Akademik Veri Yönetim Sistemi
Turkish Journal Of Engineering and Environmental Sciences, cilt.25, ss.405-413, 2001 (Scopus)
A large number of approaches for texture analysis have been suggested for the purpose of texture classification. Recently, wavelet frames were proposed for texture features extraction. In this study, nonsubsampled wavelet frame transform was used for feature extraction of 16 textures from a set of Brodatz’
album by means of various wavelet families. Texture classification was accomplished by artificial neural
network with a fast adaptive backpropagation algorithm. A new pyramidal-windowing algorithm is proposed, which forms randomly rotated texture windows of variable sizes texture windows for training a
neural networks classifier, and perfect classification results were obtained.