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

Generalized multidimensional scaling: a framework for isometry-invariant partial surface matching.

Alexander M Bronstein1, Michael M Bronstein, Ron Kimmel

  • 1Department of Computer Science, Technion Israel Institute of Technology, Haifa 32000, Israel.

Proceedings of the National Academy of Sciences of the United States of America
|January 25, 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

The intermediate-risk gap in AI-based breast cancer stratification - Authors' reply.

The Lancet. Oncology·2026
Same author

Prediction of OncotypeDX recurrence score using hematoxylin and eosin-stained whole slide images.

NPJ breast cancer·2026
Same author

Description of a collaborative sperm whale birth and shifts in coda vocal styles during key events.

Scientific reports·2026
Same author

Deep learning on histopathological images to predict breast cancer recurrence risk and chemotherapy benefit: a multicentre, model development and validation study.

The Lancet. Oncology·2026
Same author

Deep Learning on Histopathological Images to Predict Breast Cancer Recurrence Risk and Chemotherapy Benefit.

medRxiv : the preprint server for health sciences·2025
Same author

Prediction of B/T Subtype and ETV6-RUNX1 Translocation in Pediatric Acute Lymphoblastic Leukemia by Deep Learning Analysis of Giemsa-Stained Whole Slide Images of Bone Marrow Aspirates.

Pediatric blood & cancer·2025
Same journal

Tau protein as a regulator of mitochondrial function and dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A scalable, dividing cell model for the robust propagation and quantification of human sporadic Creutzfeldt-Jakob disease prions.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Epigenetic regulation of mesenchymal BMP signaling directs postnatal organ innervation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Single-shot wide-field biochemical imaging at 1 kHz frame rate.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Morphogenesis and topological evolution of a frustrated nematic liquid crystal under confinement.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

B cell-intrinsic CXCR3 drives efficient generation of ectopic pulmonary germinal center responses to influenza A virus infection.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

This study presents an efficient algorithm for matching 3D surfaces, focusing on minimizing distortion for accurate comparisons. The method enables expression-invariant 3D face recognition by mapping surface features reliably.

Area of Science:

  • Computer Vision
  • Geometric Processing
  • Machine Learning

Background:

  • Surface matching is crucial for 3D analysis.
  • Isometry-invariant methods are needed for robust comparisons.
  • Existing methods may lack computational efficiency or flexibility.

Purpose of the Study:

  • To introduce an efficient algorithm for isometry-invariant surface matching.
  • To enable minimum-distortion mapping between surfaces.
  • To apply the algorithm for expression-invariant 3D face recognition.

Main Methods:

  • Developed generalized multidimensional scaling (GMDS).
  • GMDS is a continuous optimization algorithm for embedding surfaces.
  • Algorithm supports full and partial surface matching.

Related Experiment Videos

Main Results:

  • Demonstrated efficient computation of minimum-distortion mappings.
  • Successfully applied GMDS to 3D face recognition.
  • Achieved expression-invariant matching of 3D facial surfaces.

Conclusions:

  • GMDS provides an efficient solution for isometry-invariant surface matching.
  • The algorithm is versatile for both full and partial matching tasks.
  • This approach advances 3D face recognition capabilities.