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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.4K
Association Areas of the Cortex01:21

Association Areas of the Cortex

5.5K
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,...
5.5K

You might also read

Related Articles

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

Sort by
Same author

AI-assisted 6G-IoT system for environmental monitoring and risk management in mining sites.

Scientific reports·2026
Same author

Activity of octyl gallate against drug-sensitive and buparvaquone-resistant Theileria annulata.

International journal for parasitology. Drugs and drug resistance·2026
Same author

Identification of three novel overlapping epitopes within the immunodominant region of ASFV p30 protein facilitates development of a competitive ELISA for antibody detection.

Virology·2026
Same author

CAMKV is an ISG and facilitates the degradation of rabies virus phosphoprotein via SQSTM1-mediated selective autophagy.

Autophagy·2026
Same author

Clinical Probation Teaching in Undergraduate Implant Dentistry Course: Online Versus Offline Modes.

European journal of dental education : official journal of the Association for Dental Education in Europe·2026
Same author

STAT3 Inhibitor Suppresses CCL2-dependent Recruitment and M2 Polarization of Tumor-associated Macrophages to Enhance Antitumor Immunity in Ovarian Cancer.

Applied biochemistry and biotechnology·2026

Related Experiment Video

Updated: Jul 16, 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

568

A Deep Recurrent Learning-Based Region-Focused Feature Detection for Enhanced Target Detection in Multi-Object Media.

Jinming Wang1, Ahmed Alshahir2, Ghulam Abbas3

  • 1College of Information Science & Technology, Zhejiang Shuren University, Hangzhou 310015, China.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary

This study introduces a region-focused feature detection (RFD) method for accurate target detection in challenging images. The RFD method enhances accuracy and reduces errors by analyzing image regions individually.

Keywords:
deep reinforcement learning (DRL)feature extractionhotspotimage analysismulti-objectregion of interesttarget detection

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

2.8K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

Related Experiment Videos

Last Updated: Jul 16, 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

568
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

2.8K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Target detection in high-contrast, multi-object scenarios is difficult due to varying pixel distributions and intensity properties.
  • Existing methods struggle with diverse visual attributes across different image areas.

Purpose of the Study:

  • To introduce a novel region-focused feature detection (RFD) method for improved target detection accuracy.
  • To address the challenges posed by varying contrast and intensity in complex images and videos.

Main Methods:

  • The proposed region-focused feature detection (RFD) method segments images into smaller regions for localized analysis.
  • Deep recurrent learning is employed to extract features based on similarity measures and region-specific attributes.
  • Features from overlapping regions are combined, and targets are compared to training data using contrast and intensity attributes for enhanced accuracy.

Main Results:

  • The RFD method demonstrated significant improvements: similarity index (+10.69%), extraction ratio (+9.04%), and precision (+13.27%).
  • The approach effectively reduced the false rate (-7.78%) and processing time (-9.19%).

Conclusions:

  • The region-focused feature detection (RFD) method offers a robust solution for accurate target detection in complex visual environments.
  • By analyzing distinct regions and filtering misleading features, the method enhances overall detection performance and efficiency.