Zhang, A;
Min, Z;
Zhang, Z;
Meng, MQH;
(2022)
Generalized Point Set Registration with Fuzzy Correspondences Based on Variational Bayesian Inference.
IEEE Transactions on Fuzzy Systems
, 30
(6)
pp. 1529-1540.
10.1109/TFUZZ.2022.3159099.
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Abstract
Point set registration (PSR) is an essential problem in surgical navigation and computer-assisted surgery (CAS). In CAS, PSR can be used to map the intra-operative surgical space with the pre-operative volumetric image space. The performances of PSR in real-world surgical scenarios are sensitive to noise and outliers. This paper proposes a novel point set registration approach where the additional features (i.e., the normal vectors) extracted from the point sets are utilized, and the convergence of the algorithm is guaranteed from the theoretical perspective. More specifically, we formulate the PSR with normal vectors by generalizing the Bayesian coherent point drift (BCPD) into the six-dimensional scenario. The proposed algorithm is more accurate and robust to noise and outliers, and the theoretical convergence of the proposed approach is guaranteed. Our contributions of this paper are summarized as follows. (1) The PSR problem with normal vectors is formally formulated through generalizing the BCPD approach; (2) The formulas for updating the parameters during the algorithm's iterations are given in closed forms; (3) Extensive experiments have been done to verify the proposed approach and specifically its significant improvements over the BCPD has been validated.
| Type: | Article |
|---|---|
| Title: | Generalized Point Set Registration with Fuzzy Correspondences Based on Variational Bayesian Inference |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TFUZZ.2022.3159099 |
| Publisher version: | http://doi.org/10.1109/TFUZZ.2022.3159099 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Artificial Intelligence, Engineering, Electrical & Electronic, Computer Science, Engineering, Convergence, Surgery, Hidden Markov models, Probabilistic logic, Bayes methods, Maximum likelihood estimation, Feature extraction, Computer-assisted surgery (CAS), fuzzy correspondence estimation, point set registration (PSR), variational Bayesian inference (VBI), MODEL, ICP |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10150632 |
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