Multifeature energy optimization framework and parameter adjustment-based nonrigid point set registration

Abstract

Nonrigid point set registration is a key technology in the field of remote sensing image registration, which is widely used in military and civil fields such as natural disaster damage assessment, agricultural and urban land-use planning, environmental quality monitoring, and ground target identification. We present a multifeature energy optimization framework and parameter adjustment-based nonrigid point set registration that has three contributions: (1) an energy optimization framework is designed to freely combine multiple features for estimating correspondences between two point sets, (2) the thin-plate spline and Gauss radial basis function transformation models can be optionally implemented for solving two-dimensional, three-dimensional, or higher-dimensional registration problems, and (3) the free parameters can be adjusted by the proposed three parameter adjustment approaches to yield higher registration accuracy in varied registration patterns. We test the performances of our method in contour point sets, sequence images, remote sensing images, medical images, and real images and compare with 10 state-of-the-art methods, where our method shows favorable performances in most scenarios.