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

Deconvolution01:20

Deconvolution

240
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
240

You might also read

Related Articles

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

Sort by
Same author

Operating room nurses' lived experiences in the management of critical multiple and combined trauma: a qualitative study.

Frontiers in medicine·2026
Same author

Quality of life and self-regulatory fatigue in patients with type 2 diabetes mellitus: the mediating effects of personal mastery and health-promoting behaviors.

Frontiers in medicine·2026
Same author

tRNA m<sup>1</sup>A modification ensures HSPC production via modulating Nrf1 translation in zebrafish.

EMBO reports·2026
Same author

The mediating roles of personal mastery and health-promoting behaviors in the relationship between self-regulatory fatigue and quality of life among patients with type 2 diabetes mellitus.

PloS one·2026
Same author

RBPL-1 Promotes Meiotic Homolog Pairing Through Its Conserved DWNN Domain in Caenorhabditis elegans.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026
Same author

tRNA-m1A Modification Safeguards Fetal Liver HSPCs from DNA Damage via Maintaining Iron Homeostasis.

Blood·2026
Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
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
See all related articles

Related Experiment Video

Updated: Aug 31, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.8K

Antiocclusion Visual Tracking Algorithm Combining Fully Convolutional Siamese Network and Correlation Filtering.

Xiaomiao Tao1, Kaijun Wu1, Yongshun Wang1

  • 1School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.

Computational Intelligence and Neuroscience
|August 19, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a fully convolutional Siamese network for improved target tracking, overcoming limitations of traditional machine learning methods. The enhanced algorithm achieves faster speeds and greater accuracy, even with significant target changes and interference.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K
VisioTracker, an Innovative Automated Approach to Oculomotor Analysis
05:51

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

Published on: October 12, 2011

11.2K

Related Experiment Videos

Last Updated: Aug 31, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.8K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

2.9K
VisioTracker, an Innovative Automated Approach to Oculomotor Analysis
05:51

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

Published on: October 12, 2011

11.2K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Traditional machine learning for target tracking relies on single-channel grayscale features, leading to poor performance with significant target changes or interference.
  • Existing methods struggle with variations in illumination, scale, fast motion, and in-plane rotation (IPR).
  • The need for robust and efficient target tracking algorithms is critical in various applications.

Purpose of the Study:

  • To develop an advanced target tracking algorithm that addresses the limitations of conventional machine learning approaches.
  • To enhance tracking accuracy and speed by utilizing a fully convolutional Siamese network.
  • To improve robustness against target variations and environmental interference.

Main Methods:

  • Employed a fully convolutional Siamese network to learn a similarity measurement function.
  • Utilized offline pre-training without online updates for faster tracking speeds.
  • Incorporated a sample adaptive update model to enhance training sample reliability and analyzed Hessian matrix calculations for parameter tuning (Struck function, Bike3, Boat5).

Main Results:

  • The correlation filtering-based target tracking algorithm demonstrated strong performance in accuracy and speed, maintaining tracking during simultaneous illumination and scale changes, fast motion, and IPR.
  • Parameter adjustments in the Struck function (Bike3, Boat5) were found to enhance tracking speed and system stability against interference.
  • The SiamVGG algorithm, using MeanShift vectors, achieved a 53.1% accuracy increase, a 31.8% robustness decrease, and a 28.6% error reduction.

Conclusions:

  • Fully convolutional Siamese networks offer a significant improvement over traditional methods for target tracking, particularly in challenging conditions.
  • The proposed adaptive update model and parameter tuning strategies enhance the reliability and stability of tracking algorithms.
  • The SiamVGG algorithm shows promising results in improving tracking accuracy and reducing errors through feature-based vector calculations.