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Circle Detection with Adaptive Parameterization: A Bottom-Up Approach
Lin Han1, Yan Zhuang1, Ke Chen1
1College of Biomedical Engineering, Sichuan University, Chengdu 610065, China.
Sensors (Basel, Switzerland)
|April 26, 2025
Summary
This study introduces a new circle detection algorithm using human perception parameters. It achieves high accuracy and robust performance, even with significant noise, outperforming existing methods.
Area of Science:
- Computer Vision
- Image Processing
- Human-Computer Interaction
Background:
- Circle detection is crucial in computer vision but challenging under complex imaging conditions.
- Existing methods struggle with parameter tuning and noise resilience.
Purpose of the Study:
- To present a novel circle detection algorithm using perceptually grounded parameters.
- To eliminate the need for manual hyperparameter tuning through adaptive parameterization.
Main Methods:
- Utilizes perceptual length difference resolution (λ) and minimum distinguishable distance threshold (K).
- Employs a local stochastic sampling strategy and bottom-up circular search.
- Derives all critical parameters adaptively based on λ and K.
Main Results:
- Achieved an F-score of 85.5% on a public dataset, surpassing state-of-the-art by 7.3%.
- Maintained robust detection (F-score = 85%) under 50% Gaussian noise.
- Demonstrated superior performance compared to existing methods under noisy conditions.
Conclusions:
- The novel algorithm offers high accuracy and noise resilience in circle detection.
- Adaptive parameterization based on perceptual cues enhances computational efficiency and robustness.
- Provides insights for developing vision systems aligned with human perceptual capabilities.
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