Stochastic Partial Differential Equations for Computer Vision with Uncertain Data

Tobias Preusser studied mathematics at the University of Bonn and at New York University. He received his Ph.D. from the University of Duisburg with a thesis on anisotropic geometric diffusion in image processing and his habilitation in Mathematics from the University of Bremen with a thesis on image-based computing. He is a professor for the mathematical modeling of biomedical processes at Jacobs University Bremen, head of the modeling and simulation group, and member of the management board at the Fraunhofer Institute for Medical Image Computing MEVIS. On the one hand, his research interests include modeling and simulation of bio-medical processes with partial differential equations (PDEs), mathematical image processing, and scientific visualization based on PDEs. On the other hand, his research is driven by concrete and complex application problems in medicine. Robert M. (Mike) Kirby received an M.S. degree in applied mathematics, an M.S. degree in computer science, and a Ph.D. degree in applied mathematics from Brown University, Providence, Rhode Island, in 1999, 2001, and 2002, respectively. He is currently a Professor of Computing and Associate Director of the School of Computing, University of Utah, Salt Lake City, where he is also an Adjunct Professor in the Departments of Bioengineering and Mathematics and a member of the Scientific Computing and Imaging Institute. His current research interests include scientific computing and visualization. Torben Patz received his diploma degree in mathematics from the University of Bremen, Germany, in 2009 with a thesis focusing on the numerical simulation of radio-frequency ablation and his Ph.D. from Jacobs University Bremen, Germany, in 2009. In his Ph.D. thesis, he focused on the segmentation of stochastic images with stochastic PDEs, laying the basis for the book at hand. He was a postdoctoral fellow at Jacobs University Bremen while writing the book and is now a research scientist at the Fraunhofer Institute for Medical Image Computing MEVIS. His research interests include uncertainty modeling and propagation in medical applications as well as software support for interventional radiology. University of California, Berkeley

Preface
Notation
Introduction
Partial Differential Equations and Their Numerics
Review of PDE-Based Image Processing
Numerics of Stochastic PDEs
Stochastic Images
Image Processing and Computer Vision with Stochastic Images
Sensitivity Analysis
Conclusions
Bibliography
Authors' Biographies