Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ASIFT: A New Framework for Fully Affine Invariant Image Comparison
SIAM Journal on Imaging Sciences
Leveraging 3D City Models for Rotation Invariant Place-of-Interest Recognition
International Journal of Computer Vision
City-scale landmark identification on mobile devices
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Determining the pose of a mobile device based on visual information is a promising approach to solve the indoor localization problem. We present an approach that transforms localized images along a mapping trajectory into virtual viewpoints that cover a set of densely sampled camera positions and orientations in a confined environment. The viewpoints are represented by their respective bag-of-features vectors and image retrieval techniques are applied to determine the most likely pose of query images at very low computational complexity. As virtual image locations and orientations are decoupled from actual image locations, the system is able to work with sparse reference imagery and copes well with perspective distortion. Experiments confirm that pose retrieval performance is significantly improved.