Automatic identification of butterfly species based on local binary patterns and artificial neural network."Applied Soft Computing 28 (2015): 132-137.
| dc.contributor.author | Kaya, Yılmaz | |
| dc.contributor.author | Kaycı, Lokman | |
| dc.contributor.author | Uyar, Murat | |
| dc.date.accessioned | 2017-05-08T17:14:59Z | |
| dc.date.available | 2017-05-08T17:14:59Z | |
| dc.date.issued | 2015 | |
| dc.department | Belirlenecek | en_US |
| dc.description.abstract | Butterflies are classified firstly according to their outer morphological qualities. It is required to analyze genital characters of them when classification according to outer morphological qualities is not possible. Genital characteristics of a butterfly can be determined by using various chemical substances and methods. Currently, these processes are carried out manually by preparing genital slides of the collected butterfly through some certain processes. For some groups of butterflies molecular techniques should be applied for identification which is expensive to use. In this study, a computer vision method is proposed for automatically identifying butterfly species as an alternative to conventional identification methods. The method is based on local binary pattern (LBP) and artificial neural network (ANN). A total of 50 butterfly images of five species were used for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has achieved well recognition in terms of accuracy rates for butterfly species identification. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12604/544 | |
| dc.language.iso | en | en_US |
| dc.relation.publicationcategory | Uluslararası Hakemli Dergi Makalesi | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.snmz | #KayıtKontrol# | |
| dc.subject | Butterfly identification; Local binary patterns; Texture features; Artificial neural network | en_US |
| dc.title | Automatic identification of butterfly species based on local binary patterns and artificial neural network."Applied Soft Computing 28 (2015): 132-137. | en_US |
| dc.type | Article | en_US |
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