TY - RPRT A1 - Bruns, Erich A1 - Brombach, Benjamin A1 - Zeidler, Thomas A1 - Bimber, Oliver T1 - Enabling Mobile Phones To Support Large-Scale Museum Guidance N2 - 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. KW - Objektverfolgung KW - Neuronales Netz KW - Handy KW - Objekterkennung KW - Museum KW - mobile phones KW - object recognition KW - neural networks KW - museum guidance KW - pervasive tracking Y1 - 2005 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-6777 ER - TY - RPRT A1 - Bruns, Erich A1 - Brombach, Benjamin A1 - Bimber, Oliver T1 - Mobile Phone Enabled Museum Guidance with Adaptive Classification N2 - 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. KW - Objektverfolgung KW - Neuronales Netz KW - Handy KW - Objekterkennung KW - Museum KW - Anpassung KW - Mobiltelefone KW - Museumsführer KW - Adaptive Klassifizierung KW - Ad-hoc Sensor-Netzwerke KW - mobile phones KW - object recognition KW - museum guidance KW - adaptive classification KW - ad-hoc sensor networks Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-9406 ER - TY - JOUR A1 - Brombach, Benjamin A1 - Bruns, Erich A1 - Bimber, Oliver T1 - Subobject Detection through Spatial Relationships on Mobile Phones N2 - 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. KW - Objekterkennung KW - Smartphone KW - Subobjekterkennung KW - Räumliche Beziehungen KW - Neuronales Netz KW - Museumsführer KW - Subobject Detection KW - Spatial Relationships KW - Neural Networks KW - Museum Guidance Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20081007-14296 ER -