Pyramidal Markov random field (MRF) models for optical flow estimation applied to target detection

Roger A. Samy; Daniel Duclos

doi:10.1117/12.179033

22 June 1994 Pyramidal Markov random field (MRF) models for optical flow estimation applied to target detection

Roger A. Samy, Daniel Duclos

Proceedings Volume 2233, Sensor Fusion and Aerospace Applications II; (1994) https://doi.org/10.1117/12.179033
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

In air-to-ground applications, the detection of target is a difficult problem due to complex background where classical detection algorithms generate a large amount of false alarms. This paper addresses the detection of moving target based on motion compensated sequences. In the presence of noisy image acquisition and motion discontinuities, the estimation of optical flow is reformulated in robust estimation framework. The motion estimation is based on robust optical flow algorithm developed in the pyramidal Markov Random Model framework. We present the results of this detection algorithm on real-world airborne I.R. image sequence.

Citation Download Citation

Roger A. Samy and Daniel Duclos "Pyramidal Markov random field (MRF) models for optical flow estimation applied to target detection", Proc. SPIE 2233, Sensor Fusion and Aerospace Applications II, (22 June 1994); https://doi.org/10.1117/12.179033

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