Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

681
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.
681
Association Areas of the Cortex01:21

Association Areas of the Cortex

5.4K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.4K
Vision01:24

Vision

53.5K
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.
53.5K
Visual System01:26

Visual System

594
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...
594
Effects of feedback01:24

Effects of feedback

574
Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
574
Neural Circuits01:25

Neural Circuits

1.3K
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.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Integrating Visual Perception and Control Strategies in Custom Omnidirectional Mobile Robots.

Sensors (Basel, Switzerland)·2026
Same author

High-Content Imaging and Machine Learning Classify Phenotypical Change in Coronary Artery Endothelial Cells Caused by BPS.

International journal of molecular sciences·2026
Same author

SpiKon-E: Hybrid Soft Artificial Muscle Control Using Hardware Spiking Neural Network.

Biomimetics (Basel, Switzerland)·2025
Same author

Bio-Inspired Control System for Fingers Actuated by Multiple SMA Actuators.

Biomimetics (Basel, Switzerland)·2022
Same author

Stereo Vision Based Sensory Substitution for the Visually Impaired.

Sensors (Basel, Switzerland)·2019
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

565

Enhancing Visual Feedback Control through Early Fusion Deep Learning.

Adrian-Paul Botezatu1, Lavinia-Eugenia Ferariu1, Adrian Burlacu1

  • 1Faculty of Automatic Control and Computer Engineering, "Gheorghe Asachi" Technical University of Iasi, D. Mangeron 27, 700050 Iasi, Romania.

Entropy (Basel, Switzerland)
|October 28, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning models with early fusion enhance visual servoing systems. Integrating image moments and segmentation maps improves robot control accuracy for 6-DOF robots.

Keywords:
convolutional neural networkearly fusionfeature pointsimage momentssegmentationvisual feedback control

More Related Videos

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

405
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.5K

Related Experiment Videos

Last Updated: Jul 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

565
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

405
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.5K

Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Visual servoing systems use visual feedback for robot control.
  • Deep learning models process visual data for robot movement computation.
  • Early fusion integrates additional information into neural network inputs.

Purpose of the Study:

  • To investigate early fusion techniques for visual servoing using deep models.
  • To analyze the impact of image moments, segmentation, and feature points on control accuracy.
  • To determine the optimal level of detail in auxiliary maps for robot movement control.

Main Methods:

  • Implemented early fusion by integrating image moments, region-based segmentation, and feature points into deep models.
  • Applied these techniques individually and in combination to generate maps of varying detail.
  • Experimentally evaluated the performance of these methods on a 6-degree-of-freedom robot control task.

Main Results:

  • Early fusion significantly improves the approximation of linear and angular camera velocities.
  • Auxiliary maps providing low and medium levels of detail yielded the best control results.
  • The combination of techniques demonstrated effectiveness in controlling robot movement between configurations.

Conclusions:

  • Early fusion is a beneficial strategy for enhancing visual servoing systems.
  • Image moments and segmentation-based maps provide valuable information for robot control.
  • Optimizing the level of detail in input data is crucial for achieving accurate robot movement control.