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Exponential splines and minimal-support bases for curve representation

机译:曲线表示的指数样条和最小支持基础

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Our interest is to characterize the spline-like integer-shift-invariant bases capable of reproducing exponential polynomial curves. We prove that any compact-support function that reproduces a subspace of the exponential polynomials can be expressed as the convolution of an exponential B-spline with a compact-support distribution. As a direct consequence of this factorization theorem, we show that the minimal-support basis functions of that subspace are linear combinations of derivatives of exponential B-splines. These minimal-support basis functions form a natural multiscale hierarchy, which we utilize to design fast multiresolution algorithms and subdivision schemes for the representation of closed geometric curves. This makes them attractive from a computational point of view. Finally, we illustrate our scheme by constructing minimal-support bases that reproduce ellipses and higher-order harmonic curves.
机译:我们的兴趣是表征能够重现指数多项式曲线的样条状整数移位不变基。我们证明,任何能够再现指数多项式子空间的紧致支持函数都可以表示为具有紧致支持分布的指数B样条的卷积。作为该分解定理的直接结果,我们证明了该子空间的最小支持基函数是指数B样条的导数的线性组合。这些最少支持的基础函数形成自然的多尺度层次结构,我们利用它来设计快速的多分辨率算法和细分方案,以表示闭合的几何曲线。从计算的角度来看,这使它们具有吸引力。最后,我们通过构造可再现椭圆和高阶谐波曲线的最小支持基础来说明我们的方案。

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