基于样本分布信息的自适应 Hough 变换方法

摘要

参数空间的量化单位影响霍夫变换（Hough transform ，HT ）提取直线特征的精度，为此提出一种霍夫变换中参数空间量化单位自适应调整的方法———自适应霍夫变换（Adaptive HT ，AHT）方法。首先，根据采样数据建立样本统计模型，并确定该模型的参数；然后，根据模型参数随量化单位的变化趋势以及样本信息的分布特征，给出量化单位的自适应调整策略，从而获取优化的量化单位；最后，将优化的量化单位应用于霍夫变换特征提取。实验结果表明，在结构化环境中，该方法能够实现优化量化单位的目标，从而有效减小了直线特征检测误差，提高了检测精度。%An adaptive Hough transform (AHT) method was proposed ,which aims at reducing effects of the quantization unit of the parameter space on Hough transform (HT ) in detecting line features .First , the sample model was built up by using samples and computing parameters of the model .Then ,according to changes in the model parameters and sample distributions ,the method was established to get the appropriate quantization parameters .Finally ,the optimized quantization units were obtained and applied to feature extraction in a structured environment .The results show that the proposed method can optimize the quantization units ,reduce the line detection error ,and improve detection accuracy .