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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

709
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
709

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相关实验视频

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Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
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通过注意力诱导的等级变化自动编码器进行凝视估计.

Guanhe Huang, Jingyue Shi, Jun Xu

    IEEE transactions on cybernetics
    |September 20, 2023
    PubMed
    概括

    这项研究引入了一种基于外表的目光估计的新生成框架,称为变异性目光估计网络 (VGE-Net). VGE-Net通过生成多个视线图和使用聚合注意力机制来提高具有挑战性的眼睛图像的准确性.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人与计算机的交互

    背景情况:

    • 基于外观的目光估计对于人机交互至关重要.
    • 确定性模型与低分辨率,黑暗或遮蔽眼睛图像作斗争.

    研究的目的:

    • 开发一种基于外表的可靠的目光估计方法,克服决定性方法的局限性.
    • 引入一种新的生成框架,以提高视线估计准确度.

    主要方法:

    • 建议使用变异推理的变异性凝视估计网络 (VGE-Net).
    • 由地面真相监督生成多个互补的目光地图.
    • 采用基于注意力的回归网络来进行预测的自适应融合.

    主要成果:

    • 在MPIIGaze,EYEDIAP和哥伦比亚基准上,VGE-Net在最先进的方法上表现优越.
    • 特别是在具有挑战性的目光估计场景中观察到显著的改善.
    • 废弃性研究证实了拟议的模型组件的有效性.

    结论:

    • 生成框架和VGE-Net为基于外观的目光估计提供了一个强大的解决方案.

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  • 提出的基于注意力的聚变机制在不利条件下提高了估计准确性.
  • 公开发布的代码将有助于进一步研究和开发凝视估计.