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Proceedings Paper

Training Markov random fields by sampling: how much data is required?
Author(s): Davin Milun; David B. Sher
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Paper 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.

Paper Details

Date Published: 1 September 1993
PDF: 7 pages
Proc. SPIE 1962, Adaptive and Learning Systems II, (1 September 1993); doi: 10.1117/12.150582
Show Author Affiliations
Davin Milun, SUNY/Buffalo (United States)
David B. Sher, SUNY/Buffalo (United States)


Published in SPIE Proceedings Vol. 1962:
Adaptive and Learning Systems II
Firooz A. Sadjadi, Editor(s)

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