ATR paradigm comparison with emphasis on model-based vision

Edmund G. Zelnio

doi:10.1117/12.57111

1 February 1992 ATR paradigm comparison with emphasis on model-based vision

Edmund G. Zelnio

Proceedings Volume 1609, Model-Based Vision Development and Tools; (1992) https://doi.org/10.1117/12.57111
Event: Robotics '91, 1991, Boston, MA, United States

Abstract

All automatic target recognizers (ATRs) either explicitly or implicitly use models and compute features, many use feedback, and all rely on some form of data for development and test. This paper compares three popular paradigms used for ATR--the prescreen, segment, classify (PSC) paradigm (commonly used when applying statistical pattern recognition techniques to image sensors), the matched filter (MF) paradigm, and the model-based vision (MBV) paradigm. This comparison is performed initially by considering how each contends with the ATR model space, which is introduced to aid the paradigm comparison. These paradigms are then compared in detail by examining how each uses models, features, feedback, and data to perform target recognition. Based on these discussions, three ATR sensors are examined in terms of the ATR model space to analyze how each sensor's imaging properties either help or hinder the solution of the ATR problem. The ATR model space concept is then used to motivate model-based vision solutions to the multi-sensor fusion problem and to suggest novel sensor combinations that could be used synergistically to attack the ATR problem.

Citation Download Citation

Edmund G. Zelnio "ATR paradigm comparison with emphasis on model-based vision", Proc. SPIE 1609, Model-Based Vision Development and Tools, (1 February 1992); https://doi.org/10.1117/12.57111

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