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Related Concept Videos

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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Color Vision01:24

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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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Depth Perception and Spatial Vision01:15

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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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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Electron Microscope Tomography and Single-particle Reconstruction01:07

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
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Targeted Cancer Therapies02:57

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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
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Underwater Target Detection and 3D Reconstruction System Based on Binocular Vision.

Guanying Huo1,2, Ziyin Wu3, Jiabiao Li4

  • 1Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China. huoguanying@163.com.

Sensors (Basel, Switzerland)
|October 24, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a binocular vision system for underwater target detection and 3D reconstruction. The system achieves accurate 3D measurements and detailed reconstruction of marine targets within optimal ranges.

Keywords:
3D reconstructionbinocular visiondisparity map optimizationsemi-global stereo matchingunderwater target detection

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Area of Science:

  • Robotics
  • Computer Vision
  • Marine Technology

Background:

  • Underwater target detection and 3D reconstruction present significant challenges.
  • Existing methods struggle with the complexities of marine environments and 3D data acquisition.

Purpose of the Study:

  • To develop a robust binocular vision system for underwater target detection.
  • To improve the accuracy and effectiveness of 3D reconstruction for underwater targets.

Main Methods:

  • Utilized a binocular vision system with two optical sensors.
  • Applied image denoising, color restoration, and super-pixel segmentation for target extraction.
  • Improved semi-global stereo matching with constrained matching and least squares fitting for disparity map optimization.
  • Employed triangulation principles for 3D data calculation and MeshLab for visualization.

Main Results:

  • The system demonstrated higher measurement accuracy for specific underwater targets.
  • Achieved a superior 3D reconstruction effect within a suitable operational distance.
  • Successfully extracted and reconstructed target structures and color information.

Conclusions:

  • The proposed binocular vision system effectively addresses underwater target detection and 3D reconstruction challenges.
  • The optimized stereo matching and 3D calculation methods enhance accuracy and visual fidelity.
  • The system is suitable for applications requiring precise underwater target measurement and modeling.