Robust two-phase registration method for three-dimensional point set under the Bayesian mixture framework-Reference-Cited by-同舟云学术

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Robust two-phase registration method for three-dimensional point set under the Bayesian mixture framework

Published:2022-10-30 Issue: Volume: Page:
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Yang Lijuan^ORCID,Ji Nannan,Wang Changpeng,Wu Tianjun,Li Fuxiao

Funder

Fundamental Research Funds for the Central Universities, CHD

National Nature Science Foundation of China

Key Research and Development Program of Shaanxi

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-022-01673-w.pdf

Reference37 articles.

1. Beal MJ (2003) Variational algorithms for approximate Bayesian inference. Dissertation, University of London

2. Bing J, Vemuri BC (2011) Robust point set registration using Gaussian mixture models. IEEE Trans Pattern Anal Mach Intell 33(8):1633–1645. https://doi.org/10.1109/TPAMI.2010.223

3. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

4. Chui HL, Rangarajan A (2003) A new point matching algorithm for non-rigid registration. Comput Vis Image Understand 89(2–3):114–141. https://doi.org/10.1016/S1077-3142(03)00009-2

5. Fan AX, Jiang XY, Ma Y, Mei XG, Ma JY (2021) Smoothness-driven consensus based on compact representation for robust feature matching. IEEE Trans Neural Netw Learn Syst 99:1–1. https://doi.org/10.1109/TNNLS.2021.3118409

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1. Correction to: robust two-phase registration method for three-dimensional point set under the bayesian mixture framework;International Journal of Machine Learning and Cybernetics;2023-04-05

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