利用分形分析眼球路径

Q3 Neuroscience
Robert Ahadizad Newport, Sidong Liu, Antonio Di Ieva
{"title":"利用分形分析眼球路径","authors":"Robert Ahadizad Newport, Sidong Liu, Antonio Di Ieva","doi":"10.1007/978-3-031-47606-8_42","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":7360,"journal":{"name":"Advances in neurobiology","volume":"36 ","pages":"827-848"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analyzing Eye Paths Using Fractals.\",\"authors\":\"Robert Ahadizad Newport, Sidong Liu, Antonio Di Ieva\",\"doi\":\"10.1007/978-3-031-47606-8_42\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":7360,\"journal\":{\"name\":\"Advances in neurobiology\",\"volume\":\"36 \",\"pages\":\"827-848\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in neurobiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/978-3-031-47606-8_42\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Neuroscience\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in neurobiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-031-47606-8_42","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Neuroscience","Score":null,"Total":0}
引用次数: 0

摘要

视觉模式反映了我们如何感知信息的解剖学和认知背景,受到刺激物特征和我们自身视觉感知的影响。这些模式在空间上非常复杂,在不同尺度的分形几何中显示出自相似性,因此使用欧几里得几何中使用的传统拓扑维度来测量这些模式具有挑战性。然而,使用分形测量眼球凝视模式的方法在量化几何复杂性、匹配性以及将其应用到机器学习方法中方面取得了成功。这种成功归功于分形在使用希尔伯特曲线降维、使用樋口分形维度(Higuchi fractal dimension,HFD)测量时间复杂性以及使用闵科夫斯基-布里甘维度确定几何复杂性时所具备的固有能力。了解分形在测量和分析眼球凝视模式时的多种应用,可以通过识别与神经病理学相关的标记,扩展当前不断增长的知识体系。此外,在未来的工作中,分形还能利用其获取多尺度信息(包括互补功能、结构和动态)的能力,促进眼球跟踪诊断中成像模式的定义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing Eye Paths Using Fractals.

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Advances in neurobiology
Advances in neurobiology Neuroscience-Neurology
CiteScore
2.80
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信