Bayesian multiresolution filter design

Vishnu G. Kamat; Edward R. Dougherty; Junior Barrera

doi:10.1117/12.379399

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3 March 2000 Bayesian multiresolution filter design

Vishnu G. Kamat, Edward R. Dougherty, Junior Barrera

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Proceedings Volume 3961, Nonlinear Image Processing XI; (2000) https://doi.org/10.1117/12.379399
Event: Electronic Imaging, 2000, San Jose, CA, United States

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.

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Vishnu G. Kamat, Edward R. Dougherty, and Junior Barrera "Bayesian multiresolution filter design", Proc. SPIE 3961, Nonlinear Image Processing XI, (3 March 2000); https://doi.org/10.1117/12.379399

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