A robust non-rigid point set registration method based on asymmetric gaussian representation

Wang, Gang; Wang, Zhicheng; Chen, Yufei; Zhang, Weidong

doi:10.1016/j.cviu.2015.05.014

Cited by 50 publications

(16 citation statements)

References 31 publications

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“…The recall is defined as the proportion of true-positive correspondences to the ground truth correspondences. The recall-accuracy curve, as used in [ 10 , 25 ], denotes the ability of a registration method to determine as many true-positive correspondences as possible with low errors in accuracy. The F 1 measure is used to evaluate the balance between recall and precision.…”

Section: Resultsmentioning

confidence: 99%

“…Jian et al [ 10 ] used two GMMs to represent the point sets, and the differences of the two GMMs are minimized to solve the problem of point set registration. Moreover, there are some additional algorithms [ 21 – 25 ] that use other kinds of mixture model for point set registration. However, these methods mainly use the global relationships to find the correspondence and hardly consider the structure information.…”

Section: Introductionmentioning

confidence: 99%

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Robust CPD Algorithm for Non-Rigid Point Set Registration Based on Structure Information

Peng

Xiao

et al. 2016

PLoS ONE

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Recently, the Coherent Point Drift (CPD) algorithm has become a very popular and efficient method for point set registration. However, this method does not take into consideration the neighborhood structure information of points to find the correspondence and requires a manual assignment of the outlier ratio. Therefore, CPD is not robust for large degrees of degradation. In this paper, an improved method is proposed to overcome the two limitations of CPD. A structure descriptor, such as shape context, is used to perform the auxiliary calculation of the correspondence, and the proportion of each GMM component is adjusted by the similarity. The outlier ratio is formulated in the EM framework so that it can be automatically calculated and optimized iteratively. The experimental results on both synthetic data and real data demonstrate that the proposed method described here is more robust to deformation, noise, occlusion, and outliers than CPD and other state-of-the-art algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust CPD Algorithm for Non-Rigid Point Set Registration Based on Structure Information

Peng

Xiao

et al. 2016

PLoS ONE

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“…Mismatches are filtered by using additional constraints in the second stage. Some methods use global information as constraints, which can be divided roughly into two main categories, say parameter estimation methods [11], [35], [36] and nonparametric interpolation methods [37]- [39]. The random sample consensus (RANSAC) algorithm [40] is a typical representative method of parameter estimation methods.…”

Section: Related Workmentioning

confidence: 99%

A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints

Xiao

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, we propose an effective method for remote sensing image registration. Point features are robust to remote sensing images with low quality, small overlapping area, and local deformation. Therefore, we extract point features from remote sensing images and convert the problem of remote sensing image registration into the problem of feature point matching. A correspondence set constructed solely on the similar of features often contains many false correspondences or outliers, so our key idea is to remove the mismatches in the initial correspondence set and obtain a stable correspondence through a two-step strategy. First, we use two constraints to construct the optimization model which can solve in linear time. The first constraint is that the topology of the points and their neighbors can be maintained after the spatial transformation. Another constraint is that the feature distance of the correct matches are similar to the neighbors. Then, we design a strategy to increase the number of inliers and raise the precision by a global constraint calculated from the solution in the previous step. Experiments on a variety of remote sensing image datasets demonstrate that our method is more robust and accurate than state-of-the-art methods.

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“…However, for non‐rigid shapes, the local structures among neighbouring points are also strong and stable. To capture the spatially asymmetric distribution of point set, Wang et al [13, 14] used a mixture of the asymmetric Gaussian model to represent each point set. For better use of the local structure of the point sets, Belongie et al [15] proposed a feature descriptor called shape context.…”

Section: Introductionmentioning

confidence: 99%

Non‐rigid point set registration by high‐dimensional representation

2018

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Non‐rigid point set registration is a key component in many computer vision and pattern recognition tasks. In this study, the authors propose a robust non‐rigid point set registration method based on high‐dimensional representation. Their central idea is to map the point sets into a high‐dimensional space by integrating the relative structure information into the coordinates of points. On the one hand, the point set registration is formulated as the estimation of a mixture of densities in high‐dimensional space. On the other hand, the relative distances are used to compute the local features which assign the membership probabilities of the mixture model. The proposed model captures discriminative relative information and enables to preserve both global and local structures of the point set during matching. Extensive experiments on both synthesised and real data demonstrate that the proposed method outperforms the state‐of‐the‐art methods under various types of distortions, especially for the deformation and rotation degradations.

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A robust non-rigid point set registration method based on asymmetric gaussian representation

Cited by 50 publications

References 31 publications

Robust CPD Algorithm for Non-Rigid Point Set Registration Based on Structure Information

Robust CPD Algorithm for Non-Rigid Point Set Registration Based on Structure Information

A Two-Step Method for Remote Sensing Images Registration Based on Local and Global Constraints

Non‐rigid point set registration by high‐dimensional representation

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