基于双匹配配准算法的多重复纹理图像拼接

Registration Algorithm of Multi-repeat Texture Images Based on Double-Match Image Registration

针对目前图像配准算法对于多重复纹理图像配准位置偏差的问题,提出图像内自匹配与图像间互匹配相结合的双匹配配准(Double-match image registration,DMIR)算法。首先在对待匹配图像提取尺度不变特征转换(Scale-invariant feature transform,SIFT)特征之后,通过K-近邻(K-nearest neighbor,KNN)算法进行特征匹配,分别得到同一张图片的自匹配点对和不同图像间的初始互匹配点对;然后对初始互匹配点对进行相关性计算得到最正确的匹配点对,并根据最正确的匹配点对与自匹配点对的位置关系确定更多的正确匹配点对,最后计算仿射矩阵对图像进行拼接。实验结果显示经过DMIR算法获得的正确匹配点对更均匀、更准确,且拼接图像效果更好。

To solve the problem of the registration position deviation for multi-repeat texture images,a double-match image registration(DMIR)algorithm is proposed.The DMIR algorithm not only considers the matching result of one graph with another graph,but also considers the matching result of a graph with its own features.Firstly,the key points are matched by the K-nearest neighbor(KNN)algorithm after extracting the feature points by the scale-invariant feature transform(SIFT)algorithm.As a result,the selfmatching point pairs of the same image and the initial matching point pairs between different images are obtained respectively.Secondly,the best matching point pairs are obtained by computing the correlation between different points of the initial matching point pairs.Thirdly,the correct matching point pairs of the two images are determined,which depend on the positional relationship between the best matching point pairs and the self-matching point pairs.Lastly,the affine matrix is calculated according to the matching point pairs,and the image stitching is performed.The experimental results show that the matching point pairs obtained by the DMIR algorithm are more accurate,and the stitched images are better than others.