Detecting Curves with Unknown Endpoints and Arbitrary Topology Using Minimal Paths
Detecting Curves with Unknown Endpoints and Arbitrary Topology Using Minimal Paths
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DOI：
10.1109/tpami.2011.267
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发表时间：
2012-10
影响因子：
23.6
通讯作者：
V. Kaul;A. Yezzi;Y. Tsai
中科院分区：
文献类型：
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作者：
V. Kaul;A. Yezzi;Y. Tsai
当曲线的两个端点都作为用户输入给出时，或者当给出一个输入并且曲线的总长度预先已知时，现有的最小路径技术很好地用于提取图像中的简单开放曲线。分支的曲线需要来自用户的更进一步的先前输入，即，每个分支端点。在这项工作中，我们提出了一种新的最小路径为基础的算法，它的工作原理更一般的曲线拓扑结构，对用户的初始输入的要求少得多，以前的最小路径为基础的算法。这种新方法的两个关键新颖性和益处是：1）它可以用于检测开放和闭合曲线，包括包含多个分支点和多个闭合环的更复杂的拓扑，而不需要关于要提取这些类型中的哪一种的先验知识，以及2）它仅需要单个输入点，与现有方法相比，不再被限制为所需曲线的端点，而实际上可以是沿着所需曲线的任何点（甚至是内部点）。我们对48幅图像（44幅路面裂缝图像，1幅导管图像和3幅视网膜图像）进行了算法的定量评估，以对抗人类提供的地面真实情况。结果表明，该算法确实能够准确地从图像中提取曲线状物体，与现有的最先进的最小路径为基础的图像处理算法相比，具有更少的先验知识和更少的用户交互。在未来，该算法可以应用于其他二维曲线类物体，它可以扩展到检测三维曲线。
Existing state-of-the-art minimal path techniques work well to extract simple open curves in images when both endpoints of the curve are given as user input or when one input is given and the total length of the curve is known in advance. Curves which branch require even further prior input from the user, namely, each branch endpoint. In this work, we present a novel minimal path-based algorithm which works on much more general curve topologies with far fewer demands on the user for initial input compared to prior minimal path-based algorithms. The two key novelties and benefits of this new approach are that 1) it may be used to detect both open and closed curves, including more complex topologies containing both multiple branch points and multiple closed cycles without requiring a priori knowledge about which of these types is to be extracted, and 2) it requires only a single input point which, in contrast to existing methods, is no longer constrained to be an endpoint of the desired curve but may in fact be ANY point along the desired curve (even an internal point). We perform quantitative evaluation of the algorithm on 48 images (44 pavement crack images, 1 catheter tube image, and 3 retinal images) against human supplied ground truth. The results demonstrate that the algorithm is indeed able to extract curve-like objects accurately from images with far less prior knowledge and less user interaction compared to existing state-of-the-art minimal path-based image processing algorithms. In the future, the algorithm can be applied to other 2D curve-like objects and it can be extended to detect 3D curves.