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

Isoperimetric graph partitioning for image segmentation.

Leo Grady1, Eric L Schwartz

  • 1Department of Imaging and Visualization, Siemens Corporate Research, Princeton, NJ 08540, USA. leo.grady@siemens.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|March 11, 2006
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

Independent real-world application of a clinical-grade automated prostate cancer detection system.

The Journal of pathology·2021
Same author

Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc·2020
Same author

Precise measurement of coronary stenosis diameter with CCTA using CT number calibration.

Medical physics·2019
Same author

Generation of Patient-Specific Cardiac Vascular Networks: A Hybrid Image-Based and Synthetic Geometric Model.

IEEE transactions on bio-medical engineering·2018
Same author

Lumen boundaries extracted from coronary computed tomography angiography on computed fractional flow reserve (FFRCT): validation with optical coherence tomography.

EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology·2018
Same author

Automated estimation of image quality for coronary computed tomographic angiography using machine learning.

European radiology·2018
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

We present a new image segmentation method that finds partitions with a small isoperimetric constant. This approach offers high-quality segmentations, improving speed and stability over spectral methods.

Area of Science:

  • Computer Vision
  • Image Processing
  • Graph Theory

Background:

  • Spectral graph partitioning is a common technique for image segmentation.
  • Existing spectral methods rely on eigenvector computations, which can be slow and unstable.

Purpose of the Study:

  • To introduce an alternative image segmentation approach.
  • To improve the speed and stability of spectral graph partitioning methods.

Main Methods:

  • The proposed method finds graph partitions with a small isoperimetric constant.
  • This involves solving a linear system instead of an eigenvector problem.

Main Results:

  • The new approach achieves high-quality image segmentations comparable to spectral methods.

Related Experiment Videos

  • It demonstrates improved computational speed and numerical stability.

    • Conclusions:

    • The proposed method offers a faster and more stable alternative for image segmentation.
    • Solving linear systems for isoperimetric partitioning is a viable strategy for high-quality image segmentation.