基于重要性采样的广义霍夫变换算法  

Generalized hough transform algorithm based on importance sampling

作　　者：宋晨菲 张彤[1] SONG Chenfei;ZHANG Tong(School of Mechatronic Engineering,Guilin University of Electronic Technology,Guilin 541004,China)

机构地区：[1]桂林电子科技大学机电工程学院,广西桂林541004

出　　处：《桂林电子科技大学学报》2020年第5期405-410,共6页Journal of Guilin University of Electronic Technology

基　　金：国家自然科学基金(61661017,61967005,U1501252);广西自然科学基金(2017GXNSFBA198212);广西科技基地和人才专项(桂科AD19110060);认知无线电教育部重点实验室基金(CRKL190107,CRKL160104)。

摘　　要：针对物体检测中传统广义霍夫变换算法计算量大、效率低以及基于特征匹配算法对于放缩旋转图像的误检问题,提出一种基于重要性采样的广义霍夫变换算法。将广义霍夫变换置于重要性采样框架中,边缘点根据显著性度量值筛选,作为广义霍夫变换的投票元素,并采用加权多分辨率分析方法,极大地减少了计算量。通过多种实验(放缩、旋转、加噪、遮挡等)与其他相似方法进行对比,结果表明,该算法检测精度及效率较高。A new generalized hough transform algorithm based on importance sampling framework is proposed to solve the problem of large computation and low efficiency of traditional generalized Hough transform algorithm in object detection and the problem of false detection based on feature matching algorithm for zoom and rotate images.The generalized Hough transform is placed in the importance sampling frame,the edge points are filtered according to the significance measurement value,and are taken as the voting elements of the generalized hough transform.And the weighted multi-resolution analysis method is adopted to greatly reduce the computation.Through a variety of experiments(shrinkage,rotation,noise,shielding,etc.)and comparison with other similar methods,it is verified from the theoretical basis and concrete implementation that the method has higher efficiency and better accuracy,and can meet the real-time and high precision requirements in industrial detection.

关 键 词：物体检测 重要性采样 显著性度量 广义霍夫变换 多分辨率分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]