Wang Bingjian, Lu Quan, Li Yapeng, Li Fan, Bai Liping, Lu Gang, Lai Rui
School of Technical Physics, Xidian University, 204 Box No. 2 South Road TaiBai, Xi'an, Shaan'xi, 710071, China.
Appl Opt. 2011 May 1;50(13):1861-7. doi: 10.1364/AO.50.001861.
A new image registration method for multimodal images is proposed in this paper. This method is a combination of the modified scale invariant feature transform (SIFT) feature extraction algorithm and the shape-context feature descriptor. Salient points of multimodal images are extracted by using the modified SIFT feature extraction algorithm. Then each salient point is described by using the shape-context descriptor that formed a feature vector from the orientation histograms of the subregion around each salient point. After salient points matching by using Euclidean distance, random sample consensus algorithm is used to eliminate wrong corresponding pairs. At last, multimodal images registration is achieved by affine transformation and bilinear interpolation. Experimental results for registration of IR images and electro-optical images show that this method has a good registration result.
本文提出了一种新的多模态图像配准方法。该方法是改进的尺度不变特征变换（SIFT）特征提取算法与形状上下文特征描述符的结合。利用改进的SIFT特征提取算法提取多模态图像的显著点。然后使用形状上下文描述符对每个显著点进行描述，该描述符根据每个显著点周围子区域的方向直方图形成特征向量。在使用欧几里得距离进行显著点匹配后，使用随机抽样一致性算法消除错误的对应对。最后，通过仿射变换和双线性插值实现多模态图像配准。红外图像和光电图像配准的实验结果表明，该方法具有良好的配准效果。
