Document Type : Research Paper
Computer Engineer, Department of Computer Engineering, Hamedan Branch, Islamic Azad University
Computer Engineering Department, Hamedan Branch, Islamic Azad University
In the field of diagnosis and classification of animals there are always many problems which prevents the development of rapid and effective progress in this area. In recent years, the new approaches have been proposed that are based on artificial neural networks and image processing that can detect and recognize the butterfly types. In this article, specifically, we'll scrutiny the butterfly species detecting by using image processing and smart classification methods, also we are looking for the performance improvement by employing texture of butterfly wings features. In this context, the quantization feature extraction method of the local phase is used that resist against the blur in the butterfly pictures. As well as, in order to classification, the MLP and wavelet neural network is used that the result demonstrates, the wavelet neural network achieve 100% classification accuracy in 14 butterfly species.