随机圆检测算法的采样约束和参数校准策略

A Circle Detection Algorithm Based on Sampling Constraints and Parameter Refinement

随机Hough变换和随机圆检测算法是图像中检测圆轮廓的快速方法,但在实际应用中分别在速度和精度上有不足。将上述算法中的随机采样分布、采样累积分布和采样次数阈值归为采样约束问题,将代理点计算出的参数与真实参数的偏差归为参数校准问题。经分析上述问题,将改进的随机圆检测算法作为快速识别方法,将随机圆Hough变换作为校准方法,结合两者的优点提出一种基于识别-校准框架的高效圆检测算法。实验数据证明,在噪声和不理想圆轮廓条件下,该框架能够很好地平衡检测速度与精度,从而体现出算法的高效性。

Although randomized Hough transform and randomized circle detection are two fast algorithms for circle detection in image, there exists deficiency of speed and accuracy while practicing them. In this paper, we generally summarized two problems in the above algorithms. First, sampling distribution, accumulation distribution and number of consecutive sampling were concluded as the problem of sampling constraints. Second, the bias between parameters that are only determined by the three agent pixels and true ones were regarded as the problem of refinement. Based on the analysis of the two problems, we operated improved randomized circle detection algorithm and randomized Hough transform as a fast recognition method and a refinement scheme, respectively. Thus, a new circle detection method which is in the framework of recognition-refinement was proposed. Results from applying our method to images with noise and inferior boundaries demonstrate that this framework manages to balance well the tradeoff between speed and accuracy of detection, and show the effectiveness of the proposed algorithm.