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Proceedings Paper

Bayesian multiresolution filter design
Author(s): Vishnu G. Kamat; Edward R. Dougherty; Junior Barrera
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Paper Abstract

This paper discusses a multiresolution approach to Bayesian design of binary filters. The key problem with Bayesian design is that for any window one needs enough observations of a template across the states of nature to estimate its prior distribution, thus introducing severe constraints on single window Bayesian filter designs. By using a multiresolution approach and optimized training methods, we take advantage of prior probability information in designing large-window multiresolution filters. The key point is that we define each filter value at the largest resolution for which we have sufficient prior knowledge to form a prior distribution for the relevant conditional probability, and move to a sub-window when a non-uniform prior is not available. This is repeated until we are able to make a filtering decision at some window size with a known prior for the probability P(Y equals 1x), which is guaranteed for smaller windows. We consider edge noise for our experiments with emphasis on realistically degraded document images.

Paper Details

Date Published: 3 March 2000
PDF: 12 pages
Proc. SPIE 3961, Nonlinear Image Processing XI, (3 March 2000); doi: 10.1117/12.379399
Show Author Affiliations
Vishnu G. Kamat, Texas A&M Univ. (United States)
Edward R. Dougherty, Texas A&M Univ. (United States)
Junior Barrera, Univ. de Sao Paulo (Brazil)


Published in SPIE Proceedings Vol. 3961:
Nonlinear Image Processing XI
Edward R. Dougherty; Jaakko T. Astola, Editor(s)

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