Non‐rigid point set registration by high‐dimensional representation

Huang, Huimin; Zhou, Zhiguang; Cao, Jianzhong

doi:10.1049/iet-ipr.2017.1363

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Thus, multi-sensor image registration, which is very critical for fusion, is still a challenging task. One of the solutions is to transform image registration into point set registration, and then estimate spatial transformation model from point feature [5,11]. This paper focuses on point set registration to achieve IR and VIS image registration.…”

Section: Introductionmentioning

confidence: 99%

Non-Rigid Registration via Global to Local Transformation

Pan

Zhou

et al. 2020

Teh. vjesn.

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Non-rigid point set and image registration are key problems in plenty of computer vision and pattern recognition tasks. Typically, the non-rigid registration can be formulated as an optimization problem. However, registration accuracy is limited by local optimum. To solve this problem, we propose a method with global to local transformation for non-rigid point sets registration and it also can be used to infrared (IR) and visible (VIS) image registration. Firstly, an objective function based on Gaussian fields is designed to make a problem of non-rigid registration transform into an optimization problem. A global transformation model, which can describe the regular pattern of non-linear deformation between point sets, is then proposed to achieve coarse registration in global scale. Finally, with the results of coarse registration as initial value, a local transformation model is employed to implement fine registration by using local feature. Meanwhile, the optimal global and local transformation models estimated from edge points of IR and VIS image pairs are used to achieve non-rigid image registration. The qualitative and quantitative comparisons demonstrate that the proposed method has good performance under various types of distortions. Moreover, our method can also produce accurate results of IR and VIS image registration.

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Section: Introductionmentioning

confidence: 99%

Non-Rigid Registration via Global to Local Transformation

Pan

Zhou

et al. 2020

Teh. vjesn.

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“…In Qu et al (2017), the matching problems were assumed as a union of regression and clustering and the probabilistic mixture model was introduced to model the transformation. The PR was mapped into high-dimensional space according to the structure information in Huang et al (2018).…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Liu

2019

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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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Non‐rigid point set registration by high‐dimensional representation

Cited by 2 publications

References 23 publications

Non-Rigid Registration via Global to Local Transformation

Non-Rigid Registration via Global to Local Transformation

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

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