CAJ | 학술논문

针对现有圆检测算法以像素为投票主体在二维或三维空间进行参数累积，运算复杂度高，难以达到复杂自然场景中的实时检测要求的问题，提出一种以线段为投票主体并基于一维概率 Hough 变换的实时圆检测算法。首先基于梯度方向对 Canny 边缘像素实施逐段分割，从中选取满足一定曲率条件的线段作为种子，对每个种子沿半径进行一维概率 Hough 累积；然后根据峰值大小和位置提取有效圆及其初始半径；最后利用圆的直接最小二乘拟合进一步定位圆半径和圆心。对复杂的自然场景图像进行实验的结果表明，通过选取合适的分割阈值，该算法在确保圆检测可靠性的前提下在速度上显著优于现有算法。
침대현유원검측산법이상소위투표주체재이유혹삼유공간진행삼수루적，운산복잡도고，난이체도복잡자연장경중적실시검측요구적문제，제출일충이선단위투표주체병기우일유개솔 Hough 변환적실시원검측산법。수선기우제도방향대 Canny 변연상소실시축단분할，종중선취만족일정곡솔조건적선단작위충자，대매개충자연반경진행일유개솔 Hough 루적；연후근거봉치대소화위치제취유효원급기초시반경；최후이용원적직접최소이승의합진일보정위원반경화원심。대복잡적자연장경도상진행실험적결과표명，통과선취합괄적분할역치，해산법재학보원검측가고성적전제하재속도상현저우우현유산법。
The state of the art in circle detection usually resorts to edge pixels as the voting components to perform parametric accumulation in 2D or 3D space, which generally incurs high computational cost and is thus unable to meet the real-time processing requirements in complex natural scene processing. Using edge sections as voting components, this paper presents a robust real-time circle detection algorithm based on 1D probabilistic Hough Transform. The algorithm first segments Canny edges based on their gradient directions into arc sections, from which seed sections meeting certain curvature criteria are selected. For each seed, a probability-weighted 1D Hough accumulation is then built along the radius dimension to detect a valid circle related to the seed and estimate the initial radius of the circle based on the peak magnitude and peak position of the 1D accumulation. Finally direct circular least square fitting is employed to further pinpoint the radius and center information for the detected circle. The experiment shows, when appropriate segmentation thre-sholds are chosen, the algorithm significantly outperforms the state of the art in processing speed while maintaining high reliability as far as the circle detection in complex natural scene images is concerned.