Image Segmentation and Robust Estimation Using Parzen Windows
Singh, Maneesh Kumar
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/80849
Description
Title
Image Segmentation and Robust Estimation Using Parzen Windows
Author(s)
Singh, Maneesh Kumar
Issue Date
2003
Doctoral Committee Chair(s)
Ahuja, Narendra
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
Abstract
This thesis explores the use of Parzen windows for modeling image data. The validity of such a model is shown to follow naturally from the elementary Gestalt laws of vicinity, similarity, and continuity of direction. Consistency results are derived for Parzen window estimators, both for continuous-time and discrete-time images. The problem of scale is addressed; A novel plug-in estimator is proposed for the bandwidth (scale) of the window kernels. Asymptotic optimality of the proposed bandwidth is proved. The bandwidth selection scheme is validated for segmentation of real images. The density estimation framework is extended to model more structured images, e.g., those containing structures representable using local or global linear parametric models. Algorithms for robust parameter estimation and segmentation are given. Convergence results are derived for these algorithms. The robust parameter estimation framework is then extended to the problem of registering images of an object undergoing 2-D motion, overall image alignment (camera motion), and partial image alignment (2-D object tracking). For this purpose, novel estimation measures have been proposed. Algorithms have been proposed for the above tasks, and convergence of these algorithms have been proved. All proposed algorithms have been validated on real data.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
