深度学习的树木年代学

D. K, Sukhvir Kaur
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引用次数: 0

摘要

树木年轮的手工分析是树木年代学领域的一项艰巨而又激动人心的任务。树木年轮的探测在许多科学领域都很流行。根据检测结果,用户可以确定树木的年龄、年轮和环境的变化情况。树木年轮的评估需要事先对树木年轮边界进行检测,这通常是通过立体镜、移动台和数据记录仪等物理设备进行的。为了减轻用户的手工操作,本文提出了使用去噪神经网络(dncnn)对去噪后的图像进行实际环检测的方法。在现有的论文中,作者对3幅图像进行了中值滤波。与已有工作相比,本文提供了100幅图像,先去噪后检测圆环。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dendrochronology with Deep Learning
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.
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