摘要
为解决传统尺度不变特征点提取算子计算复杂度高、抗噪能力不强以及特征点位置发生漂移等问题,提出一种基于尺度空间因果关系的尺度不变特征提取方法.首先原图像与高斯函数进行卷积获得高斯平滑图像;然后在原图与高斯图像中分别提取Harris角点作为候选特征点;最后以高斯图像上的候选特征点为中心向原图上投影寻找对应的特征点作为最终的尺度不变特征点.实验结果表明,该算法容易实现、计算效率高、抗噪能力强.该算法能为后续视觉处理提供稳定抗噪的尺度不变特征点.
To resolve the problems of high computational complexity, low anti-noise ability and the dnttmg of plxel position, a scale invariant feature algorithm based on causality is proposed in this paper. Firstly the Gauss smoothing image is built up by Gaussian convolution with the original image. Then, the Harris corners are extracted as candidate features both in the original and the Gauss image. Finally, the stable scale invariant features are acquired by projection from the original image to the Gauss image. The experimental results indicate that this algorithm is concise, fast, efficient with strong anti-noise ability, and provides a basis for subsequent visual processing.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2016年第5期85-89,共5页
Journal of Harbin Institute of Technology
基金
湖南省自然科学基金(13JJ9008)
关键词
尺度不变特征
因果关系
重复率
the scale invariant features
causality
repeatability
作者简介
刘立（1971-），男，博士，硕士生导师．
通信作者：罗扬，liuleelap@163．com．
