Model-based matching using elliptical features

James R. Burrill; Sharon X. Wang; Art Barrow; Mike Friedman; Matt Soffen

doi:10.1117/12.241136

24 May 1996 Model-based matching using elliptical features

James R. Burrill, Sharon X. Wang, Art Barrow, Mike Friedman, Matt Soffen

Proceedings Volume 2756, Automatic Object Recognition VI; (1996) https://doi.org/10.1117/12.241136
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

This paper describes a method of using elliptical features in model matching that forms the basis of a system for vehicle detection and classification. The novelty of the system is the employment of an algorithm that utilizes both line and ellipse features simultaneously. The baseline algorithm has been successfully used in images, which contain straight line features, to find a discrete correspondence between an object model and image features and to determine the pose of the object in the image relative to the camera. This research enhances the baseline algorithm by using elliptical image features to recognize circular objects in a model. Elliptical features show many desired properties in recognition. Utilizing these features not only increases the confidence level of detection and classification, but also provides the system with a good initial pose for a more robust performance.

Citation Download Citation

James R. Burrill, Sharon X. Wang, Art Barrow, Mike Friedman, and Matt Soffen "Model-based matching using elliptical features", Proc. SPIE 2756, Automatic Object Recognition VI, (24 May 1996); https://doi.org/10.1117/12.241136

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