Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Vision

Vision

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.

Visual System

Visual System

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model.

Diagnostics (Basel, Switzerland)·2023

Same author

Correction: Kumar et al. Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System. <i>Sensors</i> 2022, <i>22</i>, 5160.

Sensors (Basel, Switzerland)·2023

Same author

DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images.

Diagnostics (Basel, Switzerland)·2023

Same author

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System.

Sensors (Basel, Switzerland)·2022

Same author

Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images.

Computational intelligence and neuroscience·2022

Same author

Scene Text Recognition Based on Bidirectional LSTM and Deep Neural Network.

Computational intelligence and neuroscience·2021

Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025

Same journal

RETRACTION: Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background.

Computational intelligence and neuroscience·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 12, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Object-Based Image Retrieval Using the U-Net-Based Neural Network.

Sandeep Kumar¹, Arpit Jain², Ambuj Kumar Agarwal³

¹Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Vijayawada, Andhra Pradesh, India.

Computational Intelligence and Neuroscience

|November 22, 2021

Summary

This summary is machine-generated.

This study introduces a U-Net neural network and Haar wavelet for content-based image retrieval (CBIR), enhancing accuracy and efficiency. The proposed method significantly improves image retrieval performance on benchmark datasets.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Related Experiment Videos

Last Updated: Oct 12, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Computer Science
Artificial Intelligence
Image Processing

Background:

Digital image retrieval is increasingly important due to widespread internet and social media usage.
Existing methods for content-based image retrieval (CBIR) face challenges in accuracy and efficiency.
Deep learning techniques offer potential for improved feature extraction in image retrieval.

Purpose of the Study:

To propose a novel U-Net-based neural network for image segmentation in CBIR.
To integrate Haar Discrete Wavelet Transform (DWT) and lifting wavelet schemes for effective feature extraction.
To enhance the accuracy and efficiency of content-based image retrieval systems.

Main Methods:

A U-Net-based convolutional neural network (CNN) was employed for image segmentation.
Haar DWT and lifting wavelet schemes were utilized for feature extraction.
The proposed method was evaluated on two benchmark datasets: Corel 1K and Corel 5K.

Main Results:

The proposed method achieved high accuracy rates of 93.01% on Corel 1K and 88.39% on Corel 5K.
U-Net segmentation reduced feature vector dimensions and decreased feature extraction time by 5 seconds.
Performance analysis demonstrated improvements in accuracy, precision, and recall for image retrieval.

Conclusions:

The U-Net-based approach significantly enhances image retrieval performance.
The integration of U-Net with Haar DWT and lifting wavelets offers a robust solution for CBIR.
This research contributes to more accurate and efficient digital image retrieval systems.