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

Pinching-off of Coated Vesicles01:32

Pinching-off of Coated Vesicles

3.1K
Vesicle budding is orchestrated by distinct cytosolic proteins such as adaptor proteins, coat proteins, and GTPases. To initiate vesicle budding, membrane-bending proteins containing crescent-shaped BAR domains bind to the lipid heads in the bilayer and distort the membrane to form a protein-coated vesicle bud. Adaptors proteins such as AP2 for clathrin-coated vesicles can nucleate on the deformed membrane. Finally, coat proteins such as clathrin or COPI and COPII assemble into a coat forming...
3.1K
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

2.1K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
2.1K

You might also read

Related Articles

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

Sort by
Same author

Coupled Diffusion Posterior Sampling for Unsupervised Hyperspectral and Multispectral Images Fusion.

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

Toward Resolution Mismatching: Modality-Aware Feature-Aligned Network for Pan-Sharpening.

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

PolicyMamba: Localized Policy Attention With State Space Model for Land Cover Classification.

IEEE transactions on neural networks and learning systems·2025
Same author

Probing Synergistic High-Order Interaction for Multi-Modal Image Fusion.

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

Cyanobacterial blooms prediction in China's large hypereutrophic lakes based on MODIS observations and Bayesian theory.

Journal of hazardous materials·2024
Same author

Artificial intelligence for geoscience: Progress, challenges, and perspectives.

Innovation (Cambridge (Mass.))·2024

Related Experiment Video

Updated: Apr 27, 2026

Super-resolution Imaging of Neuronal Dense-core Vesicles
09:30

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

9.2K

Blurred palmprint recognition based on stable-feature extraction using a Vese-Osher decomposition model.

Danfeng Hong1, Jian Su2, Qinggen Hong3

  • 1College of Information Engineering, Qingdao University, Qingdao, China.

Plos One
|July 4, 2014
PubMed
Summary

This study introduces a stable-feature extraction method using Vese-Osher (VO) decomposition to effectively recognize blurred palmprints. The proposed approach demonstrates superior performance and speed for blurred palmprint recognition systems.

More Related Videos

Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation BiFC-PALM
12:42

Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation BiFC-PALM

Published on: December 22, 2015

9.4K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

1.5K

Related Experiment Videos

Last Updated: Apr 27, 2026

Super-resolution Imaging of Neuronal Dense-core Vesicles
09:30

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

9.2K
Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation BiFC-PALM
12:42

Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation BiFC-PALM

Published on: December 22, 2015

9.4K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

1.5K

Area of Science:

  • Biometrics
  • Computer Vision
  • Image Processing

Background:

  • Non-contact palmprint capture often results in image blur due to defocus.
  • Image blur significantly degrades the performance of palmprint recognition systems.
  • Effective recognition of blurred palmprints is crucial for secure biometric identification.

Purpose of the Study:

  • To propose a stable-feature extraction method for effective blurred palmprint recognition.
  • To address the performance degradation caused by image blur in palmprint systems.
  • To develop a robust and efficient blurred palmprint recognition technique.

Main Methods:

  • Simulated palmprint blur using a Gaussian defocus degradation model.
  • Applied Vese-Osher (VO) decomposition to separate blurred palmprint images into structure and texture layers.
  • Extracted stable features from the structure layer using a weighted robustness histogram of oriented gradients (WRHOG) algorithm.
  • Utilized normalized correlation coefficient for feature similarity measurement.

Main Results:

  • The structure layer derived from VO decomposition proved stable across varying blur degrees.
  • The WRHOG method effectively extracted stable features from the structure layer.
  • Experimental results on PolyU and Blurred-PolyU databases showed superior recognition performance with an equal error rate of 0.132%.
  • Achieved authentication times under 1.3 seconds, meeting real-time requirements.

Conclusions:

  • The proposed method offers a stable and effective solution for blurred palmprint recognition.
  • The Vese-Osher decomposition and WRHOG algorithm are robust for handling image blur.
  • The method demonstrates high accuracy and efficiency, making it suitable for practical biometric applications.