@inproceedings{ElsenKraissKrumbiegel1997,
  author    = {Elsen, Ingo and Kraiss, Karl-Friedrich and Krumbiegel, Dirk},
  title     = {Pixel based 3D object recognition with bidirectional associative memories},
  series = {International Conference on Neural Networks 1997},
  booktitle = {International Conference on Neural Networks 1997},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {0-7803-4122-8},
  pages     = {1679 -- 1684},
  year      = {1997},
  abstract  = {This paper addresses the pixel based recognition of 3D objects with bidirectional associative memories. Computational power and memory requirements for this approach are identified and compared to the performance of current computer architectures by benchmarking different processors. It is shown, that the performance of special purpose hardware, like neurocomputers, is between one and two orders of magnitude higher than the performance of mainstream hardware. On the other hand, the calculation of small neural networks is performed more efficiently on mainstream processors. Based on these results a novel concept is developed, which is tailored for the efficient calculation of bidirectional associative memories. The computational efficiency is further enhanced by the application of algorithms and storage techniques which are matched to characteristics of the application at hand.},
  language  = {en}
}
@inproceedings{Elsen1998,
  author    = {Elsen, Ingo},
  title     = {A pixel based approach to view based object recognition with self-organizing neural networks},
  series = {IECON'98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society},
  booktitle = {IECON'98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {0-7803-4503-7},
  doi       = {10.1109/IECON.1998.724032},
  pages     = {2040 -- 2044},
  year      = {1998},
  abstract  = {This paper addresses the pixel based classification of three dimensional objects from arbitrary views. To perform this task a coding strategy, inspired by the biological model of human vision, for pixel data is described. The coding strategy ensures that the input data is invariant against shift, scale and rotation of the object in the input domain. The image data is used as input to a class of self organizing neural networks, the Kohonen-maps or self-organizing feature maps (SOFM). To verify this approach two test sets have been generated: the first set, consisting of artificially generated images, is used to examine the classification properties of the SOFMs; the second test set examines the clustering capabilities of the SOFM when real world image data is applied to the network after it has been preprocessed to be invariant against shift, scale and rotation. It is shown that the clustering capability of the SOFM is strongly dependant on the invariance coding of the images.},
  language  = {en}
}
@inproceedings{WalterElsenMuelleretal.1999,
  author    = {Walter, Peter and Elsen, Ingo and M{\"u}ller, Holger and Kraiss, Karl-Friedrich},
  title     = {3D object recognition with a specialized mixtures of experts architecture},
  series = {IJCNN'99. International Joint Conference on Neural Networks. Proceedings},
  booktitle = {IJCNN'99. International Joint Conference on Neural Networks. Proceedings},
  publisher = {IEEE},
  address   = {New York},
  isbn      = {0-7803-5529-6},
  issn      = {1098-7576},
  doi       = {10.1109/IJCNN.1999.836243},
  pages     = {3563 -- 3568},
  year      = {1999},
  abstract  = {Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.},
  language  = {en}
}
@article{ElsenHartungHornetal.2001,
  author    = {Elsen, Ingo and Hartung, Frank and Horn, Uwe and Kampmann, Markus and Peters, Liliane},
  title     = {Streaming technology in 3G mobile communication systems},
  series = {Computer : innovative technology for computer professionals},
  volume    = {34},
  journal   = {Computer : innovative technology for computer professionals},
  number    = {9 Seiten},
  editor    = {Voas, Jeffrey},
  publisher = {IEEE},
  address   = {New York},
  issn      = {0018-9162},
  pages     = {46 -- 52},
  year      = {2001},
  abstract  = {Third-generation mobile communication systems will combine standardized streaming with a range of unique services to provide high-quality Internet content that meets the specific needs of the rapidly growing mobile market.},
  language  = {en}
}