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Influence of Earth's Curvature and Atmospheric Refraction on Leveling01:26

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During leveling, the Earth's curvature and atmospheric refraction introduce deviations in the line of sight from a true horizontal reference. When the line of sight is leveled, it remains perpendicular to the plumb line only at a single point. Beyond this, it deviates due to the Earth’s curvature, represented by the correction C. For a sight distance D, the deviation can be derived using the relationship:This relationship shows that the deviation increases quadratically with distance. Over a...
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In chromatography, a solute moves through a chromatographic column and tends to spread, forming a Gaussian-shaped band. The longer the solute spends in the column, the broader the band becomes. The broadening can lead to overlaps within the column, affecting separation effectiveness.
The effectiveness of separation can be evaluated by determining the level of separation between two neighboring peaks in a chromatogram, which represents the individual components of a sample.
In chromatography,...
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Depth Perception and Spatial Vision01:15

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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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High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon
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从基于色谱偏差的高光谱图像进行深度估计.

Zhuang Zhao, Lei Gan, Jing Han

    Optics express
    |December 19, 2025
    PubMed
    概括

    我们开发了一个新的深度估计网络,使用光谱色态偏差,这种现象是不同的光波长聚焦在不同的距离. 这种方法可以实现高精度的光谱深度估计,这是一个缺乏公共数据集的任务.

    科学领域:

    • 光学是什么?光学是什么?光学是什么?
    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 染色偏差导致焦距随波长而变化,在不同距离上聚焦不同的颜色.
    • 现有的深度估计方法缺乏用于光谱信息的专用数据集.

    研究的目的:

    • 提出和验证一种利用光谱色谱偏差的新型深度估计网络.
    • 为了解决缺乏用于光谱深度估计的公共数据集的问题.

    主要方法:

    • 构建了一个大光圈光谱成像系统,以捕捉利用固有色谱偏差的超光谱图像.
    • 开发了一个编码器-解码器网络,具有连续扩展卷积 (cdc) 和局部-全球特征交互 (LGFI) 块.
    • 集成的跳过连接通过将中间深度地图连接到编码器功能来提高深度预测准确度.

    主要成果:

    • 在深度估计中取得的相对误差低于10%.
    • 在定制收集的数据集上,证明准确度超过85%.
    • 验证了基于光谱色态偏差的深度估计网络的有效性.

    结论:

    • 拟议的基于光谱色态偏差的深度估计网络有效地利用光谱信息来准确地绘制深度图.

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  • 开发的高光谱成像系统和数据集使得在光谱深度估计方面的进一步研究成为可能.
  • 这种方法为可用光谱数据的场景中深度估计提供了有希望的解决方案.