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Interpolating cubic spline contours by minimizing second derivative discontinuity

机译:通过最小化二阶导数不连续性来插值三次样条轮廓

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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:它显示了如何从几个边缘位置估计轮廓。该技术使用边缘(切线斜率)的位置和方向将三次样条拟合到边缘,并通过基于二阶导数的最小值来计算切线大小。使用三次样条曲线(分段三阶多项式)是因为它们是可以处理拐点的最低阶多项式。为了确保平滑的整体轮廓,将多项式段连接在一起,以便保留一阶导数的连续性,并最小化二阶导数的不连续性。该技术可以用作输入和编辑轮廓的有效方法,这些轮廓通过边缘方向与基础数据绑定在一起。计算边缘方向所需的时间和找到曲线参数的时间与边缘片段的数量成线性比例。将该算法应用于医学图像,并将结果与​​圆锥样条和B样条以及与三次样条的距离近似进行比较。

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