Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/1202
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Yu, L | - |
dc.creator | Wang, K | - |
dc.creator | Zhang, DD | - |
dc.date.accessioned | 2014-12-11T08:27:18Z | - |
dc.date.available | 2014-12-11T08:27:18Z | - |
dc.identifier.isbn | 0-7803-9134-9 | - |
dc.identifier.uri | http://hdl.handle.net/10397/1202 | - |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.rights | © 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | en_US |
dc.rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | en_US |
dc.subject | Box counting | en_US |
dc.subject | Fractal dimension | en_US |
dc.subject | Iris image | en_US |
dc.subject | Coarse classification | en_US |
dc.title | Coarse iris classification based on box-counting method | en_US |
dc.type | Conference Paper | en_US |
dc.description.otherinformation | Author name used in this publication: David Zhang | en_US |
dc.description.otherinformation | Refereed conference paper | en_US |
dcterms.abstract | This paper proposes a novel algorithm for the automatic coarse classification of iris images using a box-counting method to estimate the fractal dimensions of the iris. First, the iris image is segmented into sixteen blocks, eight belonging to an upper group and eight to a lower group. We then calculate the fractal dimension value of these image blocks and take the mean value of the fractal dimension as the upper and the lower group fractal dimensions. Finally all the iris images are classified into four categories in accordance with the upper and the lower group fractal dimensions. This classification method has been tested and evaluated on 872 iris cases, and the proportions of these categories in our database are 5.50%, 38.54%, 21.79% and 34.17%. The iris images are classified with the double threshold algorithm, which classifies iris images with an accuracy of 94.61%. When we allow for the border effect, the double threshold algorithm is 98.28% accurate. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | 2005 ICIP : 2005 International Conference on Image Processing (ICIP) : September 11-14, 2005, Genova, Italy, v. 3, p. 301-304 | - |
dcterms.issued | 2005 | - |
dc.identifier.scopus | 2-s2.0-33749246574 | - |
dc.relation.ispartofbook | 2005 ICIP : 2005 International Conference on Image Processing (ICIP) : September 11-14, 2005, Genova, Italy | - |
dc.relation.conference | IEEE International Conference on Image Processing [ICIP] | - |
dc.identifier.rosgroupid | r26238 | - |
dc.description.ros | 2005-2006 > Academic research: refereed > Refereed conference paper | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
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box-counting-method_05.pdf | 2.41 MB | Adobe PDF | View/Open |
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