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Inferring surface trace and differential structure from 3-D images

机译:从3-D图像推断表面痕迹和微分结构

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Early image understanding seeks to derive analytic representations from image intensities. The authors present steps towards this goal by considering the inference of surfaces from three-dimensional images. Only smooth surfaces are considered and the focus is on the coupled problems of inferring the trace points (the points through which the surface passes) and estimating the associated differential structure given by the principal curvature and direction fields over the estimated smooth surfaces. Computation of these fields is based on determining an atlas of local charts or parameterizations at estimated surface points. Algorithm robustness and the stability of results are essential for analyzing real images; to this end, the authors present a functional minimization algorithm utilizing overlapping local charts to refine surface points and curvature estimates, and develop an implementation as an iterative constraint satisfaction procedure based on local surface smoothness properties. Examples of the recovery of local structure are presented for synthetic images degraded by noise and for clinical magnetic resonance images.
机译:早期的图像理解力图从图像强度中得出解析表示。作者通过考虑从三维图像推断表面来提出实现该目标的步骤。仅考虑光滑的表面,并且重点在于耦合的问题:推断轨迹点(表面通过的点),并估计在估计的光滑表面上由主曲率和方向场给出的关联微分结构。这些字段的计算基于确定估计表面点处的局部图集或参数化图集。算法的鲁棒性和结果的稳定性对于分析真实图像至关重要。为此,作者提出了一种功能最小化算法,该算法利用重叠的局部图来细化表面点和曲率估计,并开发出一种基于局部表面光滑度特性的迭代约束满足程序。对于因噪声而退化的合成图像和临床磁共振图像,提供了局部结构恢复的示例。

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