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相关概念视频

Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

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Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
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相关实验视频

Updated: Jun 15, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

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对于单拍结构光谱的深度学习:全面的数据集和性能分析分析.

Rhys G Evans1, Ester Devlieghere2, Robrecht Keijzer2

  • 1Industrial Vision Lab (InViLab), Faculty of Applied Engineering, Campus Groenenborger, University of Antwerp, Groenenborgerlaan 179, 2020 Antwerp, Belgium.

Journal of imaging
|August 28, 2024
PubMed
概括
此摘要是机器生成的。

为基于深度学习的结构化光谱测量 (SS-DL-SLP) 创建了一个新数据集,包含超过10,000对物理数据对. 这一基准数据集使得可靠的3D光学计量研究和开发成为可能.

关键词:
3D成像和传感器的使用.数据集数据集数据集机器学习是机器学习.个人资料测量 个人资料测量 个人资料测量结构光的结构光.

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Scanning Light Scattering Profiler SLPS Based Methodology to Quantitatively Evaluate Forward and Backward Light Scattering from Intraocular Lenses
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科学领域:

  • * 3D 光学计量学 3D 光学计量学
  • * 计算机视觉 计算机视觉
  • * 机器学习 * 机器学习

背景情况:

  • * 基于深度学习的单拍结构光谱测量 (SS-DL-SLP) 提供快速,稳定的3D测量.
  • * 获得SS-DL-SLP的大型训练数据集实际上是具有挑战性的.
  • * 现有的方法缺乏全面的,标准化的基准来评估深度学习模型.

研究的目的:

  • *为SS-DL-SLP引入一个大规模的,物理生成的数据集.
  • * 建立一个评估和比较3D光学计量学深度学习模型的基准.
  • * 促进SS-DL-SLP的研究和促进其可复制性.

主要方法:

  • * 构建一个数据集,包含超过1万个物理数据对.
  • * 使用随机表面形状的3D打印校准目标.
  • *记录相应的身高形状和变形的边缘图案.

主要成果:

  • * 既定神经网络在从边缘图案中预测高度信息方面具有非常高的准确性.
  • * 在独特的,看不见的对象上验证了网络稳定性.
  • * 实现了可靠的全场高度信息检索.

结论:

  • * 本文所介绍的数据集是推动SS-DL-SLP发展的宝贵资源.
  • *公开可用的代码和数据促进了3D光学计量学的进一步研究和开发.
  • * 该基准有助于探索和比较新的深度学习方法.