Rigid Shape Registration Based on Extended Hamiltonian Learning

Abstract

Shape registration, finding the correct alignment of two sets of data, plays a significant role in computer vision such as objection recognition and image analysis. The iterative closest point (ICP) algorithm is one of well known and widely used algorithms in this area. The main purpose of this paper is to incorporate ICP with the fast convergent extended Hamiltonian learning (EHL), so called EHL-ICP algorithm, to perform planar and spatial rigid shape registration. By treating the registration error as the potential for the extended Hamiltonian system, the rigid shape registration is modelled as an optimization problem on the special Euclidean group S E ( n ) ( n = 2 , 3 ) . Our method is robust to initial values and parameters. Compared with some state-of-art methods, our approach shows better efficiency and accuracy by simulation experiments.

Image Registration for Visualizing Magnetic Flux Leakage Testing under Different Orientations of Magnetization

“…They can only match the parts of the image with similar intensity distributions, because such methods are based on the assumption that the corresponding structures in the registered images would have similar intensities [10]. Additionally, the MFL field distribution detected under DOM varies greatly, making the shape alignment methods inapplicable [14].…”

Research on Object Panoramic 3D Point Cloud Reconstruction System Based on Structure From Motion

“…Over the past few years, a lot of research results on multiview point cloud data alignment have been put forward at home and abroad. For example, (ICP) algorithm proposed by BESL et al [18] in 1992 is a classical point cloud alignment algorithm, and the ICP algorithm and its improvements [19][20][21] have also become the most widely used alignment algorithms in the industry. However, the results of such algorithms are dependent on the relative positional relationships of the point cloud data.…”