Formation of Morphable 3D­model of Large Scale Natural Sites by Using Image Based Modeling and Rendering Techniques

No global 3D model of the environment needs to be assembled, a process which can be extremely cumbersome and error prone for large scale scenes e.g. the global registration of multiple local models can accumulate a great amount of error, while it also presumes a very accurate extraction of the underlying geometry. On the contrary, neither any such accurate geometric reconstruction of the individual local 3D models nor a very precise registration between them is required by our framework in order that it can produce satisfactory results. This paper presents an application of LP based MRF optimization techniques and also we have turned our attention to a different re­ search topic: the proposal of novel image based modeling and rendering methods, which are capable of automatically reproducing faithful (i.e. photorealistic) digital copies of complex 3D virtual environments, while also allowing the virtual exploration of these environments at interactive frame rates

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

Applications of two dimensional multiscale stochastic models Mark R. Luettgen.

Caption title.Includes bibliographical references (p. 33-34).Supported by AFOSR. AFOSR-88-0032 Supported by NSF. MIP-9015281 INT-9002393 Supported by ONR. N00014-91-J-100

Learning the temporal evolution of multivariate densities via normalizing flows

In this work, we propose a method to learn multivariate probability distributions using sample path data from stochastic differential equations. Specifically, we consider temporally evolving probability distributions (e.g., those produced by integrating local or nonlocal Fokker-Planck equations). We analyze this evolution through machine learning assisted construction of a time-dependent mapping that takes a reference distribution (say, a Gaussian) to each and every instance of our evolving distribution. If the reference distribution is the initial condition of a Fokker-Planck equation, what we learn is the time-T map of the corresponding solution. Specifically, the learned map is a multivariate normalizing flow that deforms the support of the reference density to the support of each and every density snapshot in time. We demonstrate that this approach can approximate probability density function evolutions in time from observed sampled data for systems driven by both Brownian and L\'evy noise. We present examples with two- and three-dimensional, uni- and multimodal distributions to validate the method