摘要
油库是典型的感兴趣目标之一,大多数呈圆形。针对传统的基于Hough变换检测圆的算法存在计算量大、空间复杂度高等缺点,本文提出一种改进的梯度模糊Hough变换进行油库目标识别。算法首先利用梯度信息减少计算量,然后对边缘像素进行模糊映射,以减少峰值扩散和伪峰现象,最后针对Hough变换不考虑点之间的连通性的缺点设计去虚警算法。实验结果表明该方法计算量小,精度高,能准确定位圆心和半径,识别率达82.5%,虚警率为1.6%。
Traditional Hough transform for circle detection has several limitations, such as high computation and memory complexity. To solve these problems, we presented a modified Hough transform method based on gradient information and fuzzy theory. First, gradient information was utilized to reduce the computational complexity. Furthermore, edge points were mapped based on fuzzy theory to avoid peak diffusion and false peak. Finally, a post-processing algorithm was designed to remove the false alarms, since Hough transform would neglect the connectivity between points. Experimental results show that the proposed method has many advantages, such as low computational complexity, high detection accuracy, high detection rate, and low false target rate.
出处
《光电工程》
EI
CAS
CSCD
北大核心
2008年第3期30-34,共5页
Opto-Electronic Engineering
基金
中科院研究项目资助
关键词
梯度模糊Hough变换
油库识别
最小外接矩形
区域增长
gradient fuzzy Hough transform （GFHT）
oilcan recognition
minimum enclosing rectangle
region grow
