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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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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.
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相关实验视频

Updated: Jul 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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多尺度,多维的双眼内镜图像深度估计网络.

Xiongzhi Wang1, Yunfeng Nie2, Wenqi Ren3

  • 1School of Future Technology, University of Chinese Academy of Sciences, Beijing 100039, China; School of Aerospace Science And Technology, Xidian University, Xian 710071, China.

Computers in biology and medicine
|August 19, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习模型,用于从内镜图像进行实时深度估计,这对于外科导航至关重要. 开发的多级监督深度估计网络 (MMDENet) 在具有挑战性的外科环境中显著提高了准确性.

关键词:
卷积神经网络是一种卷积神经网络.深度估计估计的深度.内镜数据集的内镜数据集.立体镜视力 立体镜视力

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科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 手术技术 手术技术

背景情况:

  • 实时深度估计对于微创手术至关重要.
  • 缺乏内镜数据集阻碍了深度获取的深度学习应用程序.
  • 当前的方法在复杂的外科环境中难以准确.

研究的目的:

  • 开发一个高精度的3D模拟模型,用于生成内镜图像数据集.
  • 为实时深度估计创建一个端到端的深度学习网络.
  • 通过精确的深度信息来增强外科导航能力.

主要方法:

  • 提出了一个端到端的多规模监管深度估计网络 (MMDENet).
  • 整合了多尺度特征提取模块,以提高对应精度.
  • 使用多维信息指导精细化模块来优化差异地图.

主要成果:

  • 与最先进的方法相比,终点误差减少了3.14%.
  • 实时处理在每秒大约30.
  • 在实际内镜图像上验证了性能,精度为93.38%.

结论:

  • MMDENet为内镜手术中的实时深度估计提供了一个有前途的解决方案.
  • 该模型的准确性和速度满足了外科导航应用的要求.
  • 在现实世界的场景中高精度表明临床采用有很大的潜力.