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Related Concept Videos

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.
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Visual System01:26

Visual System

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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...
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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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Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
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Illumination-Based Color Reconstruction for the Dynamic Vision Sensor.

Khen Cohen1, Omer Hershko1, Homer Levy1

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

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|October 14, 2023
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Summary
This summary is machine-generated.

Researchers developed a new method to reconstruct colored images using dynamic vision sensors (DVS). This technique overcomes DVS limitations, achieving state-of-the-art results for color image reconstruction.

Keywords:
active illuminationcolor reconstructioncomputational photographydynamic vision sensor

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Area of Science:

  • Computer Vision
  • Robotics
  • Sensor Technology

Background:

  • Dynamic Vision Sensors (DVS) capture brightness changes but lack color and intensity information.
  • Reconstructing color images is crucial for many computer vision and DVS applications.
  • Existing methods often suffer from spatial resolution degradation.

Purpose of the Study:

  • To present a novel method for reconstructing full spatial resolution, colored images using DVS.
  • To develop and analyze algorithms for DVS-based color image reconstruction.
  • To achieve state-of-the-art performance in colored image reconstruction with DVS.

Main Methods:

  • Utilized a dynamic vision sensor (DVS) combined with an active colored light source.
  • Developed two reconstruction algorithms: a linear-based approach and a convolutional neural network (CNN)-based approach.
  • Analyzed DVS response characteristics for accurate color reconstruction.

Main Results:

  • Successfully reconstructed high-quality, full spatial resolution colored images from DVS data.
  • Demonstrated that the proposed methods do not degrade spatial resolution.
  • Validated the algorithm's robustness across varying illumination and distance conditions.
  • Achieved state-of-the-art results compared to previous methods.

Conclusions:

  • The novel method effectively reconstructs colored images from DVS, overcoming previous limitations.
  • The developed algorithms provide high-quality, high-resolution color reconstruction.
  • This work advances DVS capabilities for color-dependent computer vision tasks.