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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Functional Classification of Joints01:09

Functional Classification of Joints

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
An immobile...
Transformation of Plane Strain01:12

Transformation of Plane Strain

When analyzing elongated structures like bars subjected to uniformly distributed loads, it is essential to understand the transformation of plane strain when coordinate axes are rotated. This transformation helps to assess how material deformation characteristics vary with orientation, which is crucial in materials science and structural engineering.
Under plane strain conditions, typical for members where one dimension significantly exceeds the others, deformations and resultant strains are...
Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
Introduction to Joints00:58

Introduction to Joints

The adult human body usually has 206 bones, and except for the hyoid bone in the neck, each bone is connected to at least one other bone. Joints are the location where bones come together. Many joints allow for movement between the bones. At these joints, the articulating surfaces of the adjacent bones can move smoothly against each other. However, the bones of other joints may be joined by connective tissue or cartilage. These joints are designed for stability and provide little or no movement.
Deformations in a Transverse Cross Section01:21

Deformations in a Transverse Cross Section

When a material is subjected to uniaxial stress, it elongates or contracts in the direction of the applied force, and also undergoes changes in the perpendicular directions. This behavior is crucial for understanding how materials behave under stress and is governed by mechanical properties such as Poisson's ratio v, which measures the ratio of transverse strain to axial strain.
As the material stretches, it expands or contracts in orthogonal directions to the load. This phenomenon varies...

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Related Experiment Video

Updated: Jul 6, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Morphologically preprocessed joint transform correlation.

S Zhang1, M A Karim

  • 1Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, Ohio 45469-0226, USA.

Applied Optics
|March 6, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new joint transform correlation method using morphological preprocessing to reduce noise and detect edges. The enhanced technique improves image discrimination compared to existing methods.

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Area of Science:

  • Image processing
  • Pattern recognition
  • Optical correlation

Background:

  • Joint transform correlation (JTC) is a widely used pattern recognition technique.
  • Traditional JTC methods can be sensitive to noise and require preprocessing for optimal performance.
  • Existing preprocessing methods like gradient operators and wavelets have limitations in noise reduction and edge preservation.

Purpose of the Study:

  • To propose a novel morphologically preprocessed joint transform correlation (MPJTC) system.
  • To enhance the discrimination capability of JTC by integrating morphological filtering and edge detection.
  • To evaluate the performance of the proposed MPJTC against other preprocessing techniques.

Main Methods:

  • Morphological filtering applied as a preprocessing step to input images.
  • Edge detection incorporated into the preprocessing pipeline.
  • Joint transform correlation implemented with the preprocessed images.
  • Computer simulations conducted for performance evaluation.

Main Results:

  • The proposed MPJTC effectively eliminates noise from input images.
  • Edge detection enhances feature extraction for improved correlation.
  • Computer simulations demonstrate superior discrimination capability of MPJTC.
  • MPJTC outperforms gradient operator-based and wavelet-based JTC systems.

Conclusions:

  • Morphological preprocessing significantly improves JTC performance.
  • The proposed MPJTC offers a robust solution for pattern recognition tasks.
  • This method provides better discrimination than existing JTC preprocessing techniques.