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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.
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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.
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Related Experiment Video

Updated: Aug 13, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching.

Yizhi Cong¹, Yan Wang^1,2, Wenju Hou²

¹Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, School of Artificial Intelligence, Jilin University, Changchun 130012, China.

Entropy (Basel, Switzerland)

|January 21, 2023

Summary

This study introduces a new image stitching method to improve feature matching in challenging low-texture areas. The approach enhances alignment accuracy and reduces distortion for better panoramic image quality.

Keywords:

feature correspondences increase hybrid terms optimization image alignment image stitching

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Published on: September 22, 2023

Area of Science:

Computer Vision
Image Processing
Computational Geometry

Background:

Image stitching relies on accurate feature detection and matching.
Current methods like SIFT+RANSAC struggle with low-textured or repetitive regions, leading to misalignment.
Insufficient matches in overlapping areas cause erroneous warping model estimation.

Purpose of the Study:

To develop a robust image stitching approach for low-textured and repetitive regions.
To improve feature correspondences and optimize hybrid terms for accurate alignment.
To mitigate projection and perspective distortion while balancing alignment and distortion.

Main Methods:

Proposed a novel method to increase feature correspondences and optimize hybrid terms.
Implemented a hybrid terms optimization warp combining global similarity transformations and initial homography.
Adjusted various term parameters to estimate optimal warping, balancing alignment and distortion.

Main Results:

Achieved sufficient correct feature correspondences in challenging overlapping regions.
Successfully eliminated misalignment in low-textured and repetitively-textured areas.
Demonstrated superior performance over state-of-the-art methods in accurate alignment and reduced distortion.

Conclusions:

The proposed method effectively addresses limitations of current image stitching techniques.
It provides accurate alignment and minimizes distortion, especially in difficult image areas.
Offers improved panoramic image quality with less projection and perspective distortion.