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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

612
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.
612

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相关实验视频

Updated: Jun 15, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

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使用模拟和图像传输方法从单眼内镜进行深度估计.

Bong Hyuk Jeong1, Hang Keun Kim2, Young Don Son2

  • 1Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, 21999, South Korea.

Computers in biology and medicine
|August 23, 2024
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种深度学习方法,用于从内镜图像中估计深度,克服空间限制. 新技术准确地估计了深度,改善了内镜导航和临床结果.

关键词:
深度学习是一种深度学习.深度估计估计的深度.内镜检查是指内镜检查.没有了,没有了,没有了.模拟到真实的转移转移.

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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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相关实验视频

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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:

  • 准确的深度感知对于内镜导航系统至关重要.
  • 内镜器件的空间限制限制了深度摄像头的集成.
  • 目前用于内镜深度估计的方法面临挑战.

研究的目的:

  • 开发一个深度学习模型,直接从内镜图像进行准确的深度图像估计.
  • 为了解决将深度摄像头纳入内镜系统的不切实际性.
  • 通过改善深度信息,提高内镜的导航和临床结果.

主要方法:

  • 使用Unity和计算机断层扫描结肠镜数据生成模拟内镜图像和深度图.
  • 采用循环生成对抗网络 (cGAN) 来增强模拟图像的真实性.
  • 训练了一个深度学习模型,使用合成数据进行深度估计.

主要成果:

  • 与以前的无监督方法相比,拟议的方法在估计深度图像方面表现出更高的精度.
  • 使用cGAN合成的数据提高了训练图像的真实性.
  • 深度学习模型实现了准确的深度估计.

结论:

  • 开发的深度学习方法为内镜中深度估计提供了实际解决方案.
  • 这种方法可以显著提升内镜导航和病变标记.
  • 预计改进的深度信息将导致更好的患者治疗结果.