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Related Concept Videos

Three-Dimensional Analysis of Strain01:29

Three-Dimensional Analysis of Strain

Three-dimensional strain analysis is crucial for understanding how materials deform under stress, particularly in elastic, homogeneous materials. This method employs principal stress axes to simplify complex stress states into more understandable forms. Subjected to stress, a small cubic element within a material either expands or contracts along these axes, transforming into a rectangular parallelepiped. This transformation effectively illustrates the material's deformation. The principal...
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Related Experiment Video

Updated: Jun 2, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Statistical 3D Shape Analysis by Local Generative Descriptors.

Umberto Castellani, Marco Cristani, Vittorio Murino

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 18, 2011
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel surface representation using generative models and a multicircular Hidden Markov Model (MC-HMM) to encode 3D shape variations. The method effectively captures local geometric properties for various applications.

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    Three-Dimensional Shape Modeling and Analysis of Brain Structures
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    Published on: November 14, 2019

    Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
    08:59

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    Published on: October 28, 2018

    Area of Science:

    • Computer Vision
    • Geometric Modeling
    • Machine Learning

    Background:

    • Traditional surface representations struggle with capturing local geometric variations in 3D shapes.
    • Generative models offer powerful tools for encoding complex data distributions.

    Purpose of the Study:

    • To propose a new approach for 3D shape surface representation.
    • To leverage generative models for encoding local geometric properties.
    • To introduce a multicircular Hidden Markov Model (MC-HMM) for stochastic surface modeling.

    Main Methods:

    • Local geometric feature collection from 3D shape neighborhoods.
    • Parameter estimation for the proposed multicircular Hidden Markov Model (MC-HMM).
    • Modeling surfaces as stochastic processes using circular geodesic pathways.

    Main Results:

    • Demonstrated effectiveness across multiple application scenarios.
    • Achieved promising results in multiple view registration.
    • Successfully applied to matching deformable shapes and object recognition in cluttered scenes.

    Conclusions:

    • The proposed MC-HMM approach provides an effective method for surface representation.
    • Generative models show significant potential as geometric descriptors for 3D shapes.
    • The approach opens new avenues for diverse computer vision and geometric applications.