Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.7K
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...
8.7K

You might also read

Related Articles

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

Sort by
Same author

Pixel-level understanding of a world in motion within a neural encoding framework.

Scientific reports·2026
Same author

Comment on: Predictors of prognosis and overall survival in pediatric mucoepidermoid carcinoma of salivary glands.

Critical reviews in oncology/hematology·2025
Same author

Optimizing elderly care: A data-driven AI model for predicting polypharmacy risk in the elderly using SHARE data.

Neuroscience·2025
Same author

High-level visual processing in the lateral geniculate nucleus revealed using goal-driven deep learning.

Journal of neuroscience methods·2025
Same author

Toward calibration-free motor imagery brain-computer interfaces: a VGG-based convolutional neural network and WGAN approach.

Journal of neural engineering·2024
Same author

On the role of generative artificial intelligence in the development of brain-computer interfaces.

BMC biomedical engineering·2024

Related Experiment Video

Updated: Mar 6, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K

Moving object detection and background enhancement for thalamic visual prostheses.

Hossam H Abolfotuh, Amr Jawwad, Bassem Abdullah

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 9, 2017
    PubMed
    Summary

    This study presents a novel image processing method to enhance visual perception for individuals using visual prostheses. The new technique improves image resolution and perceived visual quality, offering hope for restoring functional vision.

    More Related Videos

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
    07:11

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

    Published on: December 8, 2023

    2.4K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.6K

    Related Experiment Videos

    Last Updated: Mar 6, 2026

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    1.2K
    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
    07:11

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

    Published on: December 8, 2023

    2.4K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.6K

    Area of Science:

    • Biomedical Engineering
    • Computer Vision
    • Neuroscience

    Background:

    • Visual prostheses aim to restore sight for the blind.
    • Limited electrode count in current prostheses restricts image resolution.
    • Enhancing key visual features is crucial for improved perception.

    Purpose of the Study:

    • To introduce an image processing method for enhancing visual features.
    • To improve image resolution for prosthetic vision simulation.
    • To compare the proposed method against existing strategies.

    Main Methods:

    • Developed an image processing technique to enhance contrast, motion, and edges.
    • Reduced image size to an activity matrix for electrode stimulation.
    • Simulated prosthetic vision to compare image quality and perception.

    Main Results:

    • The proposed method significantly enhanced contrast, motion, and edge detection.
    • The activity matrix effectively represented salient visual information.
    • Simulations showed superior image quality and perceived visual fidelity compared to other methods.

    Conclusions:

    • The developed image processing method shows significant promise for improving visual prosthesis efficacy.
    • Enhancing key visual features and optimizing data for electrode arrays can lead to better visual restoration.
    • This approach offers a pathway to more effective visual prostheses for the blind.