Objective assessment in long-term rehabilitation under real-life recording conditions is a challenging task. We propose a data-driven method to evaluate changes in motor function under uncontrolled, long-term conditions with the low-cost Microsoft Kinect Sensor. Instead of using human ratings as ground truth data, we propose kinematic features of hand motion, healthy reference trajectories derived by principal component regression, and methods from machine learning to analyze the progression of motor function. We demonstrate the capability of this approach on datasets with repetitive unrestrained bi-manual drumming movements in 3-dimensional space of stroke survivors, patients suffering of Parkinson's disease, and a healthy control group. We present processing steps to eliminate the influence of varying recording setups under real-life conditions and offer visualization methods to support clinicians in the evaluation of treatment effects.

Archives and museums store vast collections of historical images of urban areas and make them publicly available through online platforms. Many of these images, often containing historic buildings and landscapes, can be oriented spatially using automatic methods such as structure from motion (SfM). Providing spatially and temporally oriented images of urban architecture, in combination with advanced searching and 2D/3D exploration techniques, offers new potentials in supporting historians in their research. We are developing a 3D web environment usable to historians to spatially search online media repositories containing historic photographic images. We combine 3D models of historic buildings with spatially oriented images, replacing text-based searching through meta-data with spatial and temporal browsing with respect to given focus points in historic city models.

The ongoing digitization of historical photographs in archives allows investigating the quality, quantity, and distribution of these images. However, the exact interior and exterior camera orientations of these photographs are usually lost during the digitization process. The proposed method uses content-based image retrieval (CBIR) to filter exterior images of single buildings in combination with metadata information. The retrieved photographs are automatically processed in an adapted structure-from-motion (SfM) pipeline to determine the camera parameters. In an interactive georeferencing process, the calculated camera positions are transferred into a global coordinate system. As all image and camera data are efficiently stored in the proposed 4D database, they can be conveniently accessed afterward to georeference newly digitized images by using photogrammetric triangulation and spatial resection. The results show that the CBIR and the subsequent SfM are robust methods for various kinds of buildings and different quantity of data. The absolute accuracy of the camera positions after georeferencing lies in the range of a few meters likely introduced by the inaccurate LOD2 models used for transformation. The proposed photogrammetric method, the database structure, and the 4D visualization interface enable adding historical urban photographs and 3D models from other locations.

The correct simulation and visualisation of textiles comprises many research fields such as mathematics, physics, materials science, and computer graphics. It consists of modelling the physical behaviour of textiles based on real data of garment patterns which have to be placed around an avatar. Therefore efficient numerical algorithms have to be developed to solve the appearing differential equations. Finally the obtained results are displayed photorealistically). The aim of the national research project "Virtual Try-On" consists in providing a prototype of a virtual try-on scenario in a real shop or the internet realizing cost-efficient made-tomeasure wear. In this article we present the whole process chain from the choice of clothes up to the final visualisation and the individual evaluation of fit.

In this work, we present virtual tailor tools which allow the interactive design and modification of clothing in a 3D Virtual Environment. In particular, we propose algorithms and interaction techniques for sewing and cutting garments during a physical cloth simulation, including the automatic modification of the underlying planar cloth patterns.

We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction and baseline adjustment. We incorporate recent advances in feature matching, energy minimization, stereo vision and data clustering into our approach. At the core of our correspondence estimation we use Efficient Belief Propagation for energy minimization. While state-of-the-art algorithms only work on thumbnail-sized images, our novel feature downsampling scheme in combination with a simple, yet efficient data term compression can cope with high-resolution data. The incorporation of SIFT features into data term computation further resolves matching ambiguities, making long-range correspondence estimation possible. We detect occluded areas by evaluating the correspondence symmetry, we further apply Geodesic matting to automatically in paint these regions.