提出一种快速精准的模糊图像圆检测方法，兼顾速度与抗干扰能力。
3C-FBI: A Combinatorial method using Convolutions for Circle Fitting in Blurry Images
- 结合组合采样与卷积密度估计，在参数空间高效定位圆心半径。
- 实测达0.896的交并比，40.3帧/秒，远超传统方法。
- 适合医疗、机器人等复杂环境下的高精度圆检测任务。
本文针对退化成像条件下鲁棒圆检测与拟合这一基础计算机视觉挑战，提出组合卷积圆拟合方法（3C-FBI），通过高效组合边缘像素采样与参数空间卷积密度估计，实现检测与拟合的无缝衔接。在三种实验框架中评估：（1）帕金森病评估的144帧真实医学视频数据；（2）标准圆拟合基准的受控合成数据；（3）不同空间分辨率和异常点污染水平下的系统分析。结果表明，3C-FBI达到0.896的交并比（Jaccard index），实时性能达40.3帧/秒，显著优于经典方法RCD（6.8帧/秒，标准CPU i7-10875H）。在480x480高分辨率下交并比接近1.0，160x160分辨率下含20%异常点仍保持高于0.95的准确率。合成数据测试中均值交并比达0.989，接近先进方法Qi et al. (2024)的0.991，优于RHT的0.964。该方法兼具精度、速度与鲁棒性，适用于医疗影像、机器人及工业检测等复杂场景。
原文摘要 · Abstract (English)
This paper addresses the fundamental computer vision challenge of robust circle detection and fitting in degraded imaging conditions. We present Combinatorial Convolution-based Circle Fitting for Blurry Images (3C-FBI), an algorithm that bridges the gap between circle detection and precise parametric fitting by combining (1) efficient combinatorial edge pixel (edgel) sampling and (2) convolution-based density estimation in parameter space. We evaluate 3C-FBI across three experimental frameworks: (1) real-world medical data from Parkinson's disease assessments (144 frames from 36 videos), (2) controlled synthetic data following established circle-fitting benchmarks, and (3) systematic analysis across varying spatial resolutions and outlier contamination levels. Results show that 3C-FBI achieves state-of-the-art accuracy (Jaccard index 0.896) while maintaining real-time performance (40.3 fps), significantly outperforming classical methods like RCD (6.8 fps) on a standard CPU (i7-10875H). It maintains near-perfect accuracy (Jaccard almost 1.0) at high resolutions (480x480) and reliable performance (Jaccard higher than 0.95) down to 160x160 with up to 20% outliers. In extensive synthetic testing, 3C-FBI achieves a mean Jaccard Index of 0.989 across contamination levels, comparable to modern methods like Qi et al. (2024, 0.991), and surpassing RHT (0.964). This combination of accuracy, speed, and robustness makes 3C-FBI ideal for medical imaging, robotics, and industrial inspection under challenging conditions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。