Fusion of multiple fixations with a space-variant sensor: conditional optimality of maximum-resolution blending

Alan S. Rojer; Eric L. Schwartz

doi:10.1117/12.25156

1 February 1991 Fusion of multiple fixations with a space-variant sensor: conditional optimality of maximum-resolution blending

Alan S. Rojer, Eric L. Schwartz

Author Affiliations +

Proceedings Volume 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques; (1991) https://doi.org/10.1117/12.25156
Event: Advances in Intelligent Robotics Systems, 1990, Boston, MA, United States

Abstract

In previous work by ourselves and others it has been argued that the best algorithm for fusing multiple fixations of the same scene is to simply choose the maximum resolution information available at each position in the reconstructed scene. We can show under simple assumptions of pixel distributions that this algorithm is indeed optimal in a least-squared-error sense. In the presence of noise however optimality no longer holds a certain degree of averaging of lower resolution information is required to obtain optimal reconstruction. We also present empirical demonstrations of fused images using maximum-resolution and averaged blending techniqes and we illustrate the effects of noise on the quality of reconstruction.

Citation Download Citation

Alan S. Rojer and Eric L. Schwartz "Fusion of multiple fixations with a space-variant sensor: conditional optimality of maximum-resolution blending", Proc. SPIE 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques, (1 February 1991); https://doi.org/10.1117/12.25156

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