New developments and open questions in probabilistic image analysis—a Bayesian approach-大连理工大学数学科学学院(新)

New developments and open questions in probabilistic image analysis—a Bayesian approach

2018年06月11日 13:28  点击:[]

学 术 报 告

报告题目:New developments and open questions in probabilistic image analysis—a Bayesian approach

报告人:李猛  美国莱斯大学(休斯顿)统计系



校内联系人:于波 教授

报告内容:Images (2D, 3D, or even higher dimensional) are a fundamental data type. The area of image analysis is undergoing a dramatic transformation to utilize the power of statistical modeling, which provides a unique way to describe uncertainties and leads to model-based solutions. We exemplify this by two critical and challenging problems, boundary detection and image reconstruction, in a comprehensive way from theory, methodology to application. We view the boundary as a closed smooth lower-dimensional manifold, and propose a flexible Bayesian approach based on priors indexed by the unit sphere. We introduce a probabilistic model-based technique using wavelets with adaptive random partitioning to reconstruct images. We represent multidimensional signals by a mixture of one-dimensional wavelet decompositions in the form of randomized recursive partitioning on the space of wavelet coefficient trees, where the decomposition adapts to the geometric features of the signal. State-of-the-art performances of proposed methods are demonstrated using simulations and applications including neuroimaging in brain oncology. R/Matlab packages/toolboxes and interactive shiny applications are available for routine implementation. Several open questions and challenges that arise in related fields will be discussed.

报告人简介:Meng Li is Noah Harding Assistant Professor of Statistics (endowed; tenure-track) at Rice University. Previously, Dr. Li was a Visiting Assistant Professor in the Department of Statistical Science at Duke University (2015-2017). He received his PhD in statistics from North Carolina State University. Dr. Li’s research focuses on statistical modeling of challenging data that arise in scientific and industrial applications such as images, functional data, networks, and tree-structured data, with theoretical guarantees and scalable implementation. He received the Ralph E. Powe Junior Faculty Enhancement Awards (2018), Empower Partnerships with Industry (PEPI) Award by NSF South Big Data Hub (2016), and Student Paper Award in Section on Bayesian Statistical Science by JSM (2014).



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