Diese Vorführung stellt die Anfragesprachen Xcerpt [4] und visXcerpt [3], siehe http://xcerpt.org/, vor. Xcerpt ist eine textuelle Anfragesprache, visXcerpt ist eine visuelle Anfragesprache, die als Visualisierung von textuellen XcerptProgrammen konzipiert wurde. Xcerpt und visXcerpt sind mit dem Ziel konzipiert worden, innerhalb des selben Anfrageprogramms Daten in den Datenformaten des Webs X...

In this paper, we examine and present the development of a depth map acquisition system with real-time characteristics based on a binocular active vision system. In order to obtain depth information we present a method that combines stereo matching with mechanical activity, reducing the time spent to perform the correspondence between the image points on the left and right images. Controlling t...

Recovering three-dimensional information from two-dimensional images is the fundamental goal of stereo techniques. The problem of recovering depth (three-dimensional information) from a set of images is essentially the correspondence problem: Given a point in one image, find the corresponding point in each of the other images. Finding potential correspondences usually involves matching some ima...

Dense stereo is a well studied problem in computer vision. Generally dense stereo algorithms provide only a single estimate of disparity, ignoring uncertainty in the disparity map. Here however, we present a new, linear-time, exact method for recovering entire distributions for disparity at all pixels. This is accomplished by using a recent extension, due to Durbin et al., of the well-known for...

In recent years, geotagged social media has become popular as a novel source for geographic knowledge discovery. Ground-level images and videos provide a different perspective than overhead imagery and can be applied to a range of applications such as land use mapping, activity detection, pollution mapping, etc. The sparse and uneven distribution of this data presents a problem, however, for ge...

We present maps of the topographic roughness of the Moon at hectometer and kilometer scales. The maps are derived from range profiles obtained by the Lunar Orbiter Laser Altimeter (LOLA) instrument onboard the Lunar Reconnaissance Orbiter (LRO) spacecraft. As roughness measures, we used the interquartile range of profile curvature at several baselines, from 115 m to 1.8 km, and plotted these in...