A Probabilistic Analysis of Natural Images
Date Issued
2012
Date
2012
Author(s)
Yao-Hsiang, Yang
Abstract
Natural image statistics has long been interested by the researchers of the computer vision community during the last twenty years. There has been reports on many distinct characteristics of image data collecting under various conditions. Several applications based on the assumptions derived from natural image statistics have been proved to be both useful and intelligible in compare to those works which do not employ any explicit assumptions about images.
In this thesis, I wish to summarize those experimental results and explanations in previous studies. Then I wish to investigate the role of these facts in the research. After explicating the theoretical framework, two novel applications incorporating the Fisher''s discriminant analysis and Wiener filtering are conduct in order to demostrate the power of this approach. I wish these rudimentary results are shown to be promising enough to make people more confident about the possible future toward a more complete formal theory of image analysis.
Subjects
natural image statistics
dimension reduction
illumination-invariant feature extraction
Bayesian analysis
Wiener filtering
Type
thesis
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