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利用分形分析眼径。

Analyzing Eye Paths Using Fractals.

机构信息

Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia.

出版信息

Adv Neurobiol. 2024;36:827-848. doi: 10.1007/978-3-031-47606-8_42.

Abstract

Visual patterns reflect the anatomical and cognitive background underlying process governing how we perceive information, influenced by stimulus characteristics and our own visual perception. These patterns are both spatially complex and display self-similarity seen in fractal geometry at different scales, making them challenging to measure using the traditional topological dimensions used in Euclidean geometry.However, methods for measuring eye gaze patterns using fractals have shown success in quantifying geometric complexity, matchability, and implementation into machine learning methods. This success is due to the inherent capabilities that fractals possess when reducing dimensionality using Hilbert curves, measuring temporal complexity using the Higuchi fractal dimension (HFD), and determining geometric complexity using the Minkowski-Bouligand dimension.Understanding the many applications of fractals when measuring and analyzing eye gaze patterns can extend the current growing body of knowledge by identifying markers tied to neurological pathology. Additionally, in future work, fractals can facilitate defining imaging modalities in eye tracking diagnostics by exploiting their capability to acquire multiscale information, including complementary functions, structures, and dynamics.

摘要

视觉模式反映了支配我们感知信息的过程的解剖学和认知背景,受刺激特征和我们自己的视觉感知影响。这些模式在空间上既复杂又具有自相似性,在分形几何中以不同的尺度显现,这使得使用欧几里得几何中传统的拓扑维度来测量它们具有挑战性。然而,使用分形测量眼动模式的方法已经成功地量化了几何复杂性、可匹配性,并将其应用于机器学习方法中。这种成功归因于分形在使用希尔伯特曲线降低维度、使用 Higuchi 分形维数(HFD)测量时间复杂性以及使用闵可夫斯基-布尔加因维数确定几何复杂性时所具有的固有能力。

了解分形在测量和分析眼动模式方面的许多应用,可以通过识别与神经病理学相关的标志物,扩展当前不断增长的知识体系。此外,在未来的工作中,分形可以通过利用获取多尺度信息的能力,包括互补的功能、结构和动态,来促进眼动追踪诊断中成像方式的定义。

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