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Publications
Meizhu Liu, Baba C. Vemuri, Shun-Ichi Amari and Frank Nielsen, “Shape Retrieval Using Hierarchical Total
Bregman Soft Clustering”, Transactions on Pattern Analysis and Machine Intelligence (TPAMI’12), to appear, 2012. [PDF]
Meizhu Liu, Baba C. Vemuri, and Rachid Deriche, “Unsupervised Automatic White Matter Fiber
Clustering Using a Gaussian Mixture Model”, IEEE International Symposium on Biomedical Imaging
(ISBI’12), to appear, 2012.
Frank Nielsen, Meizhu Liu, and Baba C. Vemuri, “Divergence-Based Means of Symmetric Positive
Definite Matrices”, Book Chapter, Matrix Information Geometry,to appear, 2012.
Meizhu Liu, Le Lu, Xiaojing Ye, Shipeng Yu, and Heng Huang “Coarse-to-fine Classification Using Parametric
and Nonparametric Models for Computer-Aided Diagnosis”,
20th ACM Conference on Information and Knowledge Management (CIKM’11), pp. 2509-2512, 2011. [PDF]
Meizhu Liu, Baba C. Vemuri, and Rachid Deriche, “Simultaneous Smoothing &
Estimation of DTI via Robust Variational Non-local Means”, MICCAI 2011 Workshop on
Computational Diffusion MRI (CDMRI), 2011. [PDF]
Meizhu Liu, Kefei Liu, and Xiaojing Ye, “Find the Intrinsic Space for Multiclass Classification”,
ACM Proceedings of the International Symposium on Applied Sciences in Biomedical and Communication Technologies (ACM ISABEL), 2011.
Xiaojing Ye, Kefei Liu, and Meizhu Liu, “Efficient Minimization for Dictionary Based Sparse Representation and Signal Recovery”, ACM Proceedings of the International Symposium on Applied Sciences in Biomedical and Communication Technologies (ACM ISABEL), 2011.
Francisco Escolano Ruiz, Meizhu Liu, and Edwin Hancock,
“Tensor-based Total Bregman Divergences between Graphs”,
IEEE Workshop on Information Theory in
Computer Vision and Pattern Recognition Joint with International Conference on Computer Vision (ICCV’11), 2011. [PDF]
Meizhu Liu, Le Lu, Jinbo Bi, Vikas Raykar, Matthias Wolf, Marcos Salganicoff, “Robust Large Scale Prone-Supine Polyp Matching Using Local Features: A Metric Learning Approach”, 14th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI’11), Vol. 6893, pp. 73-80, 2011. [PDF]
Meizhu Liu, Le Lu, Xiaojing Ye, Shipeng Yu, Marcos Salganicoff, “Sparse Classification for Computer Aided Diagnosis Using Learned Dictionaries”, 14th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI’11), Vol. 6893, pp. 41-48, 2011. [PDF]
Meizhu Liu, Baba C Vemuri, “Robust and Efficient Regularized Boosting Using Total Bregman Divergence”, IEEE Proceedings of the 24th Conference on Computer Vision and Pattern Recognition (CVPR’11), pp. 2897-2902, 2011. [PDF]
Jinbo Bi, Dijia Wu, Le Lu, Meizhu Liu, Yimo Tao, Matthias Wolf, “AdaBoost on Low-Rank PSD Matrices for Metric Learning with Applications in Computer Aided Diagnosis”, IEEE Proceedings of the 24th Conference on Computer Vision and Pattern Recognition (CVPR’11), pp. 1049-1056, 2011. [PDF]
Meizhu Liu and Baba C. Vemuri, “RBOOST: Riemannian Distance based Regularized Boosting”, IEEE International Symposium on Biomedical Imaging (ISBI’11), pp. 1831-1834, 2011. [PDF]
Baba C. Vemuri, Meizhu Liu, Shun-Ichi Amari and Frank Nielsen, “Total Bregman Divergence and its Applications to DTI Analysis”,
IEEE Transactions on Medical Imaging (TMI’10), Vol. 30, No. 2, pp. 475-483, 2010. [PDF]
Meizhu Liu, Baba C. Vemuri, Shun-Ichi Amari and Frank Nielsen, “Total Bregman Divergence and its Applications to Shape Retrieval”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR’10), pp. 3463-3468, 2010. [PDF]
Kefei Liu, Meizhu Liu, Zhihua Huang, Linbing Zhao and Youyu Shen, “Error Estimate of Quadra-
ture Sampling System via Discrete Hilbert Transform”, International Conference on Signal
Processing Systems (ICSPS’10), pp. 10-13, 2010.(oral) [PDF]
Kefei Liu, Shangwei Zhao, and Meizhu Liu, “Solution of a Class of Minimal Surface Problem with
Obstacle”, Journal of Mathematics Research (JMR’09), Vol. 1, No. 1, pp. 39-45, 2009. [PDF]
Thesis
Meizhu Liu, “Total Bregman Divergence, A Robust Divergence and its Applications”, Ph.D. Thesis,
University of Florida, USA, 2011.
Meizhu Liu, “Vision, Self-localization and Decision-making of Robots”, B.S. Thesis,
University of Science and Technology of China (USTC), China, 2007.
Patents
Meizhu Liu, Le Lu, Vikas Chandrakant Raykar, Salganicoff Marcos, “Systems and Methods
for Metric Learning based Polyp Prone/Supine View Matching to Improve
CAD Performance”, United States Patent Application, Application Serial No. 13/267,095.
Meizhu Liu, Le Lu, Matthias Wolf, Salganicoff Marcos,
“Coarse-to-Fine Classification using Parametric and
Non-parametric Models for Computer-Aided Diagnosis”, United States patent, filed, 08/2010.
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