Book on Computer Vision with Uncertain Data


Together with Professor Mike Kirby from the University of Utah, Fraunhofer MEVIS researchers Professor Tobias Preusser and Dr. Torben Pätz jointly authored a book titled „Stochastic Partial Differential Equations for Computer Vision with Uncertain Data“ published in July 2017.

The goal of this 160-pages book is to introduce the reader to the recent advances from the field of uncertainty quantification and error propagation for computer vision, image processing, and image analysis that are based on partial differential equations (PDEs). It presents a concept with which error propagation and sensitivity analysis can be formulated with a set of basic operations. The approach discussed in this book has the potential for application in all areas of quantitative computer vision, image processing, and image analysis. In particular, it might help medical imaging finally become a scientific discipline that is characterized by the classical paradigms of observation, measurement, and error awareness.

In image processing and computer vision applications such as medical or scientific image data analysis, as well as in industrial scenarios, images are used as input measurement data. It is a good scientific practice that proper measurements must be equipped with error and uncertainty estimates. For many applications, not only the measured values but also their errors and uncertainties should be – and more and more frequently are – taken into account for further processing. This error and uncertainty propagation must be done for every processing step such that the final result comes with a reliable precision estimate.