@techreport{AmanoBimberGrundhoefer2010,
  author    = {Amano, Toshiyuki and Bimber, Oliver and Grundh{\"o}fer, Anselm},
  title     = {Appearance Enhancement for Visually Impaired with Projector Camera Feedback},
  doi       = {10.25643/bauhaus-universitaet.1411},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20100106-14974},
  year      = {2010},
  abstract  = {Visually impaired is a common problem for human life in the world wide. The projector-based AR technique has ability to change appearance of real object, and it can help to improve visibility for visually impaired. We propose a new framework for the appearance enhancement with the projector camera system that employed model predictive controller. This framework enables arbitrary image processing such as photo-retouch software in the real world and it helps to improve visibility for visually impaired. In this article, we show the appearance enhancement result of Peli's method and Wolffshon's method for the low vision, Jefferson's method for color vision deficiencies. Through experiment results, the potential of our method to enhance the appearance for visually impaired was confirmed as same as appearance enhancement for the digital image and television viewing.},
  subject      = {Maschinelles Sehen},
  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{BrunsBrombachBimber2007,
  author    = {Bruns, Erich and Brombach, Benjamin and Bimber, Oliver},
  title     = {Mobile Phone Enabled Museum Guidance with Adaptive Classification},
  doi       = {10.25643/bauhaus-universitaet.940},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20111215-9406},
  year      = {2007},
  abstract  = {Although audio guides are widely established in many museums, they suffer from several drawbacks compared to state-of-the-art multimedia technologies: First, they provide only audible information to museum visitors, while other forms of media presentation, such as reading text or video could be beneficial for museum guidance tasks. Second, they are not very intuitive. Reference numbers have to be manually keyed in by the visitor before information about the exhibit is provided. These numbers are either displayed on visible tags that are located near the exhibited objects, or are printed in brochures that have to be carried. Third, offering mobile guidance equipment to visitors leads to acquisition and maintenance costs that have to be covered by the museum. With our project PhoneGuide we aim at solving these problems by enabling the application of conventional camera-equipped mobile phones for museum guidance purposes. The advantages are obvious: First, today's off-the-shelf mobile phones offer a rich pallet of multimedia functionalities ---ranging from audio (over speaker or head-set) and video (graphics, images, movies) to simple tactile feedback (vibration). Second, integrated cameras, improvements in processor performance and more memory space enable supporting advanced computer vision algorithms. Instead of keying in reference numbers, objects can be recognized automatically by taking non-persistent photographs of them. This is more intuitive and saves museum curators from distributing and maintaining a large number of physical (visible or invisible) tags. Together with a few sensor-equipped reference tags only, computer vision based object recognition allows for the classification of single objects; whereas overlapping signal ranges of object-distinct active tags (such as RFID) would prevent the identification of individuals that are grouped closely together. Third, since we assume that museum visitors will be able to use their own devices, the acquisition and maintenance cost for museum-owned devices decreases.},
  subject      = {Objektverfolgung},
  language  = {en}
}
@techreport{ExnerBrunsKurzetal.2009,
  author    = {Exner, David and Bruns, Erich and Kurz, Daniel and Grundh{\"o}fer, Anselm and Bimber, Oliver},
  title     = {Fast and Reliable CAMShift Tracking},
  organization    = {JP AUgmented Reality, Bauhaus-Universit{\"a}t Weimar},
  doi       = {10.25643/bauhaus-universitaet.1410},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20091217-14962},
  year      = {2009},
  abstract  = {CAMShift is a well-established and fundamental algorithm for kernel-based visual object tracking. While it performs well with objects that have a simple and constant appearance, it is not robust in more complex cases. As it solely relies on back projected probabilities it can fail in cases when the object's appearance changes (e.g. due to object or camera movement, or due to lighting changes), when similarly colored objects have to be re-detected or when they cross their trajectories. We propose extensions to CAMShift that address and resolve all of these problems. They allow the accumulation of multiple histograms to model more complex object appearance and the continuous monitoring of object identi- ties to handle ambiguous cases of partial or full occlusion. Most steps of our method are carried out on the GPU for achieving real-time tracking of multiple targets simultaneously. We explain an ecient GPU implementations of histogram generation, probability back projection, im- age moments computations, and histogram intersection. All of these techniques make full use of a GPU's high parallelization.},
  subject      = {Bildverarbeitung},
  language  = {en}
}
@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{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}
}