Gaussian mixture model (GMM) has recently been applied for image registration given its robustness and efficiency. However, in previous GMM methods, all the feature points are treated identically. By incorporating local class features, this letter proposes a multiple Gaussian mixture models (M-GMM) method for image registration. The proposed method can achieve higher accuracy results with less registration time. Experiments on real image pairs further proved the superiority of the proposed method.

Registration consistency (RC) stands out as a widely-used automatic measure from existing image registration evaluation measures. However the original RC neglects the influence brought by the image intensity variation, leading to several problems. This letter proposes a rectified registration consistency, which takes both image intensity variation and geometrical transformation into consideration. Therefore the geometrical transformation is evaluated more by decreasing the influence of intensity variation. Experiments on real image pairs demonstrated the superiority of the proposed measure over the original RC.