scholarly journals A New Affine Registration Algorithm for Matching 2D Point Sets

2007 ◽  
Author(s):  
Jeffrey Ho ◽  
Ming-hsuan Yang ◽  
Anand Rangarajan ◽  
Baba Vemuri
Keyword(s):  
Point Sets ◽  
Affine Registration ◽  
Registration Algorithm

scholarly journals A New Method for Affine Registration of Images and Point Sets

2005 ◽  
pp. 224-234 ◽  
Author(s):  
Juho Kannala ◽  
Esa Rahtu ◽  
Janne Heikkilä ◽  
Mikko Salo
Keyword(s):  
New Method ◽  
Point Sets ◽  
Affine Registration

Robust registration algorithm based on rational quadratic kernel for point sets with outliers and noise

2021 ◽  
Author(s):  
Runzhao Yao ◽  
Shaoyi Du ◽  
Teng Wan ◽  
Wenting Cui ◽  
Yang Yang ◽  
...  
Keyword(s):  
Point Sets ◽  
Registration Algorithm

scholarly journals Robust affine registration method using line/surface normals and correntropy criterion

2022 ◽  
Author(s):  
Abdurrahman Yilmaz ◽  
Hakan Temeltas
Keyword(s):  
Closed Form Solution ◽  
Point Clouds ◽  
Form Solution ◽  
Least Square ◽  
3D Registration ◽  
Point Sets ◽  
Registration Method ◽  
Affine Registration ◽  
Surface Normal ◽  
Application Procedure

AbstractThe problem of matching point clouds is an efficient way of registration, which is significant for many research fields including computer vision, machine learning, and robotics. There may be linear or non-linear transformation between point clouds, but determining the affine relation is more challenging among linear cases. Various methods have been presented to overcome this problem in the literature and one of them is the affine variant of the iterative closest point (ICP) algorithm. However, traditional affine ICP variants are highly sensitive to effects such as noises, deformations, and outliers; the least-square metric is substituted with the correntropy criterion to increase the robustness of ICPs to such effects. Correntropy-based robust affine ICPs available in the literature use point-to-point metric to estimate transformation between point clouds. Conversely, in this study, a line/surface normal that examines point-to-curve or point-to-plane distances is employed together with the correntropy criterion for affine point cloud registration problems. First, the maximum correntropy criterion measure is built for line/surface normal conditions. Then, the closed-form solution that maximizes the similarity between point sets is achieved for 2D registration and extended for 3D registration. Finally, the application procedure of the developed robust affine ICP method is given and its registration performance is examined through extensive experiments on 2D and 3D point sets. The results achieved highlight that our method can align point clouds more robustly and precisely than the state-of-the-art methods in the literature, while the registration time of the process remains at reasonable levels.

scholarly journals Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
D. Salas-Gonzalez ◽  
J. M. Górriz ◽  
J. Ramírez ◽  
P. Padilla ◽  
I. A. Illán
Keyword(s):  
Convergence Rate ◽  
Optimization Algorithm ◽  
Histogram Equalization ◽  
Brain Images ◽  
Affine Registration ◽  
Registration Algorithm ◽  
Affine Model ◽  
The Cost ◽  
General Affine ◽  
Brain Template

A procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before proceeding with the affine registration. The preprocessed source brain images are spatially normalized to a template using a general affine model with 12 parameters. A sum of squared differences between the source images and the template is considered as objective function, and a Gauss-Newton optimization algorithm is used to find the minimum of the cost function. Using histogram equalization as a preprocessing step improves the convergence rate in the affine registration algorithm of brain images as we show in this work using SPECT and PET brain images.

Affine registration for multidimensional point sets under the framework of Lie group

2013 ◽  
Vol 22 (1) ◽  
pp. 013022 ◽  
Author(s):  
Lifen Ding ◽  
Yaxin Peng ◽  
Chaomin Shen ◽  
Zhiyu Hu
Keyword(s):  
Lie Group ◽  
Point Sets ◽  
Affine Registration
Sign in / Sign up

Export Citation Format

Share Document