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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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DDCATNet: Effective Deep Learning-Based Illumination Color Cast Estimation Approach for Achieving Computational Color Constancy.

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ARiRTN: A Novel Learning-Based Estimation Model for Regressing Illumination.

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Related Experiment Video

Updated: Jul 27, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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CVCC Model: Learning-Based Computer Vision Color Constancy with RiR-DSN Architecture.

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
Summary

This study introduces a novel Residual-in-Residual Dense Selective Kernel Network (RiR-DSN) for computer vision color constancy (CVCC). The RiR-DSN significantly improves illumination estimation accuracy, outperforming existing methods under various conditions.

Keywords:
RiR-DSN architecturecomputer vision color constancyillumination estimationscene illuminant color

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Area of Science:

  • Computer Vision
  • Image Processing
  • Deep Learning

Background:

  • Estimating scene illumination is crucial for computer vision color constancy (CVCC) but remains challenging.
  • Existing CVCC algorithms struggle with accuracy under unusual circumstances, limiting their practical application.

Purpose of the Study:

  • To address limitations in current CVCC methods by proposing a novel deep learning architecture.
  • To enhance the accuracy and robustness of illumination estimation in digital images.

Main Methods:

  • Introduction of a Residual-in-Residual Dense Selective Kernel Network (RiR-DSN).
  • The architecture utilizes Selective Kernel Convolutional Blocks (SKCBs) with dynamic filter size modulation.
  • A feed-forward network structure with interconnected neurons facilitates feature propagation and reuse.

Main Results:

  • The RiR-DSN architecture effectively alleviates vanishing gradients and promotes feature reuse.
  • Demonstrated superior performance compared to state-of-the-art methods in CVCC tasks.
  • Achieved camera- and illuminant-invariant results, showcasing robustness.

Conclusions:

  • The proposed RiR-DSN offers a significant advancement in computer vision color constancy.
  • Its unique architecture provides improved feature handling and parameter efficiency.
  • The method proves effective and robust across different imaging conditions.