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

Classifier-driven generative adversarial networks for enhanced antimicrobial peptide design.

Briefings in bioinformatics·2025

Same author

De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks.

International journal of molecular sciences·2024

Same author

A Generative Neighborhood-Based Deep Autoencoder for Robust Imbalanced Classification.

IEEE transactions on artificial intelligence·2024

Same author

Embedded Temporal Convolutional Networks for Essential Climate Variables Forecasting.

Sensors (Basel, Switzerland)·2022

Same author

Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks.

Journal of imaging·2021

Same author

Quantifying the Computational Efficiency of Compressive Sensing in Smart Water Network Infrastructures.

Sensors (Basel, Switzerland)·2020

Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026

Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026

Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026

Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026

Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026

Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 5, 2026

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Recurrence Quantification Analysis for Scene Change Detection and Foreground/Background Segmentation in Videos.

Theodora Kyprianidi¹, Effrosyni Doutsi¹, Panagiotis Tsakalides^1,2

¹Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.

Journal of Imaging

|April 25, 2025

Summary

This summary is machine-generated.

Recurrence Quantification Analysis (RQA) offers an efficient method for video processing tasks like scene change detection and foreground segmentation. This technique provides robust and computationally light alternatives to deep learning models.

Keywords:

dynamic video processing foreground/background segmentation recurrence quantification analysis (RQA)scene change detection video analysis

More Related Videos

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

Related Experiment Videos

Last Updated: May 5, 2026

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

Area of Science:

Computer Vision
Dynamic Systems Analysis
Signal Processing

Background:

Recurrence Quantification Analysis (RQA) is a method for analyzing dynamic systems by examining state recurrence.
Traditional deep learning methods for video analysis require substantial data and computational resources.
RQA offers a potential alternative for dynamic video processing tasks.

Purpose of the Study:

To present the mathematical framework of RQA for dynamic video processing.
To explore RQA's application in scene change detection and foreground/background segmentation.
To evaluate RQA's computational efficiency and robustness compared to deep learning.

Main Methods:

Applying RQA to video streams by analyzing temporal dynamics of frames.
Utilizing RQA for scene change detection on Autoshot, RAI, and BBC Planet Earth datasets.
Employing RQA for foreground/background segmentation on UCF101 and DAVIS datasets.
Visualizing results using heatmaps and Recurrence Plots (RPs).

Main Results:

RQA effectively detects abrupt scene changes, achieving state-of-the-art comparable results.
RQA accurately segments foreground motion from static backgrounds.
Recurrence Plots (RPs) provide clear delineation of foreground objects.

Conclusions:

RQA is a computationally efficient, flexible, and robust approach for dynamic video processing.
RQA demonstrates significant potential for various video analysis applications.
RQA serves as a viable alternative to computationally intensive deep learning methods.