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Related Experiment Video

Updated: Jun 28, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

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 summary is machine-generated.

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.

Keywords:

adaptive parameter anti-noise bottom-up search circle detection

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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.