Fuzzy logic based selera recognition

Das, A; Pal, U; Ballester, MAF; Blumenstein, M

Fuzzy logic based selera recognition

Das, A Pal, U Ballester, MAF Blumenstein, M

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2014, pp. 561 - 568
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Das, A	en_US
dc.contributor.author	Pal, U	en_US
dc.contributor.author	Ballester, MAF	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2014, pp. 561 - 568	en_US
dc.identifier.isbn	9781479920723	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121099
dc.description.abstract	© 2014 IEEE. In this paper a selera recognition and validation system is proposed. Here selera segmentation was performed by Fuzzy logic-based clustering. Since the selera vessels are not prominent, image enhancement was required. A Fuzzy logic-based Brightness Preserving Dynamic Fuzzy Histogram Equalization and discrete Meyer wavelet was used to enhance the vessel patterns. For feature extraction, the Dense Local Binary Pattern (D-LBP) was used. D-LBP patch descriptors of each training image are used to form a bag of features, which is used to produce the training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset is used here for experimentation. An encouraging Equal Error Rate (EER) of 4.31% was achieved in our experiments.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2014.6891684	en_US
dc.title	Fuzzy logic based selera recognition	en_US
dc.type	Conference Proceeding
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. In this paper a selera recognition and validation system is proposed. Here selera segmentation was performed by Fuzzy logic-based clustering. Since the selera vessels are not prominent, image enhancement was required. A Fuzzy logic-based Brightness Preserving Dynamic Fuzzy Histogram Equalization and discrete Meyer wavelet was used to enhance the vessel patterns. For feature extraction, the Dense Local Binary Pattern (D-LBP) was used. D-LBP patch descriptors of each training image are used to form a bag of features, which is used to produce the training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset is used here for experimentation. An encouraging Equal Error Rate (EER) of 4.31% was achieved in our experiments.

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http://hdl.handle.net/10453/121099