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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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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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Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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相关实验视频

Updated: Jun 16, 2025

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
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Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

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对于光场图像的半监控语义细分,使用差异信息.

Shansi Zhang, Yaping Zhao, Edmund Y Lam

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |August 16, 2024
    PubMed
    概括

    本研究引入了一种半监督的光场 (LF) 语义细分方法,减少了对广泛的像素注释的需求. 它有效地使用LF差异信息,以最少的标记数据来改善场景理解.

    科学领域:

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

    背景情况:

    • 光场 (LF) 图像捕获多视图信息,对于场景理解至关重要.
    • 对LF图像的语义细分至关重要,但受到监督方法中需要广泛的像素智能注释的阻碍.

    研究的目的:

    • 为LF语义细分开发一种半监督的方法,尽量减少对标记数据的要求.
    • 为了利用LF差异信息来提高语义细分精度.

    主要方法:

    • 一个无监督的差异估计网络被设计成为每个视图生成差异地图.
    • 用差异地图和中央视图标签为外围视图生成伪标签,预测为了可靠性而合并.
    • 引入了差异语义的一致性损失和全面的对比学习方案 (像素级和对象级).

    主要成果:

    • 拟议的方法在基准LF语义细分数据集上实现了最先进的性能.
    • 它显示了与完全监督的方法可比的结果,即使标记数据显著减少 (例如,1/2协议).

    结论:

    • 半监督方法有效地减少了LF语义细分中的注释负担.
    • 利用LF差异信息和对比学习显著提高了细分性能和特征表示.

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