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.

Fundus diseases are among the leading causes of visual impairment and blindness, many of which could be prevented through early intervention. Ultra-widefield fundus (UWF) imaging, offering a wide field of view and enabling non-mydriatic acquisition, has emerged as an ideal modality for screening; however, the volume of cases far exceeds the diagnostic capacity of ophthalmologists, necessitating an automated diagnostic system. Nevertheless, existing deep learning algorithms often encounter challenges including high computational costs, large data requirements, and performance degradation on out-of-distribution data. Domain-adaptive fine-tuning of existing multimodal large models offers a promising pathway to address these limitations. Here, we developed an anomaly detection framework for UWF images based on CLIP. First, we proposed a dual-pathway detection mechanism for accurate anomaly detection. Second, a lightweight hybrid low-rank adapter is designed for parameter-efficient fine-tuning to bridge the domain gap between natural images and fundus images. Moreover, to address the scarcity and variability of abnormal samples, we developed an anomaly synthesis algorithm based on Poisson-blending to broaden and generalize the anomaly paradigm. With only 32 pairs of training samples, the framework achieved efficient and robust anomaly detection. The framework was evaluated on 8 independent cross-source datasets (29,739 images covering >50 anomaly patterns), yielding an average AUC of 85.93% and a peak AUC of 94.54%. Owing to its low computational and data requirements, this framework provides a practical and scalable solution for fundus disease screening and shows strong potential for clinical translation. Code and details are available through https://github.com/JohnLeo-XJTU/AnomalyUWF.