Refine
Document Type
- Report (13) (remove)
Institute
- Junior-Professur Augmented Reality (13) (remove)
Keywords
- CGI <Computergraphik> (7)
- Association for Computing Machinery / Special Interest Group on Graphics (5)
- Maschinelles Sehen (5)
- Handy (4)
- Museum (4)
- Neuronales Netz (4)
- Objekterkennung (4)
- Projektion (4)
- museum guidance (4)
- object recognition (4)
Radiometric compensation techniques allow seamless projections onto complex everyday surfaces. Implemented with projector-camera systems they support the presentation of visual content in situations where projection-optimized screens are not available or not desired - as in museums, historic sites, air-plane cabins, or stage performances. We propose a novel approach that employs the full light transport between a projector and a camera to account for many illumination aspects, such as interreflections, refractions and defocus. Precomputing the inverse light transport in combination with an efficient implementation on the GPU makes the real-time compensation of captured local and global light modulations possible.
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.
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.