Modeling natural microimage statistics. / Koloydenko, Alexey.

UMI - Dissertations Publishing, 2000. 180 p.

Research output: ThesisDoctoral Thesis

Published

Documents

  • Thesis

    Rights statement: Copyright UMI - Dissertations Publishing 2000 (reproduced from microfilm with permission from owner) Copyright by Alexey Alexandrovich Koloydenko 2000

    Accepted author manuscript, 1.2 MB, PDF document

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Abstract

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 ¯nding is the stability of the empirical microimage distribution across scene samples and with respect to scaling. With a view toward potential applications (e.g. classi¯cation, 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.
Original languageEnglish
QualificationPh.D.
Awarding Institution
  • University of Massachusetts Amherst
Supervisors/Advisors
  • Geman, Donald, Supervisor, External person
Award date1 Sep 2000
Publisher
  • UMI - Dissertations Publishing
Print ISBNs9780599957473, 0599957476
Publication statusPublished - 2000

Research outputs

This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 4589355