. Casasent, Leonard Neiberg, and Michael A. Sipe "Feature space trajectory distorted object representation for classification and pose estimation," Optical Engineering 37(3), (1 March 1998). https://doi.org/10.1117/1.602017

Published: 1 March 1998

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