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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Interactive Cosegmentation Using Global and Local Energy Optimization.

Xingping Dong, Jianbing Shen, Ling Shao

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

|July 18, 2015

Summary

This summary is machine-generated.

This study introduces an interactive cosegmentation method that uses global and local energy optimization. The novel approach improves performance by incorporating user input and object histogram matching, outperforming existing methods.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Image segmentation is crucial for computer vision tasks.
Existing unsupervised and interactive cosegmentation methods have limitations.

Purpose of the Study:

To develop a novel interactive cosegmentation method.
To improve cosegmentation performance using global and local energy optimization.

Main Methods:

Utilizes user scribbles to build a Gaussian mixture model for global energy.
Incorporates inter-image energy for global object histogram matching.
Applies spline regression for local energy minimization and smoothness learning.
Employs an iterative optimization algorithm to reduce computational complexity.

Main Results:

The proposed method outperforms state-of-the-art unsupervised and interactive cosegmentation techniques.
Achieves superior performance on the iCoseg and MSRC benchmark datasets.

Conclusions:

The novel interactive cosegmentation method effectively leverages global and local energy optimization.
Demonstrates significant improvements over existing methods in benchmark evaluations.