We address the problem of detecting tree rings in microscopy images of shrub
cross sections. This can be regarded as a special case of the instance
segmentation task with several unique challenges such as the concentric
circular ring shape of the objects and high precision requirements that result
in inadequate performance of existing methods. We propose a new iterative
method which we term Iterative Next Boundary Detection (INBD). It intuitively
models the natural growth direction, starting from the center of the shrub
cross section and detecting the next ring boundary in each iteration step. In
our experiments, INBD shows superior performance to generic instance
segmentation methods and is the only one with a built-in notion of
chronological order. Our dataset and source code are available at
this http URL

本文提出了一种新的迭代法，即迭代下一边界检测算法（INBD），它通过模拟自然生长方向，从植物截面的中心开始，在每个迭代步骤中检测下一个环形边界，解决了检测显微镜图像中的树轮等多个环状物体的问题，并且优于常规实例分割方法，是唯一具有内置时间顺序概念的方法。