Real-time large-window binary filter design

Vishnu G. Kamat; Edward R. Dougherty

doi:10.1117/12.424960

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27 April 2001 Real-time large-window binary filter design

Vishnu G. Kamat, Edward R. Dougherty

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Proceedings Volume 4303, Real-Time Imaging V; (2001) https://doi.org/10.1117/12.424960
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

Abstract

Optimal translation-invariant binary windowed filters are determined by probabilities of the form, where x is a vector of observed values in the observation window and Y is the value in the image to be estimated by the filter. The optimal window filter is defined by y(x) equals 1 if P(Y equals 1/x) > 0.5 and y(x) equals 0 if P(Y equals 1/x) <EQ 0.5, which is the binary conditional expectation. The fundamental problem of filter design is to estimate P(Y equals 1/x) from data, where x ranges over all possible observation vectors in the window. A challenging aspect of optimal translation- invariant binary windowed filters is the implementation for large windows. In the context of Bayesian multiresolution filter design recently published by the authors, the training requirements for an accurate prior are more stringent. As such the practical feasibility of the filer design becomes an issue. This paper discusses the real time issues for large window filter design and how the bottlenecks were overcome to design practical large window multiresolution filters. The most crucial bottlenecks are the real memory required for training and the time required for training to obtain a satisfactory estimation of P(Y equals 1/x) or its prior for large windows. Among other improvements a method for data representation is developed that greatly reduces storage space for the large number of templates that occur for larger windows during the training of the filter. Parallel algorithms are designed that reduce hardware related time loss during training. In addition we take advantage of Bayesian filter methodology to train for large windows. While the algorithm works for larger windows, we demonstrate the feasibility of Bayesian multiresolution filter design for window sizes of up to 31 by 31.

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Vishnu G. Kamat and Edward R. Dougherty "Real-time large-window binary filter design", Proc. SPIE 4303, Real-Time Imaging V, (27 April 2001); https://doi.org/10.1117/12.424960

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