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

<i>In vitro</i> study of ethanol production, ethanol tolerance, and antimicrobial susceptibility of gut microbes associated with liver diseases.

Gut microbes reports·2026

Same author

A (poly)Pro tip for preserving native disulfide connectivity during thiol-maleimide bioconjugation of disulfide-rich peptides.

Chemical science·2026

Same author

Six new <i>Bartonella</i> species isolated from bats in Senegal.

International journal of systematic and evolutionary microbiology·2026

Same author

Mixed genetic background better recapitulates developmental and psychiatric phenotypes and heterogeneity than inbred C57BL/6J mice.

Scientific reports·2025

Same author

Politics, Not Science Revisited: The Harms of Continuing to Ask About Gender Identity and Biological Sex.

Archives of sexual behavior·2025

Same author

Antibacterial activity of fungus comb extracts from Senegalese fungus-farming termites.

AMB Express·2025

Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 9, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Bayesian Contrast Measures and Clutter Distribution Determinants of Human Target Detection.

Ana Novak, Nicholas Armstrong, Terry Caelli

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|December 28, 2016

Summary

This summary is machine-generated.

A Bayesian approach reveals that a novel histogram contrast measure accurately predicts human target detection performance. Clutter significantly impairs detection only when it is contiguous with the target and shares similar features.

More Related Videos

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Mar 9, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

Visual perception research
Computational neuroscience
Image analysis

Background:

Human target detection is influenced by electro-optical factors, target characteristics, and contextual elements.
Understanding the interplay of these factors is crucial for improving detection systems.
Previous models often simplify contrast and clutter effects.

Purpose of the Study:

To investigate human target detection using a Bayesian framework.
To develop and compare different contrast measures, including a novel Bayesian-based histogram contrast statistic.
To analyze the impact of clutter on target detection under various conditions.

Main Methods:

A Bayesian approach was employed to model human target detection.
Three contrast formulations were developed and compared: mean contrast, perceptual contrast, and Bayesian histogram contrast.
Detection data was analyzed to assess the correlation between contrast measures and human performance, considering target size and clutter interactions.

Main Results:

The Bayesian histogram contrast statistic demonstrated a strong correlation with human detection performance, outperforming other measures.
Clutter effects were minimal for large targets and when clutter was not contiguous with the target.
Detection performance decreased significantly when targets were contiguous with similar-feature clutter, creating a "clutter camouflage" effect.

Conclusions:

A Bayesian formulation incorporating contrast histogram statistics and image context provides a robust model for human target detection.
Human observers appear to adapt their detection criteria based on image set, context, and task demands.
The developed contrast measure and clutter analysis offer insights for designing more effective visual search and detection systems.