Researchers have been involved for decades in search of an efficient skin detection method. Yet current methods have not overcome the major limitations. To overcome these limitations, in this dissertation, a clustering and region growing based skin detection method is proposed. These methods together with a significant insight result in a more effective algorithm. The insight concerns a capability to define dynamically the number of clusters in a collection of pixels organized as an image. In clustering for most problem domains, the number of clusters is fixed a priori and does not perform effectively over a wide variety of data contents. Therefore, in this dissertation, a skin detection method has been proposed using the above findings and validated. This method assigns the number of clusters based on image properties and ultimately allows freedom from manual thresholding or other manual operations. The dynamic determination of clustering outcomes allows for greater automation of skin detection when dealing with uncertain real-world conditions.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1538658 |
| Date | 08 1900 |
| Creators | Islam, A B M Rezbaul |
| Contributors | Buckles, Bill, Mikler, Armin R., Akl, Robert, Namuduri, Kamesh |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | ix, 107 pages, Text |
| Rights | Public, Islam, A B M Rezbaul, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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