Segmentation of Rays in Wood Microscopy Images Using the U-Net Model
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
North Carolina State Univ Dept Wood & Paper Sci
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Rays are an important anatomical feature in tree species identification. They are found in certain proportions in trees, which vary for each tree. In this study, the U-Net model is adopted for the first time to detect wood rays. A dataset is created with images taken from the wood database. The resolution of microscopic wood images in tangential section is 640x400. The input image for training is divided into 32x32 image blocks. Each pixel in the dataset is labeled as belonging to the ray or the background. Then, the dataset is increased by applying scale, rotation, salt-and-pepper noise, circular mean filter, and gauss filter. The U-Net network created for ray segmentation is trained using the Adam optimization algorithm. The experimental results show that the ray segmentation accuracy in testing is 96.3%.
Açıklama
Anahtar Kelimeler
Image Segmentation, Wood Rays, U-Net
Kaynak
Bioresources
WoS Q Değeri
Q2
Scopus Q Değeri
Q3
Cilt
16
Sayı
1
