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Interpolating cubic spline contours by minimizing second derivative discontinuity

机译:通过最小化第二衍生不连续性来插入立方样条轮廓

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It is shown how a contour can be estimated from a few edge positions. The technique fits a cubic spline to edges using the position and orientations of edges (tangent slopes) and computes tangent magnitudes by a minimization based on the second derivatives. Cubic splines (piecewise third-order polynomials) are used because they are the lowest-order polynomials that can deal with inflection points. For assuring a smooth overall contour, the polynomial segments are joined such that the continuity of the first derivative is preserved and discontinuity in the second derivative is minimized. This technique can be used as an efficient means for entering and editing contours which are tied to the underlying data through the edge orientations. The time required for computing the edge orientations and the time for finding the curve parameters are linearly proportional to the number of edge fragments. The algorithm was applied to medical images, and the results are compared with the conic splines and the B-splines and the distance approximation to the cubic splines.
机译:示出了如何从几个边缘位置估计轮廓。该技术适合使用边缘(切线)的位置和取向的立方样条来编辑,并通过基于第二衍生物的最小化来计算切线幅度。使用立方样条(分段三阶多项式),因为它们是可以处理拐点的最低阶多项式。为了确保光滑的整体轮廓,多项式段被连接,使得第一衍生物的连续性被保存,并且第二衍生物中的不连续性被最小化。该技术可以用作进入和编辑轮廓的有效手段,其通过边缘方向与基础数据相关联。计算边缘方向所需的时间以及查找曲线参数的时间是线性的与边缘片段的数量成比例。将该算法应用于医学图像,并将结果与​​圆锥花键和B样条与立方样条近似的距离进行比较。

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