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理學院青年學術論壇第198期——Improved Optimization Methods for Image Registration Problems

發布者: [發表時間]:2019-03-29 [來源]: [瀏覽次數]:

主講人:Geovani Nunes Grapiglia博士




報告題目:Improved Optimization Methods for Image Registration Problems


In many applications, multiple images of the same subject are obtained at different moments in time and under different conditions. The goal is to identify changes in the subject over time. For a fair comparison, the images need to be aligned in order to overlap the common features. Image Registration is the problem to find the transformation of images leading to the best overlapping. The mathematical modelling of this type of problem often results in large-scale unconstrained optimization problems to solve. The usual approach is to apply Gauss-Newton Method with Armijo line-search embedded in a multi-level coarse-to-fine scheme, where the solution of the coarse level is used to generate the initial point for the fine level. In this talk, two alternative approaches will be discussed, including the use of L-BFGS and subspace techniques.


Dr. Geovani Nunes Grapiglia got his phD degree in Mathematics in 2014 from Federal University of Parana, Brazil. He is now an assistant professor in Federal University of Parana. During the year 2013, he visited Chinese Academy of Sciences supported by the "Sandwich Doctorate Scholarship". He got the SIAM student travel grant in 2014. His research interests are nonlinear optimization theory and algorithms, particularly trust region methods, derivative-free methods, Newton's method and error bound analysis. He has published several papers on Math Programming, SIAM Optimization, and so on.