Template matching and registration based on edge feature

In order to improve the performance of heterogeneous image matching and registration, the Weighted Voting Accumulation Measure(WVAM) based on the edge feature and image registration algorithm based on the steepest descent of the likelihood function are proposed. The WVAM is capable of resisting the interference of noise and the similarity region and can achieve matching location of template. On this basis, the likelihood function of edge sets registration is established on the basis of Gauss Mixture Model (GMM) of point sets. In order to achieve the registration between the template and matching area, and resolve the optimum transformation parameter by using the steepest descent method, the likelihood function is regarded as objective function and the affine transformation parameter is regarded as the optimization variance. The results of simulation experiments of this algorithm proved that the good performance of template and registration.