@article{BrombachBrunsBimber2008,
  author    = {Brombach, Benjamin and Bruns, Erich and Bimber, Oliver},
  title     = {Subobject Detection through Spatial Relationships on Mobile Phones},
  doi       = {10.25643/bauhaus-universitaet.1353},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20081007-14296},
  year      = {2008},
  abstract  = {We present a novel image classification technique for detecting multiple objects (called subobjects) in a single image. In addition to image classifiers, we apply spatial relationships among the subobjects to verify and to predict locations of detected and undetected subobjects, respectively. By continuously refining the spatial relationships throughout the detection process, even locations of completely occluded exhibits can be determined. Finally, all detected subobjects are labeled and the user can select the object of interest for retrieving corresponding multimedia information. This approach is applied in the context of PhoneGuide, an adaptive museum guidance system for camera-equipped mobile phones. We show that the recognition of subobjects using spatial relationships is up to 68\% faster than related approaches without spatial relationships. Results of a field experiment in a local museum illustrate that unexperienced users reach an average recognition rate for subobjects of 85.6\% under realistic conditions.},
  subject      = {Objekterkennung},
  language  = {en}
}
@article{BrunsBimber2008,
  author    = {Bruns, Erich and Bimber, Oliver},
  title     = {Phone-to-Phone Communication for Adaptive Image Classification},
  doi       = {10.25643/bauhaus-universitaet.1296},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20080722-13685},
  year      = {2008},
  abstract  = {In this paper, we present a novel technique for adapting local image classifiers that are applied for object recognition on mobile phones through ad-hoc network communication between the devices. By continuously accumulating and exchanging collected user feedback among devices that are located within signal range, we show that our approach improves the overall classification rate and adapts to dynamic changes quickly. This technique is applied in the context of PhoneGuide - a mobile phone based museum guidance framework that combines pervasive tracking and local object recognition for identifying a large number of objects in uncontrolled museum environments.},
  subject      = {Peer-to-Peer-Netz},
  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}
}
@phdthesis{Bruns2010,
  author    = {Bruns, Erich},
  title     = {Adaptive Image Classification on Mobile Phones},
  doi       = {10.25643/bauhaus-universitaet.1421},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20100707-15092},
  school      = {Bauhaus-Universit{\"a}t Weimar},
  year      = {2010},
  abstract  = {The advent of high-performance mobile phones has opened up the opportunity to develop new context-aware applications for everyday life. In particular, applications for context-aware information retrieval in conjunction with image-based object recognition have become a focal area of recent research. In this thesis we introduce an adaptive mobile museum guidance system that allows visitors in a museum to identify exhibits by taking a picture with their mobile phone. Besides approaches to object recognition, we present different adaptation techniques that improve classification performance. After providing a comprehensive background of context-aware mobile information systems in general, we present an on-device object recognition algorithm and show how its classification performance can be improved by capturing multiple images of a single exhibit. To accomplish this, we combine the classification results of the individual pictures and consider the perspective relations among the retrieved database images. In order to identify multiple exhibits in pictures we present an approach that uses the spatial relationships among the objects in images. They make it possible to infer and validate the locations of undetected objects relative to the detected ones and additionally improve classification performance. To cope with environmental influences, we introduce an adaptation technique that establishes ad-hoc wireless networks among the visitors' mobile devices to exchange classification data. This ensures constant classification rates under varying illumination levels and changing object placement. Finally, in addition to localization using RF-technology, we present an adaptation technique that uses user-generated spatio-temporal pathway data for person movement prediction. Based on the history of previously visited exhibits, the algorithm determines possible future locations and incorporates these predictions into the object classification process. This increases classification performance and offers benefits comparable to traditional localization approaches but without the need for additional hardware. Through multiple field studies and laboratory experiments we demonstrate the benefits of each approach and show how they influence the overall classification rate.},
  subject      = {Kontextbezogenes System},
  language  = {en}
}