Application of artificial neural network for automatic detection of butterfly species usingcolor and texture features. Visual Computer, (2014), 30: 71-79

dc.contributor.authorKaya, Yılmaz
dc.contributor.authorKaycı, Lokman
dc.date.accessioned2017-05-08T17:05:10Z
dc.date.available2017-05-08T17:05:10Z
dc.date.issued2014
dc.departmentBelirleneceken_US
dc.description.abstractButterflies can be classified by their outer morphological qualities, genital characteristics that can be obtained using various chemical substances and methods which are carried out manually by preparing genital slides through some certain processes or molecular techniques which is a very expensive method. In this study, a new method which is based on artificial neural networks (ANN) and an image processing technique was used for identification of butterfly species as an alternative to conventional diagnostic methods. Five texture and three color features obtained from 140 butterfly images were used for identification of species. Texture features were obtained by using the average of gray level co-occurrence matrix (GLCM) with different angles and distances. The accuracy of the purposed butterfly classification method has reached 92.85 %. These findings suggested that the texture and color features can be useful for identification of butterfly species.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12604/538
dc.language.isoenen_US
dc.relation.publicationcategoryUluslararası Hakemli Dergi Makalesien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz#KayıtKontrol#
dc.subjectButterfly identification Expert system GLCM LBP Artificial neural network Texture analysisen_US
dc.titleApplication of artificial neural network for automatic detection of butterfly species usingcolor and texture features. Visual Computer, (2014), 30: 71-79en_US
dc.typeArticleen_US

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.71 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama: