@article{BeckBuchleitnerFerreinetal.2014,
  author    = {Beck, Daniel and Buchleitner, Martin and Ferrein, Alexander and Niem{\"u}ller, Tim and Steinbauer, Gerald},
  title     = {Mostly Harmless \& AllemaniACs - mixed innovations},
  pages     = {1 -- 8},
  year      = {2014},
  language  = {en}
}
@article{AltherrEdererLorenzetal.2014,
  author    = {Altherr, Lena and Ederer, Thorsten and Lorenz, Ulf and Pelz, Peter F. and P{\"o}ttgen, Philipp},
  title     = {Experimental validation of an enhanced system synthesis approach},
  series = {Operations Research Proceedings 2014},
  journal   = {Operations Research Proceedings 2014},
  editor    = {L{\"u}bbecke, Marco and Koster, Arie and Letmathe, Peter and Madlener, Reihard and Peis, Britta and Walther, Grit},
  publisher = {Springer},
  address   = {Basel},
  isbn      = {978-3-319-28695-2},
  doi       = {10.1007/978-3-319-28697-6_1},
  pages     = {6},
  year      = {2014},
  abstract  = {Planning the layout and operation of a technical system is a common task for an engineer. Typically, the workflow is divided into consecutive stages: First, the engineer designs the layout of the system, with the help of his experience or of heuristic methods. Secondly, he finds a control strategy which is often optimized by simulation. This usually results in a good operating of an unquestioned sys- tem topology. In contrast, we apply Operations Research (OR) methods to find a cost-optimal solution for both stages simultaneously via mixed integer program- ming (MILP). Technical Operations Research (TOR) allows one to find a provable global optimal solution within the model formulation. However, the modeling error due to the abstraction of physical reality remains unknown. We address this ubiq- uitous problem of OR methods by comparing our computational results with mea- surements in a test rig. For a practical test case we compute a topology and control strategy via MILP and verify that the objectives are met up to a deviation of 8.7\%.},
  language  = {en}
}
@article{AlhwarinFerreinScholl2014,
  author    = {Alhwarin, Faraj and Ferrein, Alexander and Scholl, Ingrid},
  title     = {IR stereo kinect: improving depth images by combining structured light with IR stereo},
  pages     = {1 -- 9},
  year      = {2014},
  language  = {en}
}
@incollection{AlhwarinFerreinScholl2014,
  author    = {Alhwarin, Faraj and Ferrein, Alexander and Scholl, Ingrid},
  title     = {IR stereo kinect: improving depth images by combining structured light with IR stereo},
  series = {PRICAI 2014: Trends in artificial intelligence : 13th Pacific Rim International Conference on Artificial Intelligence : Gold Coast, QLD, Australia, December 1-5, 2014 : proceedings. (Lecture notes in computer science ; vol. 8862)},
  booktitle = {PRICAI 2014: Trends in artificial intelligence : 13th Pacific Rim International Conference on Artificial Intelligence : Gold Coast, QLD, Australia, December 1-5, 2014 : proceedings. (Lecture notes in computer science ; vol. 8862)},
  publisher = {Springer},
  address   = {M{\"u}nchen},
  isbn      = {978-3-319-13559-5 (Print) ; 978-3-319-13560-1 (E-Book)},
  doi       = {10.1007/978-3-319-13560-1_33},
  pages     = {409 -- 421},
  year      = {2014},
  abstract  = {RGB-D sensors such as the Microsoft Kinect or the Asus Xtion are inexpensive 3D sensors. A depth image is computed by calculating the distortion of a known infrared light (IR) pattern which is projected into the scene. While these sensors are great devices they have some limitations. The distance they can measure is limited and they suffer from reflection problems on transparent, shiny, or very matte and absorbing objects. If more than one RGB-D camera is used the IR patterns interfere with each other. This results in a massive loss of depth information. In this paper, we present a simple and powerful method to overcome these problems. We propose a stereo RGB-D camera system which uses the pros of RGB-D cameras and combine them with the pros of stereo camera systems. The idea is to utilize the IR images of each two sensors as a stereo pair to generate a depth map. The IR patterns emitted by IR projectors are exploited here to enhance the dense stereo matching even if the observed objects or surfaces are texture-less or transparent. The resulting disparity map is then fused with the depth map offered by the RGB-D sensor to fill the regions and the holes that appear because of interference, or due to transparent or reflective objects. Our results show that the density of depth information is increased especially for transparent, shiny or matte objects.},
  language  = {en}
}