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相关概念视频

Color Vision01:24

Color Vision

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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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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.
689
Visual System01:26

Visual System

607
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
607
Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

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At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
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Updated: Jul 13, 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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对动态视觉传感器的基于照明的颜色重建

Khen Cohen1, Omer Hershko1, Homer Levy1

  • 1The Faculty of Engineering, Department of Physical Electronics, Tel Aviv University, Tel Aviv 69978, Israel.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种使用动态视觉传感器 (DVS) 重建彩色图像的新方法. 这种技术克服了DVS的局限性,实现了色彩图像重建的最先进的结果.

关键词:
活动照明 活动照明颜色重建 颜色重建计算摄影摄影的使用动态视觉传感器是一个动态视觉传感器.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 传感器技术 传感器技术

背景情况:

  • 动态视觉传感器 (DVS) 捕获亮度变化,但缺乏颜色和强度信息.
  • 重建彩色图像对于许多计算机视觉和DVS应用程序至关重要.
  • 现有的方法经常遭受空间分辨率退化.

研究的目的:

  • 介绍一种用于重建全空间分辨率的新方法,使用DVS.彩色图像.
  • 开发和分析基于DVS的彩色图像重建算法.
  • 用DVS实现彩色图像重建的最先进性能.

主要方法:

  • 使用动态视觉传感器 (DVS) 与主动彩色光源相结合.
  • 开发了两个重建算法:基于线性的方法和基于卷积神经网络 (CNN) 的方法.
  • 分析了DVS响应特征,以准确的颜色重建.

主要成果:

  • 从DVS数据中成功重建了高质量,全空间分辨率的彩色图像.
  • 证明,拟议的方法不会降低空间分辨率.
  • 在不同的照明和距离条件下验证了算法的稳定性.
  • 与以前的方法相比,取得了最先进的结果.

结论:

  • 这种新的方法有效地从DVS重建彩色图像,克服了以前的局限性.
  • 开发的算法提供了高质量,高分辨率的色彩重建.
  • 这项工作提升了对颜色依赖的计算机视觉任务的DVS功能.