@techreport{BrunsBimber2007,
  author    = {Bruns, Erich and Bimber, Oliver},
  title     = {Adaptive Training of Video Sets for Image Recognition on Mobile Phones},
  doi       = {10.25643/bauhaus-universitaet.822},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-8223},
  year      = {2007},
  abstract  = {We present an enhancement towards adaptive video training for PhoneGuide, a digital museum guidance system for ordinary camera-equipped mobile phones. It enables museum visitors to identify exhibits by capturing photos of them. In this article, a combined solution of object recognition and pervasive tracking is extended to a client-server-system for improving data acquisition and for supporting scale-invariant object recognition.},
  subject      = {Objektverfolgung},
  language  = {en}
}
@techreport{BrunsBrombachZeidleretal.2005,
  author    = {Bruns, Erich and Brombach, Benjamin and Zeidler, Thomas and Bimber, Oliver},
  title     = {Enabling Mobile Phones To Support Large-Scale Museum Guidance},
  doi       = {10.25643/bauhaus-universitaet.677},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-6777},
  year      = {2005},
  abstract  = {We present a museum guidance system called PhoneGuide that uses widespread camera equipped mobile phones for on-device object recognition in combination with pervasive tracking. It provides additional location- and object-aware multimedia content to museum visitors, and is scalable to cover a large number of museum objects.},
  subject      = {Objektverfolgung},
  language  = {en}
}
@techreport{FoecklerZeidlerBimber2005,
  author    = {F{\"o}ckler, Paul and Zeidler, Thomas and Bimber, Oliver},
  title     = {PhoneGuide: Museum Guidance Supported by On-Device Object Recognition on Mobile Phones},
  doi       = {10.25643/bauhaus-universitaet.650},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-6500},
  year      = {2005},
  abstract  = {We present PhoneGuide - an enhanced museum guidance approach that uses camera-equipped mobile phones and on-device object recognition. Our main technical achievement is a simple and light-weight object recognition approach that is realized with single-layer perceptron neuronal networks. In contrast to related systems which perform computational intensive image processing tasks on remote servers, our intention is to carry out all computations directly on the phone. This ensures little or even no network traffic and consequently decreases cost for online times. Our laboratory experiments and field surveys have shown that photographed museum exhibits can be recognized with a probability of over 90\%. We have evaluated different feature sets to optimize the recognition rate and performance. Our experiments revealed that normalized color features are most effective for our method. Choosing such a feature set allows recognizing an object below one second on up-to-date phones. The amount of data that is required for differentiating 50 objects from multiple perspectives is less than 6KBytes.},
  subject      = {Neuronales Netz},
  language  = {en}
}