Abstract/Details

Modeling natural microimage statistics


2000 2000

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Abstract (summary)

A large collection of digital images of natural scenes provides a database for analyzing and modeling small scene patches (e.g., 2 x 2) referred to as natural microimages. A pivotal finding is the stability of the empirical microimage distribution across scene samples and with respect to scaling. With a view toward potential applications (e.g. classification, clutter modeling, segmentation), we present a hierarchy of microimage probability models which capture essential local image statistics. Tools from information theory, algebraic geometry and of course statistical hypothesis testing are employed to assess the “match” between candidate models and the empirical distribution. Geometric symmetries play a key role in the model selection process.

One central result is that the microimage distribution exhibits reflection and rotation symmetry and is well-represented by a Gibbs law with only pairwise interactions. However, the acceptance of the up-down reflection symmetry hypothesis is borderline and intensity inversion symmetry is rejected. Finally, possible extensions to larger patches via entropy maximization and to patch classification via vector quantization are briefly discussed.

Indexing (details)


Subject
Statistics;
Mathematics;
Computer science
Classification
0463: Statistics
0405: Mathematics
0984: Computer science
Identifier / keyword
Applied sciences, Pure sciences, Image analysis, Microimage statistics
Title
Modeling natural microimage statistics
Author
Koloydenko, Alexey Alexandrovich
Number of pages
180
Publication year
2000
Degree date
2000
School code
0118
Source
DAI-B 61/09, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780599957473, 0599957476
Advisor
Geman, Donald
University/institution
University of Massachusetts Amherst
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
9988810
ProQuest document ID
304606925
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304606925
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