Professor Xue-cheng Tai
Head and Professor
Department of Mathematics
Faculty of Science
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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
- 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