Keywords

Algorithms, Convergence, Estimation theory

Abstract

Mean shift is an effective iterative algorithm widely used in image analysis tasks like tracking, image segmentation, smoothing, filtering, edge detection and etc. It iteratively estimates the modes of the probability function of a set of sample data points based in a region. Mean shift was invented in 1975, but it was not widely used until the work by Cheng in 1995. After that, it becomes popular in computer vision. However the convergence, a key character of any iterative algorithm, has been rigorously proved only very recently, but with strong assumptions. In this thesis, the method of mean shift is introduced systematically first and then the convergence is established under more relaxed assumptions. Finally, generalization of the mean shift method is also given for the estimation of probability density function using generalized multivariate smoothing functions to meet the need for more real life applications.

Notes

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Graduation Date

2011

Semester

Summer

Advisor

Li, Xin

Degree

Master of Science (M.S.)

College

College of Sciences

Department

Mathematics

Format

application/pdf

Identifier

CFE0004059

URL

http://purl.fcla.edu/fcla/etd/CFE0004059

Language

English

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Subjects

Dissertations, Academic -- Sciences, Sciences -- Dissertations, Academic

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Included in

Mathematics Commons

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