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Finding a Closed Boundary by Growing Minimal Paths from a Single Point 2D or 3D Images

机译:通过从单点2D或3D图像中生长最小的路径来找到封闭的边界

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In this paper, we present a new method for segmenting closed contours and surfaces. Our work builds on a variant of the Fast Marching algorithm. First, an initial point on the desired contour is chosen by the user. Next, new keypoints are detected automatically using a front propagation approach We assume that the desired object has a closed boundary. This a-priori knowledge on the topology is used to devise a relevant criterion for stopping the keypoint detection and front propagation. The final domain visited by the front will yield a band surrounding the object of interest. Linking pairs of neighboring keypoints with minimal paths allows us to extract a closed contour from a 2D image Detection of a variety of objects on real images is demonstrated Using a similar same idea, we can extract networks of minimal paths from a 3D image called Geodesic Meshing. The proposed method is applied to 3D data with promising results.
机译:在本文中,我们提出了一种用于分割闭合轮廓和表面的新方法。我们的工作建立在快速行进算法的变种上。首先,用户选择所需轮廓上的初始点。接下来,使用前传播方法自动检测新的关键点,我们假设所需对象具有闭合边界。关于拓扑的A-Priori知识用于设计用于停止关键点检测和前传播的相关标准。前线访问的最终域将产生围绕感兴趣对象的乐队。具有最小路径的相邻关键点的链接对允许我们从2D图像检测中提取闭合轮廓,使用类似相同的想法对实际图像上的各种对象进行说明,我们可以从名为GeodeSic网格的3D图像中提取最小路径的网络。该方法应用于具有有前途的结果的3D数据。

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