摘要
提出了一种新的基于Gabor滤波器与支持向量机(SVM)结合的分类器(GSC)估计方法.该方法首先运用图像的Gabor小波特征并融合图像的适配性参数,然后结合SVM分类方法将图像内任意区域的匹配概率估计问题转化为待估区域内像素的分类问题,从定量的角度衡量导航基准地图中各个区域的匹配性能.用相关匹配方法对预测的区域验证表明,本方法不但计算速度快,而且预测精度较为准确.
This paper presented a new method to obtain the statistical relation between image features and matching probability. After integrating the Gabor wavelet features and some other parameters as matching area measures, this paper uses the support vector machine （SVM） classification method to transform the estimation of matching probability problem into a classifying one. The experiments show that the proposed method not only has faster computation speed than the method based on the correlation functions,but also gives a reasonable precise estimation.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2006年第3期485-489,共5页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金资助项目(60072026)
