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Professor Xue-cheng Tai

Head and Professor
Department of Mathematics
Faculty of Science

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Professor Xue-cheng Tai

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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 scientiļ¬c 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