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Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Assessing craniofacial growth and form without landmarks: A new automatic approach based on spectral methods.

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Updated: Aug 15, 2025

Functional Mapping with Simultaneous MEG and EEG
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Non-Isometric Shape Matching via Functional Maps on Landmark-Adapted Bases.

Mikhail Panine1,2, Maxime Kirgo2,3, Maks Ovsjanikov2

  • 1Università della Svizzera italiana Lugano Switzerland.

Computer Graphics Forum : Journal of the European Association for Computer Graphics
|January 6, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for non-rigid shape matching that precisely preserves landmarks. The approach uses near-conformal maps and a novel basis, achieving state-of-the-art results on non-isometric shapes.

Keywords:
functional mapslandmark‐based correspondenceshape matching

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Area of Science:

  • Computer Vision and Graphics
  • Computational Geometry
  • Shape Analysis

Background:

  • Non-rigid shape matching is crucial for many computer graphics and vision tasks.
  • Existing methods often struggle with non-isometric shapes or exact landmark preservation.
  • The functional map framework provides a powerful representation for shape correspondence.

Purpose of the Study:

  • To develop a principled approach for non-isometric landmark-preserving non-rigid shape matching.
  • To introduce a descriptor-free and efficient method robust to mesh variability.
  • To achieve state-of-the-art performance on challenging shape matching benchmarks.

Main Methods:

  • Utilizes the functional map framework, focusing on near-conformal maps instead of isometries.
  • Introduces a novel landmark-adapted basis derived from an intrinsic Dirichlet-Steklov eigenproblem.
  • Formulates a conformally-invariant energy optimized using an extended ZoomOut method.

Main Results:

  • The proposed method achieves exact landmark preservation.
  • Demonstrates state-of-the-art performance on non-isometric shape matching benchmarks.
  • Shows near state-of-the-art performance on isometric benchmarks.

Conclusions:

  • The novel approach effectively addresses landmark-preserving non-rigid shape matching.
  • The method is efficient, robust, and performs competitively across various datasets.
  • This work advances the capabilities of the functional map framework for complex shape analysis.