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引用次数: 0
摘要
在本文中,我们提出了曲线的 de Casteljau 细分算法和曲面的 Doo-Sabin 细分算法的新变体。我们的细分方案建立在单调函数诱导的非线性加权平均规则之上。这些平均规则代替了上述著名细分算法中的中点平均规则。分析表明,曲线细分方案的平滑性继承自函数的平滑性,而函数的平滑性诱导了细化方案中使用的平均规则。结果表明,使用我们的细分方案,凸面和凹面均可由同一方案生成。在细分细化中计算边缘点时,只需交换相邻两点的权重即可实现这一点。这是一个优势,因为设计者可以根据自己的兴趣调整极限形状。
In this paper, we present new variants of both the de Casteljau subdivision algorithm for curves and Doo–Sabin subdivision algorithm for surfaces. Our subdivision schemes are built on nonlinear weighted averaging rules which are induced by monotonic functions. These averaging rules are used instead of midpoint averaging rule in the mentioned well-known subdivision algorithms. The analysis shows that the smoothness of the subdivision schemes for curves is inherited from the smoothness of the function which induces the averaging rule used in the refinement of the schemes. The results show that with our subdivision schemes, both convex surfaces and concave surfaces can be generated by the same scheme. This happens by only interchanging the weights of two adjacent points when computing the edge points in the subdivision refinement. This is an advantage since a designer can adjust the limit shape according to his interests.
期刊介绍:
Communications in Mathematics and Statistics is an international journal published by Springer-Verlag in collaboration with the School of Mathematical Sciences, University of Science and Technology of China (USTC). The journal will be committed to publish high level original peer reviewed research papers in various areas of mathematical sciences, including pure mathematics, applied mathematics, computational mathematics, and probability and statistics. Typically one volume is published each year, and each volume consists of four issues.