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Computational & Applied Math Seminar

Two-phase segmentation for intensity inhomogeneous images by the Allen-Cahn Local Binary Fitting Model

  • 演讲者:乔中华(香港理工大学)

  • 时间:2021-10-19 10:00-11:00

  • 地点:Zoom ID: 939 5891 1735 Passcode: 565324

讲座摘要

We propose a new variational model by integrating the Allen-Cahn term with a local binary fitting energy term for segmenting images with intensity inhomogeneity and noise. An inhomogeneous graph Laplacian initialization method (IGLIM) is developed to give the initial contour for two-phase image segmentation problems. To solve the Allen-Cahn equation derived from the variational model, we adopt the exponential time differencing (ETD) method for temporal discretization, and the central finite difference method for spatial discretization. The energy stability of proposed numerical schemes can be proved.  Experiments on various images demonstrate the necessity and superiority of proper initialization and verify the capability of our model for two-phase segmentation of images with intensity inhomogeneity and noise. 


个人简介
乔中华,香港理工大学教授。2006 年在香港浸会大学获得博士学位。主要从事数值微分方程方面算法设计及分析,特别是相场方程的数值模拟及计算流体力学的高效算法。至今在 SIAM Rev、SIAM J. Numer. Anal、SIAM J. Sci Comp、Numer Math、Math Comp、 J. Comp Phys 等计算数学期刊上发表学术论文 60 余篇,文章被合计引用 1000 余次。2013 年获香港研究资助局颁发的杰出青年学者奖,2018年获得香港数学会青年学者奖,2020 年获得香港研究资助局研究学者奖。