Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Asynchronous federated learning for web-based OCT image analysis.

Journal of medical imaging (Bellingham, Wash.)·2026
Same author

On opportunities and challenges of large multimodal foundation models in education.

NPJ science of learning·2025
Same author

Digital ink and differentiated subjective ratings for cognitive load measurement in middle childhood.

The British journal of educational psychology·2023
Same author

Investigating the Usability of a Head-Mounted Display Augmented Reality Device in Elementary School Children.

Sensors (Basel, Switzerland)·2021
Same author

Automatic Visual Attention Detection for Mobile Eye Tracking Using Pre-Trained Computer Vision Models and Human Gaze.

Sensors (Basel, Switzerland)·2021
Same author

ARETT: Augmented Reality Eye Tracking Toolkit for Head Mounted Displays.

Sensors (Basel, Switzerland)·2021

相关实验视频

Updated: Sep 14, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.8K

eyeNotate:基于少数镜头图像分类的移动眼睛跟踪数据的交互注释.

Michael Barz1,2, Omair Shahzad Bhatti1, Hasan Md Tusfiqur Alam1

  • 1Interactive Machine Learning, German Research Center for Artificial Intelligence (DFKI), 66123 Saarbrücken, Germany; omair_shahzad.bhatti@dfki.de (O.S.B.); hasan_md_tusfiqur.alam@dfki.de (H.M.T.A.); ho_minh_duy.nguyen@dfki.de (D.M.H.N.); daniel.sonntag@dfki.de (D.S.).

Journal of eye movement research
|July 25, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了eyeNotate,这是一个基于Web的工具,用于半自动移动眼睛跟踪数据的注释. 它使用机器学习来建议固定到区域映射,大大提高了研究人员的注释效率和可靠性.

关键词:
感兴趣的地区 (AOI)眼睛跟踪 眼睛跟踪眼睛追踪数据分析分析.固定到AOI的映射映射交互式机器学习 交互式机器学习移动眼睛追踪器 移动眼睛追踪器视觉注意力 视觉注意力 视觉注意力

更多相关视频

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

10.8K

相关实验视频

Last Updated: Sep 14, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.8K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

10.8K

科学领域:

  • 人与计算机的交互
  • 认知心理学 认知心理学
  • 可用性工程可用性工程

背景情况:

  • 移动眼睛跟踪对于理解心理学和交互设计中的视觉注意力至关重要.
  • 分析移动眼睛跟踪数据目前是一个人工和耗时的过程.
  • 现有的方法对于大数据集缺乏效率和可扩展性.

研究的目的:

  • 开发和评估 eyeNotate,这是一个基于网络的新工具,用于半自动注释移动眼睛跟踪数据.
  • 为了比较基线注释工具的效率,有效性和可靠性与机器学习 (IML支持) 增强的工具.
  • 通过专家评估,评估 eyeNotate 工具的可用性和用户体验.

主要方法:

  • 开发 eyeNotate,这是一个基于Web的注释工具,具有基线和IML支持版本.
  • 一项专家研究 (n=3) 将两种版本的可用性,注释有效性,可靠性和效率进行比较.
  • 由训练有素的注释者重新注释现有的移动眼睛跟踪数据 (n=48).
  • 半结构面试,以收集有关IML功能集成的定性反.
  • 一个后期实验评估图像分类模型的自动注释.

主要成果:

  • 与基线相比,eyeNotate的IML支持版本显示了提高效率和可比的注释有效性和可靠性.
  • 专家注释者认为IML支持功能是积极的,有助于固定到区域映射过程.
  • 定性反突出了机器学习建议在简化注释工作流程中的实用性.
  • 后期实验证实了图像分类模型在可扩展数据注释方面的潜力.

结论:

  • eyeNotate在移动眼部追踪数据分析方面取得了重大进展,减少了人工工作.
  • 集成的少数射击学习模型提高了注释效率,而不会影响数据质量.
  • 该工具是心理学和以人为中心的设计研究人员的宝贵资产,促进了更有效的视觉注意力研究.