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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

644
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.
644

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相关实验视频

Updated: Jun 29, 2025

Determining 3D Flow Fields via Multi-camera Light Field Imaging
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基于单眼深度估计的交通图像合成雾气天气模拟算法

Minan Tang1, Zixin Zhao2, Jiandong Qiu2

  • 1College of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730050, China.

Sensors (Basel, Switzerland)
|March 28, 2024
PubMed
概括

研究人员使用单眼深度估计开发了一种新的雾天气模拟算法. 这种方法从清晰的模糊图像中生成真实的模糊图像,解决了对物体检测研究模糊数据集的稀缺问题.

关键词:
大气散射模型的模拟.模拟雾模拟系统单眼的深度估计估计.自然景观统计自然景观统计交通安全 交通安全 交通安全

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Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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科学领域:

  • 计算机视觉 计算机视觉
  • 图像处理 图像处理
  • 人工智能的人工智能

背景情况:

  • 在雾条件下对象检测对于基于学习的方法来说是一个挑战.
  • 现有的雾交通图像数据集很少,阻碍了研究和开发.

研究的目的:

  • 提出一个新的雾气天气模拟算法.
  • 为了解决缺乏多样化的模糊图像数据集,用于训练和评估物体检测模型.

主要方法:

  • 利用自我监督的单眼深度估计来生成相对和绝对深度地图.
  • 使用密集的几何约束来进行尺度回收.
  • 定义可见度以生成传导率图.
  • 使用暗通道地图估计的大气光值.
  • 应用大气散射模型用于雾模拟.

主要成果:

  • 生成的雾模拟图像紧密地模仿了自然雾的特征 (AuthESI <2对>90%的图像).
  • 该方法成功地将清晰的图像转换为现实的雾场景.
  • 验证了拟议的雾模拟算法的有效性.

结论:

  • 开发的算法为增强模糊图像数据集提供了可行的解决方案.
  • 允许在恶劣天气中对物体检测系统进行更强大的训练和评估.
  • 促进自动驾驶和智能交通系统的发展.