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Feature extraction for animal fiber identification

Kong, L. X., She, F. H., Nahavandi, S. and Kouzani, A. Z. 2002, Feature extraction for animal fiber identification, in SPIE 2002 : Proceedings of the 2nd International Conference on Image and Graphics, SPIE, International Society for Optical Engineering, Bellingham, Wash., pp. 699-704.

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Title Feature extraction for animal fiber identification
Author(s) Kong, L. X.ORCID iD for Kong, L. X. orcid.org/0000-0001-6219-3897
She, F. H.ORCID iD for She, F. H. orcid.org/0000-0001-8191-0820
Nahavandi, S.ORCID iD for Nahavandi, S. orcid.org/0000-0002-0360-5270
Kouzani, A. Z.ORCID iD for Kouzani, A. Z. orcid.org/0000-0002-6292-1214
Conference name International Conference on Image and Graphics (2nd : 2002 : Hefei, China)
Conference location Hefei, China
Conference dates 16-18 Aug. 2002
Title of proceedings SPIE 2002 : Proceedings of the 2nd International Conference on Image and Graphics
Editor(s) Sui, Wei
Publication date 2002
Conference series International Conference on Image and Graphics
Start page 699
End page 704
Publisher SPIE, International Society for Optical Engineering
Place of publication Bellingham, Wash.
Keyword(s) Feature extraction
animal fiber
image processing
hybrid system
principal component analysis
Summary Fiber identification has been a very important task in many industries such as wool growing, textile processing, archaeology, histochernical engineering, and zoology. Over the years, animal fibers have been identified using physical and chemical approaches. Recently, objective identification of animal fibers has been developed based on the cuticular information of fibers. Effective and accurate extraction of representative features is essential to animal fiber identification and classification. In the current work, two different strategies are developed for this purpose. In the first method, explicit features are extracted using image processing. However, only implicit features are used in the second method with an unsupervised artificial neural network. It is found that the use of explicit features increases the accuracy of fiber identification but requires more effort on processing images and solid knowledge of what features are representative ones.
ISBN 0819446564
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2002, SPIE, International Society for Optical Engineering
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30026038

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.