.png){kind=icon}
.png){kind=icon}
Return
Circle Detection with Adaptive Parameterization: A Bottom-Up Approach
.png){kind=icon}
OA
AI
DOI：10.3390/s25082552.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Circle detection remains a critical yet challenging task in computer vision, particularly under complex imaging conditions where existing measurement methods face persistent challenges in parameter configuration and noise resilience. This paper presents a novel circle detection algorithm based on two perceptually grounded parameters: the perceptual length difference resolution lambda, derived from human cognitive models, and the minimum distinguishable distance threshold K, determined through empirical observations. The algorithm implements a local stochastic sampling strategy integrated with a bottom-up circular search mechanism, with all critical parameters in the algorithm derived adaptively based on lambda and K, eliminating the need for repetitive hyperparameter search processes. Experiments demonstrate that our methodology achieves an exceptional Fscore of 85.5% on the public circle detection dataset, surpassing state-of-the-art approaches by approximately 7.3%. Notably, the framework maintains robust detection capability (Fscore = 85%) under extreme noise conditions (50% Gaussian noise contamination), maintaining superior performance relative to comparative methods. The adaptive parameterization strategy provides insights for developing vision systems that bridge computational efficiency with human perceptual robustness.
Keywords:
adaptive parameter
anti-noise
bottom-up search
circle detection
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.