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CONVERGENCE OF THE MEAN SHIFT ALGORITHM AND ITS GENERALIZATIONS
| Title: | CONVERGENCE OF THE MEAN SHIFT ALGORITHM AND ITS GENERALIZATIONS. |
 31 views 17 downloads |
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| Name(s): |
Hu, Ting, Author Li, Xin, Committee Chair University of Central Florida, Degree Grantor |
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| Type of Resource: | text | |
| Date Issued: | 2011 | |
| Publisher: | University of Central Florida | |
| Language(s): | English | |
| Abstract/Description: | 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. | |
| Identifier: | CFE0004059 (IID), ucf:49133 (fedora) | |
| Note(s): |
2011-08-01 M.S. Sciences, Department of Mathematics Masters This record was generated from author submitted information. |
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| Subject(s): |
estimation convergence mean shift algorithm kernel density |
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| Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0004059 | |
| Restrictions on Access: | public | |
| Host Institution: | UCF |
