Robust Generalized Point Cloud Registration with Expectation Maximization Considering Anisotropic Positional Uncertainties

In medical image analysis (MIA) and computerassisted surgery (CAS), aligning two multiple point sets (PSs) together is an essential but also a challenging problem. For example, rigidly aligning multiple point sets into one common coordinate frame is a prerequisite for statistical shape modelling (SSM). Accurately aligning the pre-operative space with the intraoperative space in CAS is very crucial to successful interventions. In this article, we formally formulate the multiple generalized point set registration problem (MGPSR) in a probabilistic manner, where both the positional and the normal vectors are used. The six-dimensional vectors consisting of both positional and normal vectors are called as generalized points. In the formulated model, all the generalized PSs to be registered are considered to be the realizations of underlying unknown hybrid mixture models (HMMs). By assuming the independence of the positional and orientational vectors (i.e., the normal vectors), the probability density function (PDF) of an observed generalized point is computed as the product of Gaussian and Fisher distributions. Furthermore, to consider the anisotropic noise in surgical navigation, the positional error is assumed to obey a multi-variate Gaussian distribution. Finally, registering PSs is formulated as a maximum likelihood (ML) problem, and solved under the expectation maximization (EM) technique. By using more enriched information (i.e., the normal vectors), our algorithm is more robust to outliers. By treating all PSs equally, our algorithm does not bias towards any PS. To validate the Manuscript

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“…This non-linear equation of κ j has no analytical solution, and the fixed-point iteration method is used to solve the problem [60].…”

Section: M-cov Stepmentioning

confidence: 99%

Joint Rigid Registration of Multiple Generalized Point Sets With Anisotropic Positional Uncertainties in Image-Guided Surgery

Min

Wang

Meng

2022

IEEE Trans. Automat. Sci. Eng.

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“…Then Z. Min el at. [33]- [36] continued to improve the experimental results by optimizing parameters in this methodology. D. Zhu el at.…”

Section: B Removal Of Perspective Distortionsmentioning

confidence: 99%

Autonomous Removal of Perspective Distortion of Elevator Button Images based on Corner Detection

Ma¹,

Liu²,

Zhu³

2020

Preprint

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Elevator button recognition is an important function to realize the autonomous operation of elevators. However, challenging image conditions and various image distortions make it difficult to accurately recognize buttons. In this work, We propose a novel algorithm that can automatically correct perspective distortions of elevator panel images based on button corner detection results. The algorithm first leverages DeepLabv3+ model and Hough Transform method to obtain button segmentation results and button corner detection results, then utilizes pixel coordinates of standard button corners as reference features to estimate camera motions for correcting perspective distortions. The algorithm is much more robust to outliers and noise on the removal of perspective distortion than traditional geometric approaches as it only performs on a single image autonomously. 15 elevator panel images are captured from different angles of view as the dataset. The experimental results show that our approach significantly outperforms traditional geometric techniques in accuracy and robustness. Rectification results of the proposed algorithm is 77.4% better than the results of traditional geometric algorithm in average.

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“…where p(z) ∈ {π 1 , · · · , π k } represents a discrete distribution over the hidden variable z, which is a prior that directs the mixture process of Gaussian distributions. Following the conventions in point set registration literature [19], p(z) is assumed to be a uniform distribution, i.e. π k = 1/K, in this work.…”

Section: Problem Formalizationmentioning

confidence: 99%

“…In [18], an encoded pattern is leveraged to help estimate the rigid-body transformations between the target and robot, which is actually a similar idea with this work, except that the reference points are encoded in advance there, whereas we have to estimate these points in this work. From a technical view, the methodologies used in this work share many similarities with point set registration [19]- [22] and the main difference lies in the parameters that need to be optimized. Point set registration focuses on solving two key problems: point correspondences and parameter optimization [23], which are also the cores of grid fitting algorithm.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Removal of Perspective Distortion for Robotic Elevator Button Recognition

Zhu

Liu

et al. 2019

2019 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Elevator button recognition is considered an indispensable function for enabling the autonomous elevator operation of mobile robots. However, due to unfavorable image conditions and various image distortions, the recognition accuracy remains to be improved. In this paper, we present a novel algorithm that can autonomously correct perspective distortions of elevator panel images. The algorithm first leverages the Gaussian Mixture Model (GMM) to conduct a grid fitting process based on button recognition results, then utilizes the estimated grid centers as reference features to estimate camera motions for correcting perspective distortions. The algorithm performs on a single image autonomously and does not need explicit feature detection or feature matching procedure, which is much more robust to noises and outliers than traditional feature-based geometric approaches. To verify the effectiveness of the algorithm, we collect an elevator panel dataset of 50 images captured from different angles of view. Experimental results show that the proposed algorithm can accurately estimate camera motions and effectively remove perspective distortions.

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Robust Generalized Point Cloud Registration with Expectation Maximization Considering Anisotropic Positional Uncertainties

Cited by 26 publications

References 19 publications

Joint Rigid Registration of Multiple Generalized Point Sets With Anisotropic Positional Uncertainties in Image-Guided Surgery

Joint Rigid Registration of Multiple Generalized Point Sets With Anisotropic Positional Uncertainties in Image-Guided Surgery

Autonomous Removal of Perspective Distortion of Elevator Button Images based on Corner Detection

Autonomous Removal of Perspective Distortion for Robotic Elevator Button Recognition

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