The automatic detection of tree-ring boundaries and other anatomical features
using image analysis has progressed substantially over the past decade with
advances in machine learning and imagery technology, as well as increasing
demands from the dendrochronology community. This paper presents a publicly
available database of 64 scanned images of transverse sections of commercially
grown Pinus taeda trees from northern Uruguay, ranging from 17 to 24 years old.
The collection contains several challenging features for automatic ring
detection, including illumination and surface preparation variation, fungal
infection (blue stains), knot formation, missing cortex or interruptions in
outer rings, and radial cracking. This dataset can be used to develop and test
automatic tree ring detection algorithms. This paper presents to the
dendrochronology community one such method, Cross-Section Tree-Ring Detection
(CS-TRD), which identifies and marks complete annual rings in cross-sections
for tree species presenting a clear definition between early and latewood. We
compare the CS-TRD performance against the ground truth manual delineation of
all rings over the UruDendro dataset. The CS-TRD software identified rings with
an average F-score of 89% and RMSE error of 5.27px for the entire database in
less than 20 seconds per image. Finally, we propose a robust measure of the
ring growth using the \emph{equivalent radius} of a circle having the same area
enclosed by the detected tree ring. Overall, this study contributes to the
dendrochronologist's toolbox of fast and low-cost methods to automatically
detect rings in conifer species, particularly for measuring diameter growth
rates and stem transverse area using entire cross-sections.

这篇论文介绍了使用图像分析自动检测树木年轮边界和其他解剖特征的方法，并提出了一种用于检测树木年轮的算法，该算法在测试数据集上表现良好，并提出了一种使用等效半径来衡量年轮生长的稳健方法。