Dendrochronology with Deep Learning

• Published in: 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) pp. 333 - 336
• Main Authors: K, Divya, Kaur, Sukhvir
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.04.2021
• Subjects: canny edges; Deep learning; denoising neural network; Image edge detection; Image resolution; neural network; Neural networks; Noise reduction; Performance evaluation; tree ring; Vegetation
• DOI: 10.1109/ICIEM51511.2021.9445305

Analysis of the tree rings by hand is a difficult task and agitated for dendrochronology domain area. Detection of the tree rings are quite popular in numerous fields of science. As, the detected results enables the users to determine the age of tree, tree with good ring and environment changes. The evaluation of the tree rings requires previous detection of the tree ring boundaries that is usually performed physically with devices like stereoscope, moving table, along with data recorder. To ease the manual work of users, this paper presents detection of actual ring as good for denoised images using denoising neural network (dncnn). In existing paper, author worked for 3 images with median filter. In comparison to the existing work, this paper provides 100 images which were denoised first and then detected rings.