Local Structure Matching Driven by Joint-Saliency-Structure Adaptive Kernel Regression

TL;DR

This paper introduces a novel nonrigid image registration method that uses joint-saliency-structure adaptive kernel regression to effectively align images with outliers and large deformations by focusing on salient structures.

Contribution

It proposes a local adaptive kernel regression framework guided by joint saliency structures to improve registration accuracy in challenging scenarios with outliers and large deformations.

Findings

Achieves near state-of-the-art registration performance on challenging image pairs.

Effectively handles outliers and missing correspondences.

Outperforms five existing nonrigid registration algorithms.

Abstract

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.