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Proceedings Paper

Multiresolution statistical methods in image analysis
Author(s): Mark R. Luettgen; William Clement Karl; Alan S. Willsky; Robert R. Tenney
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Paper Abstract

In this paper, we discuss a statistical framework for multiscale signal and image processing based on a class of multiresolution stochastic models, which can be used to represent spatial random processes at a range of scales. The model class is quite rich, and in fact includes the class of Markov random fields. In addition, the models have a scale recursive structure which naturally leads to efficient, scale recursive algorithms for smoothing and likelihood calculation. We discuss an application of the framework to the problem of computing optical flow in image sequence, and demonstrate computational savings on the order of one to two orders of magnitude over standard algorithms.

Paper Details

Date Published: 1 November 1992
PDF: 12 pages
Proc. SPIE 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods, (1 November 1992); doi: 10.1117/12.131582
Show Author Affiliations
Mark R. Luettgen, Massachusetts Institute of Technology (United States)
William Clement Karl, Massachusetts Institute of Technology (United States)
Alan S. Willsky, Massachusetts Institute of Technology (United States)
Robert R. Tenney, Alphatech, Inc. (United States)


Published in SPIE Proceedings Vol. 1826:
Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods
David P. Casasent, Editor(s)

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