论文标题
迭代下一个边界检测，例如灌木横截面显微镜图像中树环的分割
Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections
论文作者
论文摘要
我们解决了在灌木横截面的显微镜图像中检测树环的问题。这可以被视为实例分割任务的特殊情况，具有几个独特的挑战，例如对象的同心圆环形状以及高精度要求，导致现有方法的性能不足。我们提出了一种新的迭代方法，我们将其称为下一个边界检测（INBD）。从灌木横截面的中心开始，它直观地建模自然生长方向，并在每个迭代步骤中检测下一个环边界。在我们的实验中，INBD显示出与通用实例分割方法的卓越性能，并且是唯一具有内置时间顺序概念的方法。我们的数据集和源代码可在http://github.com/alexander-g/inbd上找到。
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 http://github.com/alexander-g/INBD.
