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

Comprehensive Review of Open-Source Fundus Image Databases for Diabetic Retinopathy Diagnosis.

Sensors (Basel, Switzerland)·2025
Same author

Predicting Response to [177Lu]Lu-PSMA Therapy in mCRPC Using Machine Learning.

Journal of personalized medicine·2024
Same author

Preprocessing of Iris Images for BSIF-Based Biometric Systems: Binary Detected Edges and Iris Unwrapping.

Sensors (Basel, Switzerland)·2024
Same author

Human Tracking in Top-View Fisheye Images: Analysis of Familiar Similarity Measures via HOG and against Various Color Spaces.

Journal of imaging·2022
Same author

Seamless Copy-Move Replication in Digital Images.

Journal of imaging·2022

Related Experiment Video

Updated: Jul 12, 2025

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

Revisiting Mehrotra and Nichani's Corner Detection Method for Improvement with Truncated Anisotropic Gaussian

Baptiste Magnier1, Khizar Hayat2

  • 1Euromov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
Summary

This study enhances a corner detection algorithm using a novel approach. The improved method demonstrates superior efficiency and reliability compared to existing techniques, offering a valuable tool for computer vision.

Keywords:
anisotropic Gaussiancorner detectionfirst derivative of the Gaussianhalf edgesoriented Gaussiantruncated Gaussian

More Related Videos

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.6K
Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
07:03

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

7.7K

Related Experiment Videos

Last Updated: Jul 12, 2025

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
Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.6K
Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
07:03

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

7.7K

Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • The Mehrotra and Nichani corner detection method (early 1990s) showed promise but lacked reliability.
  • Its core concepts involved the half-edge concept and directional truncated first derivative of Gaussian.

Purpose of the Study:

  • To comprehensively assess an enhanced corner detection algorithm.
  • To evaluate its strengths, limitations, and overall effectiveness through qualitative and quantitative analysis.

Main Methods:

  • Developed an enhanced corner detection algorithm building upon the Mehrotra and Nichani method.
  • Conducted experiments on both synthetic and real images.
  • Performed qualitative and quantitative evaluations, including visual examples.

Main Results:

  • The enhanced algorithm demonstrated improved efficiency and reliability.
  • Experimental assessments showed the refined method outperforms established benchmark techniques.
  • The algorithm proved effective on both synthetic and real-world image data.

Conclusions:

  • The modifications have significantly improved the corner detection method's performance.
  • The enhanced algorithm offers a robust and easily implementable solution for the computer vision community.
  • This refined approach provides valuable insights for robust corner detection strategies.