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

9.0K
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...
9.0K
Force Classification01:22

Force Classification

2.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.8K
Detection of Black Holes01:10

Detection of Black Holes

1.7K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Determination of non-volatile metabolic profiles and their sensory relevance in different grades of brandy through widely targeted metabolomics.

Food chemistry: X·2026
Same author

Atlas of predicted protein complex structures across kingdoms.

Nature communications·2026
Same author

The Clinical Utility of Whole-Exome Sequencing in the Prenatal Diagnosis of Fetal Skeletal Dysplasia.

International journal of women's health·2026
Same author

Accurate Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers.

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

Study of Ultrasound Diagnosis of Acrania-Exencephaly-Anencephaly Sequence in Middle First Trimester: A Multicenter Center, Retrospective Analysis.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2025
Same author

Diffusion Models are Efficient Data Generators for Human Mesh Recovery.

IEEE transactions on pattern analysis and machine intelligence·2025
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

IEEE transactions on neural networks and learning systems·2026
Same journal

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Apr 30, 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

1.3K

Fast and robust object detection using asymmetric totally corrective boosting.

Peng Wang, Chunhua Shen, Nick Barnes

    IEEE Transactions on Neural Networks and Learning Systems
    |May 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    We introduce totally corrective asymmetric boosting algorithms for faster, more accurate real-time object detection. These methods optimize asymmetric loss and update classifiers efficiently, outperforming existing state-of-the-art detectors.

    Related Experiment Videos

    Last Updated: Apr 30, 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

    1.3K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Boosting-based object detection is a rapidly advancing field.
    • Existing frameworks like Viola-Jones use symmetric loss and stagewise optimization.

    Purpose of the Study:

    • To propose novel totally corrective asymmetric boosting algorithms for real-time object detection.
    • To improve upon the efficiency and performance of current boosting-based detection methods.

    Main Methods:

    • Explicitly optimizing asymmetric loss functions.
    • Utilizing totally corrective updates for weak classifier coefficients via Lagrange duals.
    • Employing column generation for optimization and enabling de-selection of irrelevant classifiers.

    Main Results:

    • Achieved superior detection performance compared to state-of-the-art object detectors.
    • Demonstrated improved efficiency with fewer weak classifiers in the ensemble.
    • Showcased simpler, non-heuristic training procedures compared to AsymBoost.

    Conclusions:

    • The proposed totally corrective asymmetric boosting algorithms offer significant improvements in object detection.
    • These algorithms provide a more efficient and effective approach to real-time object detection.
    • Experimental validation on face and pedestrian detection confirms the superiority of the proposed methods.