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Reconstructing Curvilinear Networks Using Path Classifiers and Integer Programming

机译:使用路径分类器和整数编程重建曲线网络

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We propose a novel approach to automated delineation of curvilinear structures that form complex and potentially loopy networks. By representing the image data as a graph of potential paths, we first show how to weight these paths using discriminatively-trained classifiers that are both robust and generic enough to be applied to very different imaging modalities. We then present an Integer Programming approach to finding the optimal subset of paths, subject to structural and topological constraints that eliminate implausible solutions. Unlike earlier approaches that assume a tree topology for the networks, ours explicitly models the fact that the networks may contain loops, and can reconstruct both cyclic and acyclic ones. We demonstrate the effectiveness of our approach on a variety of challenging datasets including aerial images of road networks and micrographs of neural arbors, and show that it outperforms state-of-the-art techniques.
机译:我们提出了一种新颖的方法来自动描绘形成复杂且可能是环状的网络的曲线结构。通过将图像数据表示为潜在路径的图形,我们首先展示如何使用经过区分训练的分类器加权这些路径,这些分类器既健壮又通用,足以应用于非常不同的成像模式。然后,我们提出了一种整数编程方法,该方法可找到路径的最佳子集,但要遵循消除不合理解决方案的结构和拓扑约束。与早期的方法假定网络采用树形拓扑结构不同,我们的方法显式地对以下事实建模:网络可能包含回路,并且可以重建循环回路和非循环回路。我们在各种具有挑战性的数据集上证明了我们的方法的有效性,这些数据集包括公路网的航拍图像和神经乔木的显微照片,并表明它优于最新技术。

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