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Carbon Skeletons

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Life on Earth is carbon-based, as all macromolecules that make up living organisms contain carbon atoms. All organic compounds have a carbon backbone. Each carbon atom is tetravalent and can bond with four other atoms, making it an extraordinarily flexible component of biological molecules. Because carbon’s valence electrons are stable, it rarely becomes an ion. As the carbon chain increases in length, structural modifications such as ring structures, double bonds, and branching side...

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Updated: Nov 19, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Image Co-Skeletonization via Co-Segmentation.

Koteswar Rao Jerripothula, Jianfei Cai, Jiangbo Lu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 1, 2021

Summary

This summary is machine-generated.

This study introduces image co-skeletonization, a novel method for joint skeleton extraction from multiple images. It leverages shared object information to improve skeletonization accuracy, outperforming single-image approaches.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Joint image processing offers advantages over individual methods.
Single image skeletonization is challenging due to lack of prior object knowledge.
Leveraging common priors in semantically similar images can aid skeletonization.

Purpose of the Study:

To introduce and develop image co-skeletonization for joint skeleton extraction.
To propose a coupled framework for co-skeletonization and co-segmentation.
To facilitate shape information discovery for improved skeletonization.

Main Methods:

A coupled framework integrating co-skeletonization and co-segmentation.
Utilizing shared object shape information across images.
Development of a novel benchmark dataset with 1.8K annotated images across 38 categories.

Main Results:

The coupled framework synergistically benefits both co-skeletonization and co-segmentation.
The method achieves promising results in weakly supervised, supervised, and unsupervised scenarios.
Demonstrated effectiveness of leveraging common priors for improved skeleton extraction.

Conclusions:

Image co-skeletonization is a viable and effective approach for joint skeleton extraction.
The proposed coupled framework enhances accuracy through synergistic task interaction.
The developed benchmark dataset facilitates future research in image co-skeletonization.