Use of the generalized maximum-likelihood algorithm for estimation of Markovian-modeled image motion

Nader M. Namazi; David W. Foxall

doi:10.1117/12.23534

1 November 1990 Use of the generalized maximum-likelihood algorithm for estimation of Markovian-modeled image motion

Nader M. Namazi, David W. Foxall

Proceedings Volume 1349, Applications of Digital Image Processing XIII; (1990) https://doi.org/10.1117/12.23534
Event: 34th Annual International Technical Symposium on Optical and Optoelectronic Applied Science and Engineering, 1990, San Diego, CA, United States

Abstract

A new iterative technique for frame-to-frame image motion estimation is introduced and impleniented. The algorithm presented in this paper is based on the maximum likelihood criterion and is referred to as the GML algorithm. This scheme requires the covariance function matrix of the motion a priori. For this reason a possible motion model will be introduced and implemented. Simulation experiments are presented which investigate the performance of the algorithm in conjunction with real and synthetic images. Key Words : Motion Compensation Maximum Likelihood Covariance Function Markovian Field.

Citation Download Citation

Nader M. Namazi and David W. Foxall "Use of the generalized maximum-likelihood algorithm for estimation of Markovian-modeled image motion", Proc. SPIE 1349, Applications of Digital Image Processing XIII, (1 November 1990); https://doi.org/10.1117/12.23534

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