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

Localization of shapes using statistical models and stochastic optimization.

Francois Destrempes1, Max Mignotte, Jean-Francois Angers

  • 1DIRO, Université de Montréal, Montreal, Canada. destremp@iro.umontreal.ca

IEEE Transactions on Pattern Analysis and Machine Intelligence
|July 14, 2007
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

Comparison of four attenuation-compensation methods for backscatter coefficient estimation and characterization of focal liver lesions.

Physics in medicine and biology·2026
Same author

Theoretical Foundations of the Echo Envelope Statistical Modeling: A Tutorial.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025
Same author

Thoracolumbar Fascia Microstructure and Low Back Pain: Insights From Quantitative Ultrasound Homodyned K-Distribution Statistical Modeling.

Ultrasound in medicine & biology·2025
Same author

Enhancing Liver Nodule Visibility and Diagnostic Classification Using Ultrasound Local Attenuation Coefficient Slope Imaging.

Ultrasound in medicine & biology·2025
Same author

A Phantom-Free Approach for Estimating the Backscatter Coefficient of Aggregated Red Blood Cells Applied to COVID-19 Patients.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2024
Same author

Effect of rt-PA on Shear Wave Mechanical Assessment and Quantitative Ultrasound Properties of Blood Clot Kinetics In Vitro.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·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 a novel unsupervised statistical method for shape localization using probabilistic principal component analysis (PPCA) and a pseudo-likelihood model. The approach effectively handles shape variability and enhances object specificity for accurate image analysis.

Area of Science:

  • Computer Vision
  • Statistical Modeling
  • Image Analysis

Background:

  • Accurate shape localization in images is crucial for various applications.
  • Existing methods may struggle with high shape variability and require labeled data.
  • Statistical models offer a robust framework for image analysis tasks.

Purpose of the Study:

  • To develop a new unsupervised statistical model for shape deformation and localization.
  • To incorporate Probabilistic Principal Component Analysis (PPCA) for modeling shape prior distributions.
  • To enhance the model with object specificity criteria based on image color segmentation.

Main Methods:

  • A pseudo-likelihood model based on the statistical distribution of the gradient vector field.
  • Mixtures of PPCA to accommodate greater shape variability.

Related Experiment Videos

  • Integration of global/local object specificity criteria derived from preliminary color segmentation.
  • Minimization of a Gibbs field for shape localization.
  • Application of the Exploration/Selection (E/S) stochastic algorithm for optimal deformation.
  • Iterative Conditional Estimation (ICE) for gradient vector field parameter estimation.
  • Exploration/Selection/Estimation (ESE) procedure for color segmentation.

    • Main Results:

    • A novel unsupervised statistical method for shape localization.
    • The model demonstrates effectiveness in handling shapes with greater variability.
    • The inclusion of object specificity criteria improves localization accuracy.
    • The E/S algorithm successfully finds optimal deformations.

    Conclusions:

    • The proposed model offers a robust and unsupervised approach to shape localization.
    • The integration of PPCA and color segmentation enhances model performance.
    • This method advances statistical shape analysis techniques for image understanding.