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 - TY - RPRT A1 - Föckler, Paul A1 - Zeidler, Thomas A1 - Bimber, Oliver T1 - PhoneGuide: Museum Guidance Supported by On-Device Object Recognition on Mobile Phones N2 - 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. KW - Neuronales Netz KW - Objekterkennung KW - Handy KW - Museum KW - Mobile phones KW - object recognition KW - neural networks KW - museum guidance Y1 - 2005 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-6500 ER - TY - RPRT A1 - Bruns, Erich A1 - Bimber, Oliver T1 - Adaptive Training of Video Sets for Image Recognition on Mobile Phones N2 - 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. KW - Objektverfolgung KW - Neuronales Netz KW - Handy KW - Objekterkennung KW - Museum KW - Anpassung KW - mobile phones KW - object recognition KW - neural networks KW - museum guidance KW - pervasive tracking KW - temporal adaptation Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-8223 ER -