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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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Vision01:24

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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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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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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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The Retina01:32

The Retina

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The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Analysis Model of Image Colour Data Elements Based on Deep Neural Network.

Chao Jiang1, Zhen Jiang1, Daijiao Shi1

  • 1School of Arts, Anhui University of Finance and Economics, Bengbu 233030, Anhui, China.

Computational Intelligence and Neuroscience
|July 28, 2022
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Summary
This summary is machine-generated.

This study introduces a novel deep neural network model for objective image color element analysis, overcoming limitations of subjective visual evaluation. The new method enhances efficiency and accuracy in color data analysis and spectral reconstruction.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Current image color element analysis in China relies on subjective visual evaluation, leading to inefficiency and errors.
  • Human factors significantly impact the accuracy and reliability of traditional image analysis methods.

Purpose of the Study:

  • To develop an advanced image color data element analysis model using deep neural networks.
  • To improve the efficiency and accuracy of image color analysis compared to subjective methods.

Main Methods:

  • Implementation of an intelligent image color data element analysis model using TensorFlow and a Docker cluster framework.
  • Integration of a quantization-modified error diffusion model for enhanced color management and accuracy.
  • Application of rotating principal component analysis for image color error correction.
  • Utilization of transfer learning and convolutional neural networks for the deep neural network model.

Main Results:

  • The proposed deep neural network model accurately reveals true color components in target images.
  • High spectral data reconstruction accuracy was achieved for the real color components.
  • The analysis results demonstrated strong adaptability across various image datasets.

Conclusions:

  • The novel deep neural network-based approach offers a more objective, efficient, and accurate method for image color element analysis.
  • This method significantly improves color management and spectral data reconstruction in image reproduction processes.