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 Videos

Gradient vector flow fast geometric active contours.

Nikos Paragios1, Olivier Mellina-Gottardo, Visvanathan Ramesh

  • 1Siemens Corporate Research, Real-Time Vision and Modeling Department, 755 College Road East, Princeton, NJ 08540, USA. nikos@scr.siemens.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 21, 2004
PubMed
Summary
This summary is machine-generated.

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

Metabolic determinants of cancer immunotherapy outcomes identified by plasma profiling.

Nature medicine·2026
Same author

Evaluation of Two Commercial Artificial Intelligence Segmentation Systems for Radiation Therapy.

Journal of medical physics·2026
Same author

Two Projections Suffice for Cerebral Vascular Reconstruction.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2025
Same author

Advances in Artificial Intelligence for Glioblastoma Radiotherapy Planning and Treatment.

Cancers·2025
Same author

Deep learning detection of acute and sub-acute lesion activity from single-timepoint conventional brain MRI in multiple sclerosis.

Medical image analysis·2025
Same author

Quantitative Evaluation of a Fully Automated Planning Solution for Prostate-Only and Whole-Pelvic Radiotherapy.

Cancers·2024
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

This study introduces an edge-driven bidirectional geometric flow for accurate boundary extraction. The novel method effectively handles complex shapes and topological changes in images.

Area of Science:

  • Computer Vision
  • Image Processing
  • Geometric Modeling

Background:

  • Boundary extraction is crucial for image analysis.
  • Existing methods struggle with topological changes and complex deformations.
  • Active contour models, like snakes, are widely used but have limitations.

Purpose of the Study:

  • To propose an edge-driven bidirectional geometric flow for robust boundary extraction.
  • To combine geodesic active contour flow with gradient vector flow for enhanced snake behavior.
  • To develop a method capable of handling topological changes and significant shape deformations.

Main Methods:

  • Utilized a level set formulation for the geometric flow.
  • Integrated geodesic active contour flow with gradient vector flow external force.

Related Experiment Videos

  • Implemented an efficient numerical schema for the flow.

    • Main Results:

    • The proposed flow demonstrated robust behavior and fast convergence.
    • Successfully handled topological changes and significant shape deformations.
    • Achieved promising boundary extraction results on both real and synthetic images.

    Conclusions:

    • The edge-driven bidirectional geometric flow is a powerful tool for boundary extraction.
    • The method shows significant potential for various image analysis applications.
    • The approach offers an efficient and robust solution for complex image segmentation tasks.