A robust point set registration algorithm based on information geometry

2014 ◽  
Author(s):  
Xiaoqiang Hua ◽  
Ping Wang ◽  
Kefeng Ji ◽  
Yinghui Gao ◽  
Ruigang Fu
Keyword(s):  
Information Geometry ◽  
Point Set Registration ◽  
Registration Algorithm ◽  
Point Set

scholarly journals A Novel Information-Theoretic Point-Set Measure Based on the Jensen-Havrda-Charvat-Tsallis Divergence

2008 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Suyash Awate ◽  
James Gee
Keyword(s):  
Matrix Decomposition ◽  
Fine Tuning ◽  
Information Theoretic ◽  
Point Set Registration ◽  
Registration Algorithm ◽  
Divergence Point ◽  
Theoretic Point ◽  
Point Set ◽  
Jensen Shannon Divergence ◽  
Decomposition Schemes

A novel point-set registration algorithm was proposed in [6] based on minimization of the Jensen-Shannon divergence. In this contribution, we generalize this Jensen-Shannon divergence point-set measure framework to the Jensen-Havrda-Charvat-Tsallis divergence. This generalization permits a fine-tuning of the actual divergence measure between robustness and specificity. The principle contribution of this submission is theitk::JensenHavrdaCharvatTsallisPointSetMetric class which is derived from the existing itk::PointSetToPointSetMetric. In addition, we provide other classes with utility that would extend beyond the point-set measure framework that we provide in this paper. This includes a point-set analogue of the itk::ImageFunction, i.e. itk::PointSetFunction. From this class we derive the class itk::ManifoldParzenWindowsPointSetFunction which provides a Parzen windowing scheme for learning the local structure of point-sets. Finally, we include the itk::DecomposeTensorFunction class which wraps the different vnl matrix decomposition schemes for easy use within ITK.

An Improved Non-Rigid Point Set Registration Algorithm by Preserving Local Topology

2021 ◽  
Vol 31 (4) ◽  
pp. 646-655
Author(s):  
Qiang Sang ◽  
Tao Huang ◽  
Huihuang Tang ◽  
Ping Jiang
Keyword(s):  
Point Set Registration ◽  
Registration Algorithm ◽  
Point Set ◽  
Local Topology

Precise Point Set Registration Algorithm for Indoor Scene Reconstruction

2019 ◽  
Author(s):  
Qixing Xie ◽  
Yang Yang ◽  
Teng Wan ◽  
Wenting Cui ◽  
Yuying Liu ◽  
...  
Keyword(s):  
Scene Reconstruction ◽  
Point Set Registration ◽  
Registration Algorithm ◽  
Indoor Scene ◽  
Point Set

scholarly journals Point Set Registration Based on Improved KL Divergence

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Guangfu Qu ◽  
Won Hyung Lee
Keyword(s):  
Gaussian Distribution ◽  
State Of The Art ◽  
Gaussian Mixture ◽  
Registration Accuracy ◽  
Position Information ◽  
Point Set Registration ◽  
Kl Divergence ◽  
Registration Algorithm ◽  
Robustness To Noise ◽  
Point Set

A point set registration algorithm based on improved Kullback–Leibler (KL) divergence is proposed. Each point in the point set is represented as a Gaussian distribution. The Gaussian distribution contains the position information of the candidate point and surrounding ones. In this way, the entire point set can be modeled as a Gaussian mixture model (GMM). The registration problem of two point sets is further converted as a minimization problem of the improved KL divergence between two GMMs, and the genetic algorithm is used to optimize the solution. Experimental results show that the proposed algorithm has strong robustness to noise, outliers, and missing points, which achieves better registration accuracy than some state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document