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聯絡
- (852) 3411 7336
- xuechengtai@hkbu.edu.hk
簡歷
台雪成教授研究主要集中在數值數學和計算數學。近年來,他主要從事圖像處理、數據分析和機器學習問題。他使用偏微分方程的數值方法和技巧來解決圖像處理和數據分類方面的問題,並推廣這些技術到其他現代應用。穩健、準確的模型和快速、穩定的演算算法是他研究的一些主要問題。
成就
- 第八屆"馮康科學計算獎"2009年獲獎
新加坡南洋理工大學南洋研究卓越獎 (2001年)
出版
- 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