We propose a novel approach to reconstructing curvilinear tree structures evolving over time, such as road networks in 2D aerial images or neural structures in 3D microscopy stacks acquired in vivo. To enforce temporal consistency, we simultaneously process all images in a sequence, as opposed to reconstructing structures of interest in each image independently. We formulate the problem as a Quadratic Mixed Integer Program and demonstrate the additional robustness that comes from using all available visual clues at once, instead of working frame by frame. Furthermore, when the linear structures undergo local changes over time, our approach automatically detects them.

中文翻译：

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通过增强时间一致性重建时间间隔序列中的进化树结构

我们提出了一种重建随时间变化的曲线树结构的新颖方法，例如获得的2D航拍图像中的道路网络或获得的3D显微镜堆栈中的神经结构。 体内。为了增强时间一致性，我们要同时处理序列中的所有图像，这与在每个图像中独立地构建感兴趣的结构相反。我们将问题表述为二次混合整数程序，并演示了一次性使用所有可用视觉线索而不是逐帧工作所带来的额外健壮性。此外，当线性结构随时间发生局部变化时，我们的方法会自动检测到它们。