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

You might also read

Related Articles

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

Sort by
Same author

Joint effects of visual acuity impairment and visual field loss on reading performance: a low-vision simulation in Chinese readers.

Eye and vision (London, England)·2026
Same author

Prospects and hot spots for mammalian target of rapamycin in the field of neuroscience from 2002 to 2021.

Frontiers in integrative neuroscience·2022
Same author

Mechanism of magnetisation relaxation in {MIII2DyIII2} (M = Cr, Mn, Fe, Al) "Butterfly" complexes: how important are the transition metal ions here?

Chemical science·2019
Same author

Clinical outcomes of lumen-apposing metal stent in the management of benign gastrointestinal strictures: a systematic review and meta-analysis.

Scandinavian journal of gastroenterology·2019
Same author

MicroRNA-351 eases insulin resistance and liver gluconeogenesis via the PI3K/AKT pathway by inhibiting FLOT2 in mice of gestational diabetes mellitus.

Journal of cellular and molecular medicine·2019
Same author

Autoinducer-2 of Fusobacterium nucleatum promotes macrophage M1 polarization via TNFSF9/IL-1β signaling.

International immunopharmacology·2019
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Jul 12, 2025

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

ALGD-ORB: An improved image feature extraction algorithm with adaptive threshold and local gray difference.

Guoming Chu1, Yan Peng1, Xuhong Luo1

  • 1School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, China.

Plos One
|October 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces Adaptive Threshold and Local Gray Difference-ORB (ALGD-ORB) to improve visual SLAM. The new algorithm enhances feature point distribution and distinctiveness for more accurate autonomous navigation.

More Related Videos

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
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.9K

Related Experiment Videos

Last Updated: Jul 12, 2025

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
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
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

42.9K

Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Simultaneous Localization and Mapping (SLAM) is vital for autonomous navigation.
  • Current ORB algorithms struggle with feature point density, overlap, and distribution, causing errors.
  • Representative image features are essential for robust SLAM performance.

Purpose of the Study:

  • To develop an improved image feature extraction algorithm for visual SLAM.
  • To address the limitations of traditional ORB algorithms, including feature point redundancy and imbalance.
  • To enhance the accuracy and reliability of autonomous navigation systems.

Main Methods:

  • Introduced Adaptive Threshold and Local Gray Difference-ORB (ALGD-ORB).
  • Employed an adaptive threshold for enhanced feature point detection.
  • Utilized an improved quadtree method for feature point distribution homogenization.
  • Combined gray size and gray difference for feature descriptor enhancement.

Main Results:

  • ALGD-ORB significantly improved feature point distribution uniformity.
  • The algorithm maintained high accuracy and real-time performance.
  • Enhanced feature descriptor distinctiveness and reduced mismatches/redundancies.

Conclusions:

  • ALGD-ORB offers a superior approach to feature extraction in visual SLAM.
  • The proposed method addresses key limitations of traditional ORB algorithms.
  • ALGD-ORB contributes to more robust and reliable autonomous navigation systems.