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Deformation of Member under Multiple Loadings

Deformation of Member under Multiple Loadings

When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...

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Related Experiment Video

Updated: May 15, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

A Multiple Object Geometric Deformable Model for Image Segmentation.

John A Bogovic¹, Jerry L Prince, Pierre-Louis Bazin

¹Johns Hopkins University, Baltimore, MD, USA.

Computer Vision and Image Understanding : CVIU

|January 15, 2013

Summary

This summary is machine-generated.

This study introduces a new deformable model for segmenting multiple objects in images, overcoming limitations of previous methods. The efficient framework ensures accurate object relationships and topology, improving scalability for complex medical imaging tasks.

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Area of Science:

Medical image analysis
Computer vision
Computational anatomy

Background:

Deformable models are standard for single object segmentation.
Existing multi-object segmentation methods have significant limitations.
Accurate segmentation of multiple objects is crucial for medical imaging analysis.

Purpose of the Study:

To present a novel and efficient deformable model for multi-object segmentation in 2D and 3D.
To address limitations in current multi-object segmentation techniques.
To enable accurate segmentation of objects with multiple compartments.

Main Methods:

A new object representation for deformable models.
A framework ensuring object relationships and topology.
Computationally efficient evolution scheme independent of object count.
Boundary-specific speeds and object-specific forces.

Main Results:

Guaranteed prevention of overlaps and gaps between segmented objects.
Enabled segmentation of objects with multiple compartments.
Demonstrated superior performance and scalability compared to previous approaches.

Conclusions:

The novel framework offers significant improvements for multi-object segmentation.
The method is highly effective for complex medical imaging tasks like organ parcellation.
This approach enhances the utility and scalability of deformable models for segmentation.