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Related Concept Videos

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...
658
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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Related Experiment Video

Updated: Aug 29, 2025

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
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Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

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Progressive Early Image Recognition for Wireless Vision Sensor Networks.

AlKhzami AlHarami1, Abubakar Abubakar1, Bo Zhang2

  • 1Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new wireless vision sensor network (WVSN) concept for efficient video surveillance. It reduces power consumption and bandwidth by processing images at the sensor node, achieving 88% recognition accuracy.

Keywords:
image recognitionimage reconstructionimage restorationsmart cameras

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Wireless vision sensor networks (WVSNs) are crucial for automated tasks like video surveillance.
  • Large-scale deployments face challenges with sensor cost, bandwidth, and power consumption.
  • Internet-of-Things (IoT) integration necessitates optimized resource utilization.

Purpose of the Study:

  • To propose a novel WVSN concept for efficient image compression and early recognition at the sensing node.
  • To reduce bandwidth requirements and smart image processing integrated at the sensing node.
  • To minimize power consumption and bandwidth utilization in WVSNs.

Main Methods:

  • Implemented a WVSN processing only parts of acquired images at the sensor node.
  • Utilized a convolutional neural network at the central server for progressive image recognition.
  • Compared the proposed system against traditional sensor nodes.

Main Results:

  • Achieved an average recognition accuracy of 88% with 83% confidence probability for five subimages.
  • Reduced overall power consumption at the sensor node by 43%.
  • Minimized bandwidth utilization between sensor and server nodes by 86%.

Conclusions:

  • The proposed WVSN implementation offers a significant trade-off in power and bandwidth efficiency.
  • Early image processing at the sensor node enhances WVSN performance.
  • This approach is suitable for large-scale, resource-constrained sensor network applications.