Han Lin, Zhuang Yan, Chen Ke, Xie Yuhua, Liao Guoliang, Yin Guangfu, Lin Jiangli
College of Biomedical Engineering, Sichuan University, Chengdu 610065, China.
Sensors (Basel). 2025 Apr 17;25(8):2552. doi: 10.3390/s25082552.
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 λ, 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 λ 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.
在计算机视觉中，圆检测仍然是一项关键但具有挑战性的任务，尤其是在复杂成像条件下，现有测量方法在参数配置和抗噪声能力方面一直面临挑战。本文提出了一种基于两个感知基础参数的新型圆检测算法：从人类认知模型推导而来的感知长度差异分辨率λ，以及通过实证观察确定的最小可区分距离阈值K。该算法实现了一种与自下而上的圆形搜索机制相结合的局部随机采样策略，算法中的所有关键参数均基于λ和K自适应推导得出，无需重复进行超参数搜索过程。实验表明，我们的方法在公共圆检测数据集上实现了85.5%的卓越F分数，比最先进的方法高出约7.3%。值得注意的是，该框架在极端噪声条件（50%高斯噪声污染）下保持了强大的检测能力（F分数 = 85%），相对于比较方法保持了卓越的性能。自适应参数化策略为开发将计算效率与人类感知鲁棒性相结合的视觉系统提供了见解。
