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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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Perceptual Constancy01:12

Perceptual Constancy

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Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
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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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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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相关实验视频

Updated: Jul 27, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

586

CVCC模型:基于学习的计算机视觉颜色常数与RiR-DSN架构.

Ho-Hyoung Choi1

  • 1School of Dentistry, Advanced Dental Device Development Institute, Kyungpook National University, Jung-gu, Daegu 41940, Republic of Korea.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
概括

这项研究介绍了一种用于计算机视觉颜色恒定性 (CVCC) 的新型残余密集选择性内核网络 (RiR-DSN). RiR-DSN显著提高了照明估计的准确性,在各种条件下优于现有的方法.

科学领域:

  • 计算机视觉 计算机视觉
  • 图像处理 图像处理
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 估计场景照明对于计算机视觉颜色恒定性 (CVCC) 至关重要,但仍然具有挑战性.
  • 现有的CVCC算法在异常情况下难以准确,这限制了它们的实际应用.

研究的目的:

  • 通过提出一种新的深度学习架构来解决当前CVCC方法的局限性.
  • 为了提高数字图像照明估计的准确性和稳定性.

主要方法:

  • 引入一个残余密集选择性内核网络 (RiR-DSN).
  • 该架构使用具有动态波器尺寸调制的选择性内核卷积块 (SKCB).
  • 带有相互连接的神经元的前网络结构促进了特征的传播和重复使用.

主要成果:

  • RiR-DSN架构有效地减轻了消失梯度,并促进了功能重用.
  • 与最先进的方法相比,在CVCC任务中表现出优越的性能.
  • 取得了相机和照明器不变的结果,展示了强度.

结论:

  • 拟议的RiR-DSN在计算机视觉颜色恒定性方面取得了重大进展.
关键词:
这是一个RiR-DSN架构.计算机视觉颜色的恒定性 颜色的恒定性照明估计估计的估计.场景照明色彩照明剂颜色

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  • 它独特的架构提供了改进的功能处理和参数效率.
  • 该方法在不同的成像条件下被证明是有效和稳健的.