Dokument-ID Dokumenttyp Verfasser/Autoren Herausgeber Haupttitel Abstract Auflage Verlagsort Verlag Erscheinungsjahr Seitenzahl Schriftenreihe Titel Schriftenreihe Bandzahl ISBN Quelle der Hochschulschrift Konferenzname Quelle:Titel Quelle:Jahrgang Quelle:Heftnummer Quelle:Erste Seite Quelle:Letzte Seite URN DOI Abteilungen OPUS4-677 Bericht Bruns, Erich; Brombach, Benjamin; Zeidler, Thomas; Bimber, Oliver Enabling Mobile Phones To Support Large-Scale Museum Guidance 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. 2005 urn:nbn:de:gbv:wim2-20111215-6777 10.25643/bauhaus-universitaet.677 Junior-Professur Augmented Reality OPUS4-650 Bericht Föckler, Paul; Zeidler, Thomas; Bimber, Oliver PhoneGuide: Museum Guidance Supported by On-Device Object Recognition on Mobile Phones 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. 2005 urn:nbn:de:gbv:wim2-20111215-6500 10.25643/bauhaus-universitaet.650 Junior-Professur Augmented Reality