  For nonrigid image registration, matching the particular structures (or the
outliers) that have missing correspondence and/or local large deformations, can
be more difficult than matching the common structures with small deformations
in the two images. Most existing works depend heavily on the outlier
segmentation to remove the outlier effect in the registration. Moreover, these
works do not handle simultaneously the missing correspondences and local large
deformations. In this paper, we defined the nonrigid image registration as a
local adaptive kernel regression which locally reconstruct the moving image's
dense deformation vectors from the sparse deformation vectors in the
multi-resolution block matching. The kernel function of the kernel regression
adapts its shape and orientation to the reference image's structure to gather
more deformation vector samples of the same structure for the iterative
regression computation, whereby the moving image's local deformations could be
compliant with the reference image's local structures. To estimate the local
deformations around the outliers, we use joint saliency map that highlights the
corresponding saliency structures (called Joint Saliency Structures, JSSs) in
the two images to guide the dense deformation reconstruction by emphasizing
those JSSs' sparse deformation vectors in the kernel regression. The
experimental results demonstrate that by using local JSS adaptive kernel
regression, the proposed method achieves almost the best performance in
alignment of all challenging image pairs with outlier structures compared with
other five state-of-the-art nonrigid registration algorithms.
