Fuzzy C mean clustering in off-line handwriting signature verfication system
This research is the first research that suggests the usage of the stable region of a signature for verification purpose. This research highlighted the design and development of the proposed Fuzzy C Mean clustering to determine the stable segments of a signature generated from windowing segmentation...
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2006
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| 在線閱讀: | http://ir.unimas.my/id/eprint/1705/8/2013-02-thLeeBYfull.pdf |
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my-unimas-ir.17052023-03-07T08:20:04Z Fuzzy C mean clustering in off-line handwriting signature verfication system 2006 Lee, Beng Yong QA76 Computer software This research is the first research that suggests the usage of the stable region of a signature for verification purpose. This research highlighted the design and development of the proposed Fuzzy C Mean clustering to determine the stable segments of a signature generated from windowing segmentation process where features could be extracted from signature images. Six features are extracted from each stable segment i.e. Image Size, Ratio of height to width, slant, Maximum horizontal projection, Vertical centre of mass and Horizontal Relative gravity centre/ Horizontal centre of mass. Besides that, this research also demonstrated how to select a best features to represent each determined stable segment. Faculty of Computer Science and Information Technology 2006 Thesis http://ir.unimas.my/id/eprint/1705/ http://ir.unimas.my/id/eprint/1705/8/2013-02-thLeeBYfull.pdf text en validuser masters Universiti Malaysia Sarawak (UNIMAS) Faculty of Computer Science and Information Technology |
| institution |
Universiti Malaysia Sarawak |
| collection |
UNIMAS Institutional Repository |
| language |
English |
| topic |
QA76 Computer software |
| spellingShingle |
QA76 Computer software Lee, Beng Yong Fuzzy C mean clustering in off-line handwriting signature verfication system |
| description |
This research is the first research that suggests the usage of the stable region of a signature for verification purpose. This research highlighted the design and development of the proposed Fuzzy C Mean clustering to determine the stable segments of a signature generated from windowing segmentation process where features could be extracted from signature images. Six features are extracted from each stable segment i.e. Image Size, Ratio of height to width, slant, Maximum horizontal projection, Vertical centre of mass and Horizontal Relative gravity centre/ Horizontal centre of mass. Besides that, this research also demonstrated how to select a best features to represent each determined stable segment. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Lee, Beng Yong |
| author_facet |
Lee, Beng Yong |
| author_sort |
Lee, Beng Yong |
| title |
Fuzzy C mean clustering in off-line handwriting signature verfication system |
| title_short |
Fuzzy C mean clustering in off-line handwriting signature verfication system |
| title_full |
Fuzzy C mean clustering in off-line handwriting signature verfication system |
| title_fullStr |
Fuzzy C mean clustering in off-line handwriting signature verfication system |
| title_full_unstemmed |
Fuzzy C mean clustering in off-line handwriting signature verfication system |
| title_sort |
fuzzy c mean clustering in off-line handwriting signature verfication system |
| granting_institution |
Universiti Malaysia Sarawak (UNIMAS) |
| granting_department |
Faculty of Computer Science and Information Technology |
| publishDate |
2006 |
| url |
http://ir.unimas.my/id/eprint/1705/8/2013-02-thLeeBYfull.pdf |
| _version_ |
1783727900361490432 |