Vision Journal Club, Apr 17, 2013, 03:00PM - 04:00PM, Wachman 1015D
High Resolution Registration, Tracking, and Synthesis of 3D Deforming Objects
Dr Meizhu Liu, Siemens Corporate Research
A robust divergence measure is fundamental in both theoretical and practical applications. Dr. Liu proposed a new robust class of divergences, termed by total Bregman divergence (tBD). A series of theoretical results for this new divergence are established. In particular, the L1-norm tBD induces a closed form center for a set of samples. This center is automatically adjusted for noisy data and outliers. Thus, tBD and the t-centers are statistically more robust than the conventional divergences and their induced centers. The effectiveness of tBD has been validated in many real applications. For instance, a hierarchical shape retrieval scheme has been developed to integrate Gaussian mixture model based shape representation and tBD hard/soft clustering. tBD and t-center were also applied to clinical diffusion tensor imaging (DTI) problems for image analysis, including DTI signal estimation, interpolation and segmentation, and fiber clustering etc. In addition, tBD is used to regularize the conventional boosting and metric learning algorithms for classification. It has been shown that tBD can enhance the robustness and improve the accuracy of these algorithms, and reduce their computational complexity.
Dr. Meizhu Liu is a Research Scientist at Siemens Corporation, Corporate Research (SCR). She received her Ph.D. degree in Department of Computer Information Science and Engineering from University of Florida in 2011. She received her B.S. degree from University of Science and Technology of China in 2007. Her research focuses on exploring computer vision and machine learning techniques for vision and medical image applications. She has worked at Siemens Medical Solutions USA, and INRIA in France.