Machine Learning
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Localization Based on Building Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Image Based Localization in Urban Environments
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Coarse-to-fine vision-based localization by indexing scale-Invariant features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Towards low bit rate mobile visual search with multiple-channel coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Location Discriminative Vocabulary Coding for Mobile Landmark Search
International Journal of Computer Vision
Learning from mobile contexts to minimize the mobile location search latency
Image Communication
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We present a prototype of an information guide system to be used outdoor in our campus. It allows a user to find places of interest (e.g., lecture halls and libraries) using a camera phone. We use a database of panoramic views of campus scenes tagged by GPS locations, which can diminish overlapping between views. Panoramic views with the closest locations with the query view are acquired. We exploit a wide-baseline matching technique to match between views. However, due to dissimilarity in viewpoints and presence of repetitive structures, a vast percentage of matches could be false matches. We propose a verification model to effectively eliminate false matches. The true correspondences are chosen for pose recovery and information is then projected onto the image. The system is validated by extensive experiments, with images taken in different seasons, weather, illumination conditions, etc.