Professor Xue-cheng Tai

Head and Professor
Department of Mathematics
Faculty of Science

Contact

(852) 3411 7336
xuechengtai@hkbu.edu.hk

About

Professor Tai Xue-cheng’s research has been focused on numerical mathematics and computational mathematics. In recent years, he has worked mainly on image processing, data analysis and machine learning problems. He is using numerical partial differential techniques for the application of image processing and data classification and extended these techniques to other modern applications. Robust and accurate models and fast, stable algorithms are some of the main concerns in his research.

Achievements

Prize winner for the 8th “Feng-Kang prize for scientiﬁc computing 2009”
Nanyang Award for Research Excellence, Nanyang Technological University, Singapore (2001)

Research Outputs

Deng, L. J., M. Feng & X. C. Tai. “The fusion of panchromatic and multispectral remote sensing images via tensor-based sparse modeling and hyper-Laplacian prior.” Information Fusion 52 (2019): 76-89. https://doi.org/10.1016/j.inffus.2018.11.014
Deng, L. J., R. Glowinski & X. C. Tai. “A New Operator Splitting Method for the Euler Elastica Model for Image Smoothing.” SIAM Journal on Imaging Sciences 12.2 (2019): 1190-1230. https://doi.org/10.1137/18M1226361
Yan, S., J. Liu, H. Huang & X. C. Tai. “A dual EM algorithm for TV regularized Gaussian mixture model in image segmentation.” Inverse Problems & Imaging 13.3 (2019): 653-677. http://dx.doi.org/10.3934/ipi.2019030
Shi, Y., K. Yin, X. C. Tai, H. DeMirci, A. Hosseinizadeh, B. Hogue, H. Li, A. Ourmazd, P. Schwander, I. A. Vartanyants, C. H. Yoon, A. Aquila & H. Liu. “Evaluation of the performance of classification algorithms for XFEL single-particle imaging data.” IUCrJ 6 (2019): 331-340.https://doi.org/10.1107/S2052252519001854
He, X., W. Zhu & X. C. Tai. “Segmentation by Elastica Energy with L1 and L2 Curvatures: a Performance Comparison.” Numerical Mathematics-Theory Methods and Applications 12.1 (2019): 285-311. https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.4208%2Fnmtma.OA-2017-0051

Show All

Huo, L., S. Luo, Y. Dong, X. C. Tai & Y. Wang. “An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data.” Scale Space and Variational Methods in Computer Vision 11603 (2019): 144-155. https://link.springer.com/chapter/10.1007/978-3-030-22368-7_12
Li, L. F., S. Luo, X. C. Tai & J. Yang. “A Variational Convex Hull Algorithm.” Scale Space and Variational Methods in Computer Vision 11603 (2019): 224-235. https://link.springer.com/chapter/10.1007%2F978-3-030-22368-7_18
Boyd, Z. M., E. Bae, X. C. Tai & A. L. Bertozzi. “Simplified energy landscape for modularity using total variation.” SIAM Journal on Applied Mathematics 78.5 (2018): 2439-2464. https://doi.org/10.1137/17M1138972
Liu, Z., S. Wali, Y. Duan, H. Chang, C. Wu & X. C. Tai. “Proximal ADMM for Euler's elastica based image decomposition model.” Numerical Mathematics Theory Methods and Applications 12.2 (2018): 370-402. https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.4208%2Fnmtma.OA-2017-0149
Kimmel, Ron & Xue-cheng Tai. Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 1. The Netherlands: Elsevier, 2018.https://www.elsevier.com/books/processing-analyzing-and-learning-of-images-shapes-and-forms-part-1/kimmel/978-0-444-64205-9
Tai, Xue-cheng, Egil Bae & Marius Lysaker. Imaging, Vision and Learning Based on Optimization and PDEs. IVLOPDE, Bergen, Norway, August 29-September 2, 2016. Berlin: Springer, 2018. https://www.springer.com/gp/book/9783319912738
Wei, Ke, Ke Yin, Xue-cheng Tai & Tony F. Chan. “New region force for variational models in image segmentation and high dimensional data clustering.” Annals of Mathematical Sciences and Applications 3.1 (2018): 255-286. https://dx.doi.org/10.4310/AMSA.2018.v3.n1.a8
Yin, Ke & Xue-cheng Tai. “An effective region force for some variational models for learning and clustering.” Journal of Scientific Computing 74.1 (2018): 175-196. https://link.springer.com/article/10.1007/s10915-017-0429-4
Chan, Hei-long, Shi Yan, Lok-ming Lui & Xue-cheng Tai. “Topology-Preserving Image Segmentation by Beltrami Representation of Shapes.” Journal of Mathematical Imaging and Vision 60.3 (2018): 401-421.https://link.springer.com/article/10.1007/s10851-017-0767-8
Wei, K., K. Yin, X. C. Tai & T. F. Chan. “New region force for variational models in image segmentation and high dimensional data clustering.” Annals of Mathematical Sciences and Applications 3.1 (2018): 255-286. https://arxiv.org/pdf/1704.08218.pdf
Bae, ZhuEgil, Xue-cheng Tai & Wei Zhu. “Augmented lagrangian method for an euler's elastica based segmentation model that promotes convex contours.” Inverse Problems & Imaging 11.1 (2017): 1-12.http://dx.doi.org/10.3934/ipi.2017001
Tai, Xue-cheng & Jinming Duan. “A simple fast algorithm for minimization of the elastica energy combining binary and level set representations.” International Journal of Numerical Analysis and Modeling 14.6 (2017): 809-821. http://www.math.ualberta.ca/ijnam/Volume-14-2017/No-6-17/2017-06-01.pdf
Yuan, Jing, Ke Yin, Yi-guang Bai, Xiang-chu Feng & Xue-cheng Tai. “Bregman-Proximal Augmented Lagrangian Approach to Multiphase Image Segmentation.” International Conference on Scale Space and Variational Methods in Computer Vision (2017): 524-534. https://link.springer.com/chapter/10.1007/978-3-319-58771-4_42
Bae, E., X. C. Tai & W. Zhu. “Augmented lagrangian method for an euler's elastic based segmentation model that promotes convex contours.” Inverse Problems and Imaging 11.1 (2017): 1-23.http://www.aimsciences.org/article/doi/10.3934/ipi.2017001
Tai, Xue-cheng & Jinming Duan. “A simple fast algorithm for minimization of the elastica energy combing binary and level set representations.” International Journal of Numerical Analysis and Modelling (2017). http://www.math.ualberta.ca/ijnam/Volume-14-2017/No-6-17/2017-06-01.pdf
Yin, K. & X. C. Tai. “An effective region force for some variational models for learning and clustering.” Journal of Scientific Computing (2017): 1-21. https://link.springer.com/article/10.1007/s10915-017-0429-4
Boyd, Z., E. Bae, X. C. Tai & A. L. Bertozzi. “Simplified Energy Landscape for Modularity Using Total Variation.” SIAM Journal on Applied Mathematics 78.5 (2017): 2439-2464. https://arxiv.org/pdf/1707.09285.pdf
Yuan, J., K. Yin, Y. G. Bai, X. C. Feng & X. C. Tai. “Bregman-Proximal Augmented Lagrangian Approach to Multiphase Image Segmentation.” Scale Space and Variational Methods in Computer Vision: 6th International Conference (2017): 524-534.https://link.springer.com/chapter/10.1007/978-3-319-58771-4_42