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

Polarimetric image segmentation via maximum-likelihood approximation and efficient multiphase level-sets.

Ismail Ben Ayed1, Amar Mitiche, Ziad Belhadj

  • 1Institut National de la Recherche Scientifique, INRS-EMT 800, de La Gauchetière Ouest, Montréal, QC, H5A 1K6, Canada. benayedi@emt.inrs.ca

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 26, 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

Deep Learning Models Connecting Images and Text: A Primer for Radiologists.

Radiographics : a review publication of the Radiological Society of North America, Inc·2025
Same author

Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation.

Medical image analysis·2025
Same author

Neighbor-aware calibration of segmentation networks with penalty-based constraints.

Medical image analysis·2025
Same author

A Foundation Language-Image Model of the Retina (FLAIR): encoding expert knowledge in text supervision.

Medical image analysis·2024
Same author

Domain adaptation for EEG-based, cross-subject epileptic seizure prediction.

Frontiers in neuroinformatics·2024
Same author

Do we really need dice? The hidden region-size biases of segmentation losses.

Medical image analysis·2023
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

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

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

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

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

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

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

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

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

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

Achieving Text-based Person Retrieval with Any Granularity.

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

This study presents a novel level set method for segmenting complex polarimetric images. The approach efficiently partitions image domains using a multiphase method, validated on synthetic and real-world data.

Area of Science:

  • Image Processing
  • Computer Vision
  • Computational Mathematics

Background:

  • Polarimetric imaging generates complex data requiring advanced segmentation techniques.
  • Existing segmentation methods may struggle with the unique characteristics of polarimetric imagery.
  • Accurate image segmentation is crucial for various applications, including remote sensing and medical imaging.

Purpose of the Study:

  • To develop and evaluate a new level set method for segmenting complex polarimetric images.
  • To introduce a multiphase approach that efficiently handles image domain partitioning.
  • To ensure robust segmentation through the integration of image observation terms and boundary priors.

Main Methods:

  • A level set framework is employed for image segmentation.

Related Experiment Videos

  • The method minimizes a functional incorporating maximum-likelihood approximation for complex Wishart/Gaussian image models.
  • A novel multiphase technique embeds partition constraints within curve evolution for guaranteed domain partitioning.

    • Main Results:

    • The proposed level set method demonstrates effective segmentation of complex polarimetric images.
    • Successful application is shown on both synthetic and real-world image datasets.
    • Quantitative evaluations confirm the method's performance and provide comparative analysis.

    Conclusions:

    • The developed level set method offers an efficient and robust solution for polarimetric image segmentation.
    • The multiphase approach ensures reliable image domain partitioning from arbitrary initial configurations.
    • The study validates the effectiveness of the proposed technique through comprehensive testing and comparison.