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A complex texture classification algorithm based on Gabor-type filtering cellular neural networks and self-organized fuzzy inference neural networks

dc.citation.epage3945en_US
dc.citation.spage3942en_US
dc.contributor.authorLin, CTen_US
dc.contributor.authorHuang, CHen_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.date.accessioned2014-12-08T15:25:24Z
dc.date.available2014-12-08T15:25:24Z
dc.date.issued2005en_US
dc.description.abstractIn this paper, a bio-inspired complex texture classification algorithm has been introduced. This algorithm included two neural networks; One is Gabor-type Filtering Cellular Neural Networks which used to simulate human retina, the other is Self-Organized Fuzzy Inference Neural Networks which used to simulate the brain. Both the neural networks can be considered as feed-forward neural networks. Thus, we can say that the whole system which has been introduced in this paper is also a feed-forward system and which contains the ability of parallel processing.en_US
dc.identifier.isbn0-7803-8834-8en_US
dc.identifier.issn0271-4302en_US
dc.identifier.journal2005 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), VOLS 1-6, CONFERENCE PROCEEDINGSen_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/17784
dc.identifier.wosnumberWOS:000232002403231
dc.language.isoen_USen_US
dc.titleA complex texture classification algorithm based on Gabor-type filtering cellular neural networks and self-organized fuzzy inference neural networksen_US
dc.typeProceedings Paperen_US

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