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ProMask: Probability mask representation for skeleton detection.

Xiuxiu Bai¹, Lele Ye¹, Zhe Liu¹

¹School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Neural Networks : the Official Journal of the International Neural Network Society

|March 6, 2023

Summary

This summary is machine-generated.

This study introduces ProMask, a novel skeleton detection model. ProMask enhances accuracy and robustness in identifying object skeletons, even with complex backgrounds and varying scales.

Keywords:

Convolutional neural network Probability representation Robustness Skeleton detection

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Area of Science:

Computer Vision
Image Analysis
Machine Learning

Background:

Object skeleton detection in natural images is challenging due to scale variations and complex backgrounds.
Skeleton representations are compact but sensitive to spatial positioning, complicating detection.
Existing methods struggle with the small visual footprint and positional sensitivity of skeleton lines.

Purpose of the Study:

To propose a novel skeleton detection model, ProMask, addressing limitations of current methods.
To introduce a probability mask representation and a vector router for improved skeleton detection.
To enhance detection performance, efficiency, and robustness in natural image environments.

Main Methods:

Developed ProMask, a skeleton detection model incorporating a probability mask representation.
Integrated a vector router module with orthogonal basis vectors for dynamic position adjustment.
Utilized a skeleton probability mask to describe the gradual formation of skeleton points.

Main Results:

ProMask demonstrates high detection performance and robustness.
The model achieves superior performance, efficiency, and robustness compared to state-of-the-art methods.
Experimental results validate the effectiveness of the proposed probability mask representation.

Conclusions:

The proposed ProMask model offers significant improvements in skeleton detection.
The novel skeleton probability representation is effective, reasonable, and simple.
This approach is expected to become a standard for future skeleton detection tasks.