155860832x
2009-02-26 - extension: rar - size: 3 MB
155860832x
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Video results for: computer networks a systems approachMore results from video
PhoneGuide (User) We present PhoneGuide -- an enhanced museum guidance approach that uses camera-equipped mobile (More) 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 connectivity 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 reviled 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 les than 6KBytes.
Special thanks to the Senckenberg Museum of Natural History, Frankfurt, to the Museum für Ur- und Frühgeschichte, Weimar, and to CellIQ for their support.
Föckler, P., Zeidler, T., Brombach, B., Bruns, E., and Bimber, O.
PhoneGuide: Museum Guidance Supported by On-Device Object Recognition on Mobile Phones
In proceedings of International Conference on Mobile and Ubiquitous Computing (MUM'05), 2005 (Less)
PhoneGuide (Content Provider) We present PhoneGuide -- an enhanced museum guidance approach that uses camera-equipped mobile (More) 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 connectivity 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 reviled 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 les than 6KBytes.
Special thanks to the Senckenberg Museum of Natural History, Frankfurt, to the Museum für Ur- und Frühgeschichte, Weimar, and to CellIQ for their support.
Föckler, P., Zeidler, T., Brombach, B., Bruns, E., and Bimber, O.
PhoneGuide: Museum Guidance Supported by On-Device Object Recognition on Mobile Phones
In proceedings of International Conference on Mobile and Ubiquitous Computing (MUM'05), 2005 (Less)
LiP-Computer Networks A Systems Approach
2009-09-11 - extension: zip - size: 3 MB
LiP-Computer Networks A Systems Approach
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comp
2008-12-13 - extension: rar - size: 3 MB
comp
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155860832X
2009-06-08 - extension: rar - size: 3 MB
155860832X
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