Bayesian multiresolution filter design

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

doi:10.1117/12.379399

3 March 2000 Bayesian multiresolution filter design

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

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.

Citation Download Citation

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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