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arXiv 2102.08260math.ATcs.CG
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欧拉示性数曲面

Euler Characteristic Surfaces

Gabriele Beltramo, Rayna Andreeva, Ylenia Giarratano, Miguel O. Bernabeu, Rik Sarkar, Primoz Skraba

摘要:

研究了欧拉示性数在多参数拓扑数据分析中的应用。欧拉示性数是一个经典的、易于理解的拓扑不变量,它已经出现在许多应用中,包括在随机场的上下文中。本文的目的是将欧拉示性数推广到高维参数空间。虽然使用更强的不变量如同调的高维参数空间的拓扑数据分析仍然是深入研究的主题,但欧拉示性数在理论上和计算上更易于管理,并且该分析可以被视为多参数拓扑数据分析中的重要中间步骤。我们使用人工生成的例子,并在视网膜图像中检测糖尿病视网膜病变的现实世界中的应用程序的技术的有用性。

英文摘要:

We study the use of the Euler characteristic for multiparameter topological data analysis. Euler characteristic is a classical, well-understood topological invariant that has appeared in numerous applications, including in the context of random fields. The goal of this paper is to present the extension of using the Euler characteristic in higher-dimensional parameter spaces. While topological data analysis of higher-dimensional parameter spaces using stronger invariants such as homology continues to be the subject of intense research, Euler characteristic is more manageable theoretically and computationally, and this analysis can be seen as an important intermediary step in multi-parameter topological data analysis. We show the usefulness of the techniques using artificially generated examples, and a real-world application of detecting diabetic retinopathy in retinal images.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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