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

6.7K
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...
6.7K
Detection of Black Holes01:10

Detection of Black Holes

2.2K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.2K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.3K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.3K
Classification of Signals01:30

Classification of Signals

603
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
603

You might also read

Related Articles

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

Sort by
Same author

Robust Low-Complexity WMMSE Precoding Under Imperfect CSI with Per-Antenna Power Constraints.

Sensors (Basel, Switzerland)·2026
Same author

A Hybrid Low-Complexity WMMSE Precoder with Adaptive Damping for Massive Multi-User Multiple-Input Multiple- Output Systems.

Sensors (Basel, Switzerland)·2025
Same author

Tensor Based Semi-Blind Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiple-Input Multiple-Output Communication Systems.

Sensors (Basel, Switzerland)·2024
Same author

Channel Estimation for RIS-Assisted MIMO Systems in Millimeter Wave Communications.

Sensors (Basel, Switzerland)·2023
Same author

A Fast Circle Detection Algorithm Based on Information Compression.

Sensors (Basel, Switzerland)·2022
Same author

Accelerated PARAFAC-Based Channel Estimation for Reconfigurable Intelligent Surface-Assisted MISO Systems.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: Aug 7, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K

An Anti-Noise Fast Circle Detection Method Using Five-Quadrant Segmentation.

Yun Ou1, Honggui Deng1, Yang Liu1

  • 1School of Physics and Electronics, Central South University, Lushan South Road, Changsha 410083, China.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel anti-noise, fast circle detection algorithm for computer vision. The new method significantly improves noise resistance and computational speed compared to existing algorithms.

Keywords:
anti-noisecircle detectionfive-quadrant segmentation

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
Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.6K

Related Experiment Videos

Last Updated: Aug 7, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K
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
Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.6K

Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Circle detection is a fundamental computer vision task.
  • Existing algorithms suffer from poor noise resistance and slow computation.

Purpose of the Study:

  • To develop an anti-noise and fast circle detection algorithm.
  • To enhance the performance of circle detection in noisy environments.

Main Methods:

  • Applied curve thinning and connection post-edge extraction.
  • Utilized directional filtering for circular arc extraction, suppressing noise.
  • Implemented a novel five-quadrant circle fitting algorithm with a 'divide and conquer' approach.

Main Results:

  • The proposed algorithm demonstrates superior performance in noisy conditions.
  • Achieved faster computation speeds compared to RCD, CACD, WANG, and AS.
  • Validated on two open datasets, showing best performance under noise while maintaining speed.

Conclusions:

  • The developed algorithm offers a robust solution for circle detection in challenging, noisy images.
  • It provides a significant advancement in both accuracy and efficiency for computer vision applications.