Nonrigid Image Registration Using Higher-Order MRF Model with Dense Local Descriptor

In this paper, we propose a nonrigid image registration method using a Markov Random Field (MRF) energy model with higher-order smoothness priors and a dense local descriptor. The image registration is designed as finding an optimal labeling of the MRF energy model where the label corresponds to a discrete displacement vector. The proposed MRF energy model uses matching scores of dense local descriptors between images as a data cost. In this model, spatial relationships are constructed between nodes using higher-order smoothness priors. As the local descriptor is invariant to scale and rotation and also robust to changing appearances, this method can handle multimodal images involving scale and rotation transformations. The higher-order smoothness priors can generate desired smoother displacement vector fields and do not suffer from fronto-parallel effects commonly occurred in first-order priors. The usage of higher-order priors in the energy model enables this method to produce more accurate registration results. In the experiments, we will show registration results using multi-modal Brain MRI Images and facial images with expression and light changes.