Combining Appearance and Topology for Wide Baseline Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Wide Baseline Point Matching Using Affine Invariants Computed from Intensity Profiles
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Indoor Localization Using Camera Phones
WMCSA '06 Proceedings of the Seventh IEEE Workshop on Mobile Computing Systems & Applications: Supplement
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Search Strategies of Visually Impaired Persons Using a Camera Phone Wayfinding System
ICCHP '08 Proceedings of the 11th international conference on Computers Helping People with Special Needs
Outdoors augmented reality on mobile phone using loxel-based visual feature organization
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Indoor Positioning and Navigation with Camera Phones
IEEE Pervasive Computing
Object Detection by Keygraph Classification
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Wide area localization on mobile phones
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
A conceptual framework for camera phone-based interaction techniques
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
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We present an application for mobile phones to detect indoor signs and help in localization. Because it depends only on device capabilities, it is flexible and unconstrained. Detection is accomplished online by keygraph matching between sign images collected offline and the image from a mobile camera phone. After detection we apply a simple localization method based on a comparison between the detected sign and a dataset, consisting of images of the whole environment taken at different positions. We show the results obtained using the application in a local indoor environment.