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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...

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Related Experiment Video

Updated: Jun 7, 2026

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

Real-time discriminative background subtraction.

Li Cheng¹, Minglun Gong, Dale Schuurmans

¹Bioinformatics Institute, A STAR, Singapore.

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

|October 21, 2010

Summary

This summary is machine-generated.

This study introduces a new algorithm for real-time video object segmentation, effectively handling changing backgrounds. The method achieves high-quality results comparable to offline techniques, running efficiently on graphics processing units (GPUs).

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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

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Last Updated: Jun 7, 2026

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

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Real-time video analysis presents challenges in segmenting foreground objects when background textures dynamically change.
Existing methods often struggle to adapt to temporal variations in background scenes.

Purpose of the Study:

To develop an adaptive background subtraction algorithm for live video segmentation.
To achieve high-quality object detection in dynamic environments with real-time performance.

Main Methods:

Formulated background subtraction as minimizing a penalized instantaneous risk functional, creating a local online discriminative algorithm.
Developed a global algorithm using Markov Random Field (MRF) with graph-cuts for pixel interactions.
Implemented algorithms on graphics processing units (GPUs) for parallel processing.

Main Results:

The proposed algorithm demonstrates rapid adaptation to temporal background changes.
Achieved real-time performance (≥ 75 fps) on mid-range GPUs.
Empirical studies show segmentation quality comparable to state-of-the-art offline methods.

Conclusions:

The developed approach offers a robust solution for real-time foreground object segmentation in videos with non-stationary backgrounds.
The GPU-accelerated implementation enables efficient video analysis suitable for various applications.