改进的SURF算法在彩色车载影像匹配中的应用

Vehicle based Images Matching by Improved SURF Algorithm

摘要:

在分析了具有尺度不变特征的鲁棒特征加速算法(SURF算法)的基础上，提出了一种基于高斯颜色模型的增维彩色SURF算法.该算法将RGB颜色模型转换到高斯颜色模型，使用SURF算法（增加了48维颜色特征描述向量）进行匹配，再使用核线约束剔除误匹配.结果表明：尽管相对于原有的SURF算法略微增加了计算量，但是在匹配点对数、匹配正确率、匹配点分布均匀性上都具有明显的优势.

Abstract:

Aimed at the matching problem for color images obtained by vehicle based mobile mapping system, an improved algorithm is proposed based on speeded up robust features(SURF) algorithm, which is robust in rotation and resizing. A dimension incremented color SURF algorithm based on Gaussian color model is proposed. RGB color model is transformed to Gaussian color model firstly, then the new SURF method is employed for matching experiment, which is improved by additional 48 dimentional colour features vectors. At last, the epiplor constraint is applied to the elimination of false matches. The experimental results show that the proposed algorithm performs favorably on matching points numbers, matching accuracy, and distribution uniformity of matching points, although a slightly more calculation is needed in comparison with the original SURF algorithm. It can meet the matching demand of mobile mapping system (MMS).