Robust rigid registration algorithm based on pointwise correspondence and correntropy

Du, Shaoyi; Xu, Guanglin; Zhang, Sirui; Zhang, Xuetao; Gao, Yue; Chen, Badong

doi:10.1016/j.patrec.2018.06.028

Cited by 73 publications

(27 citation statements)

References 16 publications

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“…To improve the registration procedure, the use of automatic co-registration algorithms seems to be a good solution. Such approach could make registration faster and much more accurate (20)(21)(22).…”

Section: Discussionmentioning

confidence: 99%

Data processing of 3D and 4D in-vivo electron paramagnetic resonance imaging co-registered with ultrasound. 3D printing as a registration tool

Gonet

Epel

Elas

2019

Computers & Electrical Engineering

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We present the concept of image registration using ultrasound (US) and electron paramagnetic resonance (EPR) imaging and discuss the benefits of this solution, as well as its limitations. Both phantoms and murine tumors were used to test US and EPR image co-registration. Comparison of dental molding cast immobilization and predesigned cradle revealed that the latter approach is more effective in stabilizing the fiducial position. In vivo imaging of mouse tumors, image registration and comparison of fiducials system for 3D spatial as well as 4D spatial-spectral EPR imaging supported by 3D US were demonstrated. Ultrasound may provide a convenient alternative to other anatomical imaging methods for image registration in preclinical research. Of particular interest is a fusion of US tissue structure, doppler vascular function and EPR oxygen or redox imaging.

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Section: Discussionmentioning

confidence: 99%

Data processing of 3D and 4D in-vivo electron paramagnetic resonance imaging co-registered with ultrasound. 3D printing as a registration tool

Gonet

Epel

Elas

2019

Computers & Electrical Engineering

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“…As a result, this plane-to-plane ICP method is more stable and robust for measurement noise. In general, as mentioned in [23], ICP and its variants perform well in ideal cases [24]- [26].…”

Section: Introductionmentioning

confidence: 85%

An Accelerated and Robust Partial Registration Algorithm for Point Clouds

et al. 2020

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Partial registration for point clouds plays an important role in various fields such as 3D mapping reconstruction, remote sensing, unmanned driving, and cultural heritage protection. Unfortunately, partial registration is challenging due to difficulties such as the low overlap ratio of two point clouds and the perturbation in the orderless and sparse 3D point clouds. Thus, a variety of the 3D shape context descriptors are introduced for finding the optimal matching. However, extracting geometric features and descriptors are time consuming and easily degenerated by noise. To overcome these problems, we introduce a parallel coarse-to-fine partial registration method. Our contributions can be summarized as: Firstly, a robust coarse trimmed method is proposed to estimate the coarse overlap area and the initial transformation via fast bilateral denoising and parallel point feature histogram (PPFH) descriptor aligning. Secondly, an accelerated fine registration procedure is conducted by a parallel trimmed iterative closest point (PTrICP) method. Moreover, most parts of our coarse-to-fine workflow are accelerated under the Graphics Processing Unit (GPU) parallel execution mode for efficiency. Thirdly, we extend our method from the rigid registration to the isotropic scaling registration, which improves its applicability. Experiments have demonstrated that our method is feasible and robust in various situations, including the low overlap ratio, outlier, noise and scaling.

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“…Then, Peng et al [19], Dong et al [20] extended this trimmed strategy to the nonrigid transformation. Du et al [21], [22] proposed an ICP algorithm based on the probability or correntropy for precise registration with outliers and noise. Empirically, these methods can significantly improve the performance in a variety of noise degenerations.…”

Section: Introductionmentioning

confidence: 99%

A Coarse-to-Fine Generalized-ICP Algorithm With Trimmed Strategy

Wang

Peng

et al. 2020

IEEE Access

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In this paper, we introduce a modified Generalized Iterative Closest Point (GICP) algorithm by presenting a coarse-to-fine strategy. Our contributions can be summarized as: Firstly, we use adaptively a plane-to-plane probabilistic matching model by gradually reducing the neighborhood range for given two point sets. It is an inner coarse-to-fine iteration process. Secondly, we use an outer coarse-to-fine strategy to bridge the point-to-point and plane-to-plane registration for refining the matching. Thirdly, we use the trimmed method to gradually eliminate the effects of incorrect correspondences, which improves the robustness of the methods especially for the low overlap cases. Moreover, we also extend our method to the scale registration case. Finally, we conduct extensive experiments to demonstrate that our method is more reliable and robust in various situations, including missing points, noise and different scale factors. Experimental results show that our approach outperforms several state-of-the-art registration methods. INDEX TERMS Registration, modified GICP, trimmed method.

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Robust rigid registration algorithm based on pointwise correspondence and correntropy

Cited by 73 publications

References 16 publications

Data processing of 3D and 4D in-vivo electron paramagnetic resonance imaging co-registered with ultrasound. 3D printing as a registration tool

Data processing of 3D and 4D in-vivo electron paramagnetic resonance imaging co-registered with ultrasound. 3D printing as a registration tool

An Accelerated and Robust Partial Registration Algorithm for Point Clouds

A Coarse-to-Fine Generalized-ICP Algorithm With Trimmed Strategy

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