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.

scholarly journals Nonrigid Points Alignment with Soft-weighted Selection

2018 ◽  
Author(s):  
Xuelong Li ◽  
Jian Yang ◽  
Qi Wang
Keyword(s):  
Reproducing Kernel ◽  
State Of The Art ◽  
Reproducing Kernel Hilbert Space ◽  
Low Rank ◽  
Gaussian Fields ◽  
Registration Accuracy ◽  
Point Matching ◽  
Point Set Registration ◽  
Crucial Problem ◽  
Point Set

Point set registration (PSR) is a crucial problem in computer vision and pattern recognition. Existing PSR methods cannot align point sets robustly due to degradations, such as deformation, noise, occlusion, outlier, and multi-view changes. In this paper, we present a self-selected regularized Gaussian fields criterion for nonrigid point matching. Unlike most existing methods, we formulate the registration problem as a sparse approximation task with low rank constraint in reproducing kernel Hilbert space (RKHS). A self-selected mechanism is used to dynamically assign real-valued label for each point in an accuracy-aware weighting manner, which makes the model focus more on the reliable points in position. Based on the label, an equivalent matching number optimization is embedded into the non-rigid criterion to enhance the reliability of the approximation. Experimental results show that the proposed method can achieve a better result in both registration accuracy and correct matches compared to state-of-the-art approaches.

A robust non-rigid point set registration method based on inhomogeneous Gaussian mixture models

2017 ◽  
Vol 34 (10) ◽  
pp. 1399-1414 ◽  
Author(s):  
Wanxia Deng ◽  
Huanxin Zou ◽  
Fang Guo ◽  
Lin Lei ◽  
Shilin Zhou ◽  
...  
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Registration Method ◽  
Point Set Registration ◽  
Point Set

scholarly journals Convex hull indexed Gaussian mixture model (CH-GMM) for 3D point set registration

2016 ◽  
Vol 59 ◽  
pp. 126-141 ◽  
Author(s):  
Jingfan Fan ◽  
Jian Yang ◽  
Danni Ai ◽  
Likun Xia ◽  
Yitian Zhao ◽  
...  
Keyword(s):  
Convex Hull ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Point Set Registration ◽  
Point Set

A contour-guided pose alignment method based on Gaussian mixture model for precision assembly

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pengyue Guo ◽  
Zhijing Zhang ◽  
Lingling Shi ◽  
Yujun Liu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Assembly System ◽  
Alignment Method ◽  
Content Type ◽  
Point Set Registration ◽  
Contour Point ◽  
Point Set

Purpose The purpose of this study was to solve the problem of pose measurement of various parts for a precision assembly system. Design/methodology/approach A novel alignment method which can achieve high-precision pose measurement of microparts based on monocular microvision system was developed. To obtain the precise pose of parts, an area-based contour point set extraction algorithm and a point set registration algorithm were developed. First, the part positioning problem was transformed into a probability-based two-dimensional point set rigid registration problem. Then, a Gaussian mixture model was fitted to the template point set, and the contour point set is represented by hierarchical data. The maximum likelihood estimate and expectation-maximization algorithm were used to estimate the transformation parameters of the two point sets. Findings The method has been validated for accelerometer assembly on a customized assembly platform through experiments. The results reveal that the proposed method can complete letter-pedestal assembly and the swing piece-basal part assembly with a minimum gap of 10 µm. In addition, the experiments reveal that the proposed method has better robustness to noise and disturbance. Originality/value Owing to its good accuracy and robustness for the pose measurement of complex parts, this method can be easily deployed to assembly system.

Nonrigid point set registration based on Laplace mixture model with local constraints

2019 ◽  
Vol 40 (2) ◽  
pp. 335-343
Author(s):  
Chao Xu ◽  
Xianqiang Yang ◽  
Xiaofeng Liu
Keyword(s):  
Em Algorithm ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Nonrigid Registration ◽  
Model Parameters ◽  
The Novel ◽  
Content Type ◽  
Point Set Registration ◽  
Point Set ◽  
Novel Method

Purpose This paper aims to investigate a probabilistic mixture model for the nonrigid point set registration problem in the computer vision tasks. The equations to estimate the mixture model parameters and the constraint items are derived simultaneously in the proposed strategy. Design/methodology/approach The problem of point set registration is expressed as Laplace mixture model (LMM) instead of Gaussian mixture model. Three constraint items, namely, distance, the transformation and the correspondence, are introduced to improve the accuracy. The expectation-maximization (EM) algorithm is used to optimize the objection function and the transformation matrix and correspondence matrix are given concurrently. Findings Although amounts of the researchers study the nonrigid registration problem, the LMM is not considered for most of them. The nonrigid registration problem is considered in the LMM with the constraint items in this paper. Three experiments are performed to verify the effectiveness and robustness and demonstrate the validity. Originality/value The novel method to solve the nonrigid point set registration problem in the presence of the constraint items with EM algorithm is put forward in this work.

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