@article{OertelBung2012,
  author    = {Oertel, Mario and Bung, Daniel B.},
  title     = {Initial stage of two-dimensional dam-break waves: laboratory versus VOF},
  series = {Journal of hydraulic research},
  volume    = {50},
  journal   = {Journal of hydraulic research},
  number    = {1},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {1814-2079 (E-Journal); 0022-1686 (Print)},
  doi       = {10.1080/00221686.2011.639981},
  pages     = {89 -- 97},
  year      = {2012},
  abstract  = {Since several decades, dam-break waves have been of main research interest. Mathematical approaches have been developed by analytical, physical and numerical models within the past 120 years. During the past 10 years, the number of research investigations has increased due to improved measurement techniques as well as significantly increased computer memories and performances. In this context, the present research deals with the initial stage of two-dimensional dam-break waves by comparing physical and numerical model results as well as analytical approaches. High-speed images and resulting particle image velocimetry calculations are thereby compared with the numerical volume-of-fluid (VOF) method, included in the commercial code FLOW-3D. Wave profiles and drag forces on placed obstacles are analysed in detail. Generally, a good agreement between the laboratory and VOF results is found.},
  language  = {en}
}
@article{KirsteinMuellerWaleckiMingersetal.2012,
  author    = {Kirstein, Simon and M{\"u}ller, Karsten and Walecki-Mingers, Mark and Deserno, Thomas M.},
  title     = {Robust adaptive flow line detection in sewer pipes},
  series = {Automation in construction},
  journal   = {Automation in construction},
  number    = {21},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1872-7891 (E-Journal) ; 0926-5805 (Print)},
  doi       = {10.1016/j.autcon.2011.05.009},
  pages     = {24 -- 31},
  year      = {2012},
  abstract  = {As part of a novel approach to automatic sewer inspection, this paper presents a robust algorithm for automatic flow line detection. A large image repository is obtained from about 50,000 m sewers to represent the high variability of real world sewer systems. Automatic image processing combines Canny edge detection, Hough transform for straight lines and cost minimization using Dijkstra's shortest path algorithm. Assuming that flow lines are mostly smoothly connected horizontal structures, piecewise flow line delineation is reduced to a process of selecting adjacent line candidates. Costs are derived from the gap between adjacent candidates and their reliability. A single parameter α enables simple control of the algorithm. The detected flow line may precisely follow the segmented edges (α = 0.0) or minimize gaps at joints (α = 1.0). Both, manual and ground truth-based analysis indicate that α = 0.8 is optimal and independent of the sewer's material. The algorithm forms an essential step to further automation of sewer inspection.},
  language  = {en}
}