积极的目光标签:可视化用于建立信任
IEEE transactions on visualization and computer graphics
|April 22, 2024
概括
手动标记注视数据的感兴趣区域 (AOI) 是缓慢的,容易出现错误. 本研究引入了一种交互式,基于机器学习的方法,用于更快,更准确的AOI注释,增强凝视分析.
科学领域:
- 人与计算机的交互
- 计算机视觉 计算机视觉
- 数据可视化 数据可视化
背景情况:
- 用于凝视数据分析的感兴趣区域 (AOI) 的手动注释是劳动密集型的,容易引起标签模两可.
- 有效和准确的AOI注释对于可靠的目光数据解释至关重要.
研究的目的:
- 开发一个交互式,以用户为中心的系统,以实现高效和可解释的AOI注释.
- 通过不确定性意识可视化,增强对基于机器学习的AOI分类的信任.
主要方法:
- 一个集成的工作流,将可视化 (EyeFlower字符,缩小维度) 与机器学习和用户反相结合.
- 不确定性意识可视化,以建立用户对分类过程的信任.
- 硬件无关的方法支持固定和移动的眼睛跟踪数据.
主要成果:
- 展示了一种交互式标签方法,简化了AOI注释.
- 展示了不确定性意识可视化在培养用户信任方面的有效性.
- 验证了拟议系统的多功能性和硬件不可知性.
结论:
- 交互式,可解释的注释系统显著提高了AOI标签的效率和准确性.
- 这种方法提高了用户对自动分类的信任,促进了更可靠的目光分析.
- 这种方法为各种眼睛跟踪应用提供了多功能解决方案.
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