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

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

Theoretical Analysis, Neural Network-Based Inverse Design, and Experimental Verification of Multilayer Thin-Plate Acoustic Metamaterial Unit Cells.

Materials (Basel, Switzerland)·2026

Same author

Topology Design of Soft Phononic Crystals for Tunable Band Gaps: A Deep Learning Approach.

Materials (Basel, Switzerland)·2025

Same author

Interface stress transfer model and modulus parameter equivalence method for composite materials embedded with tensile pre-strain shape memory alloy fibers.

PloS one·2024

Same author

A Cylindrical Near-Field Acoustical Holography Method Based on Cylindrical Translation Window Expansion and an Autoencoder Stacked with 3D-CNN Layers.

Sensors (Basel, Switzerland)·2023

Same author

Broadband Sound Insulation and Dual Equivalent Negative Properties of Acoustic Metamaterial with Distributed Piezoelectric Resonators.

Materials (Basel, Switzerland)·2022

Same author

Acoustic Insulation Mechanism of Membrane-Type Acoustic Metamaterials Loaded with Arbitrarily Shaped Mass Blocks of Variable Surface Density.

Materials (Basel, Switzerland)·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: Jul 12, 2025

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

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

Published on: August 30, 2013

Loop Closure Detection Method Based on Similarity Differences between Image Blocks.

Yizhe Huang^1,2,3, Bin Huang¹, Zhifu Zhang⁴

¹Hubei Key Laboratory of Modern Manufacturing Quality Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China.

Sensors (Basel, Switzerland)

|October 28, 2023

Summary

This summary is machine-generated.

This study introduces a novel loop closure detection method for mobile robots, improving visual simultaneous localization and mapping (SLAM) accuracy. The technique enhances loop closure detection by analyzing image block similarities, achieving 100% recall rate.

Keywords:

convolutional neural network loop closure detection similarity difference visual simultaneous localization and mapping

More Related Videos

Super-resolution Imaging of Neuronal Dense-core Vesicles

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Related Experiment Videos

Last Updated: Jul 12, 2025

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

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

Published on: August 30, 2013

Super-resolution Imaging of Neuronal Dense-core Vesicles

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Autonomous mobile robots rely on visual simultaneous localization and mapping (SLAM) for navigation.
Loop closure detection is critical in SLAM to correct accumulated errors and ensure map consistency.
Current deep learning methods for loop closure detection often overlook internal image similarities.

Purpose of the Study:

To propose a novel loop closure detection method for visual SLAM systems.
To enhance the accuracy and reliability of loop closure detection in mobile robots.
To address limitations in existing deep learning approaches by incorporating image block similarity analysis.

Main Methods:

Utilized a lightweight convolutional neural network (CNN) for extracting image descriptors.
Implemented a block similarity calculation module to re-evaluate image pair similarity.
Divided highly similar image pairs into blocks to analyze internal feature similarities.

Main Results:

The proposed method significantly outperforms existing loop closure detection techniques.
Achieved a 100% recall rate in loop closure detection accuracy.
Demonstrated the universality and effectiveness of the block similarity calculation module.

Conclusions:

The novel loop closure detection method based on image block similarity effectively improves SLAM system accuracy.
The block similarity calculation module enhances the identification of correct loop closures and reduces false positives.
The proposed approach offers a universally applicable solution for improving loop closure detection in various CNN models.