Context-based urban terrain reconstruction from uav-videos for geoinformation applications

Urban terrain reconstruction has many applications in areas of civil engineering, urban planning, surveillance and defense research. Therefore the needs of covering ad-hoc demand and performing a close-range urban terrain reconstruction with miniaturized and relatively inexpensive sensor platforms are constantly growing. Using (miniaturized) unmanned aerial vehicles, (M) UAVs, represents one of the most attractive alternatives to conventional large-scale aerial imagery. We cover in this paper a four-step procedure of obtaining georeferenced 3D urban models from video sequences. The four steps of the procedure - orientation, dense reconstruction, urban terrain modeling and geo-referencing - are robust, straight-forward, and nearly fully-automatic. The two last steps - namely, urban terrain modeling from almost-nadir videos and co-registration of models - represent the main contribution of this work and will therefore be covered with more detail. The essential substeps of the third step include digital terrain model (DTM) extraction, segregation of buildings from vegetation, as well as instantiation of building and tree models. The last step is subdivided into quasi-intrasensorial registration of Euclidean reconstructions and intersensorial registration with a geo-referenced orthophoto. Finally, we present reconstruction results from a real data-set and outline ideas for future work

Repetitive Sequence Variations in the Promoter Region of the Adhesin-Encoding Gene sabA of Helicobacter pylori Affect Transcription

The pathogenesis of diseases elicited by the gastric pathogen Helicobacter pylori is partially determined by the effectiveness of adaptation to the variably acidic environment of the host stomach. Adaptation includes appropriate adherence to the gastric epithelium via outer membrane protein adhesins such as SabA. The expression of sabA is subject to regulation via phase variation in the promoter and coding regions as well as repression by the two-component system ArsRS. In this study, we investigated the role of a homopolymeric thymine [poly(T)] tract -50 to -33 relative to the sabA transcriptional start site in H. pylori strain J99. We quantified sabA expression in H. pylori J99 by quantitative reverse transcription-PCR (RT-PCR), demonstrating significant changes in sabA expression associated with experimental manipulations of poly(T) tract length. Mimicking the length increase of this tract by adding adenines instead of thymines had similar effects, while the addition of other nucleotides failed to affect sabA expression in the same manner. We hypothesize that modification of the poly(T) tract changes DNA topology, affecting regulatory protein interaction(s) or RNA polymerase binding efficiency. Additionally, we characterized the interaction between the sabA promoter region and ArsR, a response regulator affecting sabA expression. Using recombinant ArsR in electrophoretic mobility shift assays (EMSA), we localized binding to a sequence with partial dyad symmetry -20 and +38 relative to the sabA + 1 site. The control of sabA expression by both ArsRS and phase variation at two distinct repeat regions suggests the control of sabA expression is both complex and vital to H. pylori infection

Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging

The recovery of objects obscured by scattering is an important goal in imaging and has been approached by exploiting, for example, coherence properties, ballistic photons or penetrating wavelengths. Common methods use scattered light transmitted through an occluding material, although these fail if the occluder is opaque. Light is scattered not only by transmission through objects, but also by multiple reflection from diffuse surfaces in a scene. This reflected light contains information about the scene that becomes mixed by the diffuse reflections before reaching the image sensor. This mixing is difficult to decode using traditional cameras. Here we report the combination of a time-of-flight technique and computational reconstruction algorithms to untangle image information mixed by diffuse reflection. We demonstrate a three-dimensional range camera able to look around a corner using diffusely reflected light that achieves sub-millimetre depth precision and centimetre lateral precision over 40 cm×40 cm×40 cm of hidden space.MIT Media Lab ConsortiumUnited States. Defense Advanced Research Projects Agency. Young Faculty AwardMassachusetts Institute of Technology. Institute for Soldier Nanotechnologies (Contract W911NF-07-D-0004

Generation of homogeneous 3D Gaussian noise with spherically symmetric covariance

In this paper, an approach for 3D noise generation is presented. The proposed algorithm might be a useful tool for the generation of correlated phase screens. These phase screens can be used for the simulation and modeling of optical wave propagation through atmospheric turbulence. Arbitrary user-defined covariance functions between voxel pairs can be achieved. Correlated 3D noise is formed by superposition of multiple uncorrelated 3D Gaussian noise patterns. These uncorrelated input noise patterns are of different dimensions. They are upsampled to the same target dimensions by linear interpolation. Each input pattern then contributes to total covariance on different spatial scales. The covariances between different voxels are expressed analytically by propagation of error. For a subset of randomly chosen voxels in the entire voxel space, relative deviations between the analytical and user-defined covariances are calculated. A sum of squares of these relative deviations is then minimized by machine learning methods. The optimized parameters are the weighting factors of individual uncorrelated 3D noise patterns. Corresponding covariance functions are numerically evaluated for two current atmospheric turbulence spectra. The first one is the generalized modified atmospheric spectrum. The second one is the generalized modified von Karman spectrum. Based on these covariance functions, optimal superpositions are calculated. Finally, statistical properties of these patterns are validated by ensemble sample covariance analysis