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 Experiment Video

Updated: Jun 21, 2026

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

A unified framework for gesture recognition and spatiotemporal gesture segmentation.

Jonathan Alon1, Vassilis Athitsos, Quan Yuan

  • 1Computer Science Department, Boston University, Boston, MA 02215, USA. jalon@cs.bu.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|July 4, 2009
PubMed
Summary

This study presents a unified framework for hand gesture recognition that simultaneously segments gestures in space and time, enabling recognition even with ambiguous hand locations or unknown start/end times in cluttered video.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Loss of KDM4B exacerbates bone-fat imbalance and mesenchymal stromal cell exhaustion in skeletal aging.

Cell stem cell·2021
Same author

Sex Effect on Cardiac Damage in Mice With Experimental Autoimmune Encephalomyelitis.

ASN neuro·2021
Same author

CTGF facilitates cell-cell communication in chondrocytes via PI3K/Akt signalling pathway.

Cell proliferation·2021
Same author

Challenges of Stem-cell-based Craniofacial Regeneration.

Current stem cell research & therapy·2021
Same author

An electroporation strategy to synthesize the membrane-coated nanoparticles for enhanced anti-inflammation therapy in bone infection.

Theranostics·2021
Same author

Investigation of membrane fouling mechanism of intracellular organic matter during ultrafiltration.

Scientific reports·2021

Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Spatiotemporal gesture segmentation is crucial for accurate hand gesture recognition.
  • Existing methods often require pre-segmented spatial or temporal data, limiting real-world applicability.
  • Handling gestures in continuous, unsegmented video streams with cluttered backgrounds remains a challenge.

Purpose of the Study:

  • To introduce a unified framework for simultaneous spatial segmentation, temporal segmentation, and recognition of hand gestures.
  • To develop a method robust to ambiguous hand locations and unknown gesture timing.
  • To enable gesture recognition in challenging, real-world scenarios like continuous image streams with cluttered backgrounds.

Main Methods:

  • A novel unified framework integrating bottom-up and top-down information flow.

Related Experiment Videos

Last Updated: Jun 21, 2026

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

  • A spatiotemporal matching algorithm handling multiple candidate hand detections per frame.
  • A classifier-based pruning framework for efficient rejection of incorrect gesture matches.
  • A subgesture reasoning algorithm to differentiate overlapping gesture models.

    • Main Results:

    • Demonstrated effective gesture recognition despite ambiguous hand locations and unknown temporal segmentation.
    • Successfully applied the method to continuous image streams with moving, cluttered backgrounds.
    • Achieved robust performance in recognizing hand-signed digits and retrieving signs from American Sign Language (ASL) video databases.

    Conclusions:

    • The proposed unified framework significantly advances hand gesture recognition capabilities.
    • The method offers a robust solution for unsegmented gesture recognition in complex environments.
    • This approach has broad implications for applications requiring natural human-computer interaction.