Geo-indexed object recognition for mobile vision tasks
Proceedings of the 10th international conference on Human computer interaction with mobile devices and services
Learning Distance Functions for Automatic Annotation of Images
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
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
Phone-to-phone communication for adaptive image classification
Proceedings of the 6th International Conference on Advances in Mobile Computing and Multimedia
Subobject detection through spatial relationships on mobile phones
Proceedings of the 14th international conference on Intelligent user interfaces
An Attentive Machine Interface Using Geo-Contextual Awareness for Mobile Vision Tasks
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
DAVID: discriminant analysis for verification of monuments in image data
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Geo-contextual priors for attentive urban object recognition
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Information overlay for camera phones in indoor environments
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Building-based structural data for core functions of outdoor scene analysis
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Boosting histograms of descriptor distances for scalable multiclass specific scene recognition
Image and Vision Computing
Intelligent eye: location-based multimedia information for mobile phones
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
Mobile Augmented Reality: A topometric system for wide area augmented reality
Computers and Graphics
Automated annotation of landmark images using community contributed datasets and web resources
SAMT'10 Proceedings of the 5th international conference on Semantic and digital media technologies
Real-time object recognition using mobile devices
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
Visual mapping and multi-modal localisation for anywhere AR authoring
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
A fast offline building recognition application on a mobile telephone
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
CAPIRE: a context-aware points of interest REcognition system using a CBIR approach
FMN'10 Proceedings of the Third international conference on Future Multimedia Networking
ClayVision: the (elastic) image of the city
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Object recognition using discriminative parts
Computer Vision and Image Understanding
Who is here: location aware face recognition
Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones
A mobile indoor navigation system interface adapted to vision-based localization
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
Context-aware Discriminative Vocabulary Tree Learning for mobile landmark recognition
Digital Signal Processing
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We present a computer vision system for the detection and identification of urban objects from mobile phone imagery, e.g., for the application of tourist information services. Recognition is based on MAP decision making over weak object hypotheses from local descriptor responses in the mobile imagery. We present an improvement over the standard SIFT key detector [7] by selecting only informative (i-SIFT) keys for descriptor matching. Selection is applied first to reduce the complexity of the object model and second to accelerate detection by selective filtering. We present results on the MPG-20 mobile phone imagery with severe illumination, scale and viewpoint changes in the images, performing with ≈ 98% accuracy in identification, efficient (100%) background rejection, efficient (0%) false alarm rate, and reliable quality of service under extreme illumination conditions, significantly improving standard SIFT based recognition in every sense, providing - important for mobile vision - runtimes which are ≈ 8 (≈24) times faster for the MPG-20 (ZuBuD) database.