通过曲线图像网格构建生物形状的离散描述

Jing Xu, Andrey N. Chernikov
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引用次数: 0

摘要

网格生成是获取由图像表示的生物对象的离散描述符的有用工具。直边单元网格的生成已经被很好地理解了。然而,为了匹配自然界中无处不在的弯曲形状,需要具有弯曲(高阶)元素的网格。此外,对于大型数据集的处理,需要进行自动网格划分。在这项工作中,我们提出了一种允许自动构建高阶曲线网格的新技术。该技术允许将直边网格转换为具有C1或C2平滑边界的曲线网格,同时保持所有元素有效,并且通过雅可比矩阵测量具有良好的质量。用实例说明了这种技术。实验结果表明,网格边界能够很自然地表示物体的形状,与相应的线性网格相比,网格边界的表示精度得到了提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction of discrete descriptions of biological shapes through curvilinear image meshing
Mesh generation is a useful tool for obtaining discrete descriptors of biological objects represented by images. The generation of meshes with straight sided elements has been fairly well understood. However, in order to match curved shapes that are ubiquitous in nature, meshes with curved (high-order) elements are required. Moreover, for the processing of large data sets, automatic meshing procedures are needed. In this work, we present a new technique that allows for the automatic construction of high-order curvilinear meshes. This technique allows for a transformation of straight-sided meshes to curvilinear meshes with C1 or C2 smooth boundaries while keeping all elements valid and with good quality as measured by their Jacobians. The technique is illustrated with examples. Experimental results show that the mesh boundaries naturally represent the objects’ shapes, and the accuracy of the representation is improved compared to the corresponding linear mesh.
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