Training Markov random fields by sampling: how much data is required?

Davin Milun; David B. Sher

doi:10.1117/12.150582

1 September 1993 Training Markov random fields by sampling: how much data is required?

Davin Milun, David B. Sher

Proceedings Volume 1962, Adaptive and Learning Systems II; (1993) https://doi.org/10.1117/12.150582
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

We have been developing edge relaxation and binary image enhancement systems using parameters derived from an ensemble of training images. We tabulate the frequencies of local structures in the training ensemble and reconstruct noisy/corrupted images so that they best match the local characteristics of the set of training images. This paper investigates how many such training images are required to generate a useful and consistent set of local neighborhood probabilities.

Citation Download Citation

Davin Milun and David B. Sher "Training Markov random fields by sampling: how much data is required?", Proc. SPIE 1962, Adaptive and Learning Systems II, (1 September 1993); https://doi.org/10.1117/12.150582

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