Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Methods of Obtaining Topography

Methods of Obtaining Topography

Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

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...

You might also read

Related Articles

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

Sort by

Same author

LDSNet: A Lightweight Detail-Sensitive Network for Small Object Detection in Low-Altitude UAV Scenarios.

Journal of imaging·2026

Same author

Unified Multi-Modal Object Tracking Through Spatial-Temporal Propagation and Modality Synergy.

Journal of imaging·2025

Same author

Deep learning-based debris flow hazard detection and recognition system: a case study.

Scientific reports·2025

Same author

Ion-Migration Mechanism: An Overall Understanding of Anionic Redox Activity in Metal Oxide Cathodes of Li/Na-Ion Batteries.

Advanced materials (Deerfield Beach, Fla.)·2022

Same author

Self-supervised knowledge distillation for complementary label learning.

Neural networks : the official journal of the International Neural Network Society·2022

Same author

HECTD3 regulates the tumourigenesis of glioblastoma by polyubiquitinating PARP1 and activating EGFR signalling pathway.

British journal of cancer·2022

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 15, 2025

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

Single-Pixel Background Modeling Algorithm for Strong Sky Scenes Based on Local Region Spatial Bases.

Biao Li¹, Zhiyong Xu^2,3, Jianlin Zhang^2,3

¹School of Computer Sciences and Engineering, Guilin University of Aerospace Technology, Jinji Road, Guilin 541004, China.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This summary is machine-generated.

This study introduces Single Pixel Background modeling (SPB) for dim-small target detection. SPB effectively suppresses background noise, enhancing target visibility even in low signal-to-noise ratio conditions.

Keywords:

background modeling dim-small target extremely low signal-to-noise ratio strong random vignetting strong sky scene

More Related Videos

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Super-Resolution Imaging of Bacterial Secreted Proteins Using Genetic Code Expansion

Super-Resolution Imaging of Bacterial Secreted Proteins Using Genetic Code Expansion

Published on: February 10, 2023

Related Experiment Videos

Last Updated: Aug 15, 2025

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Published on: May 10, 2020

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Super-Resolution Imaging of Bacterial Secreted Proteins Using Genetic Code Expansion

Super-Resolution Imaging of Bacterial Secreted Proteins Using Genetic Code Expansion

Published on: February 10, 2023

Area of Science:

Computer Vision
Signal Processing
Image Analysis

Background:

Background suppression is crucial for dim-small target detection.
Existing methods struggle with low signal-to-noise ratios and complex backgrounds.

Purpose of the Study:

To develop a novel background modeling algorithm for enhanced dim-small target detection.
To improve target extraction stability and accuracy in challenging conditions.

Main Methods:

Introduced Single Pixel Background modeling (SPB).
Constructs pixel-specific background base functions using local background information.
Optimally estimates the background of each pixel using these bases.

Main Results:

SPB effectively separates targets from undulating sky backgrounds at SNR < 1.5 dB.
Achieved stable and enhanced target detection amidst complex motion.
Difference images show white noise residuals and significantly enhanced targets.

Conclusions:

SPB demonstrates superior performance compared to five other algorithms for low SNR target detection.
The algorithm provides a robust solution for dim-small target detection challenges.