Outdoors augmented reality on mobile phone using loxel-based visual feature organization

By Gabriel Takacs, Vijay Ramaseshan Chandrasekhar, Natasha Gelfand, Yingen Xiong, Wei-Chao Chen, Thanos Bismpigiannis, Radek Grzeszczuk, Kari A. Pulli, Bernd Girod. Multimedia Information Retrieval, 2008

343 citations recorded by the source corpus.

We have built an outdoors augmented reality system for mobile phones that matches camera-phone images against a large database of location-tagged images using a robust image retrieval algorithm. We avoid network latency by implementing the algorithm on the phone and deliver excellent performance by adapting a state-of-the-art image retrieval algorithm based on robust local descriptors. Matching is performed against a database of highly relevant features, which is continuously updated to reflect changes in the environment. We achieve fast updates and scalability by pruning of irrelevant features based on proximity to the user. By compressing and incrementally updating the features stored on the phone we make the system amenable to low-bandwidth wireless connections. We demonstrate system robustness on a dataset of location-tagged images and show a smart-phone implementation that achieves a high image matching rate while operating in near real-time.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/37c7ea6c4de77114b729a2dc65b2536bee46ff4b

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Residual Enhanced Visual Vectors for on-device image matching (2011, background): “Many mobile visual search (MVS) applications have been successfully developed for recognition of outdoor landmarks [1], product covers [2], and printed documents [3], [4], amongst other categories.”
DeepCham: Collaborative Edge-Mediated Adaptive Deep Learning for Mobile Object Recognition (2016, background): “Thus an edge-based solution is required which ideally would perform adaptation training on the mobile phones.”
Real-Time Detection and Tracking for Augmented Reality on Mobile Phones (2010, methodology): “Takacs et al. recently implemented the SURF algorithm for mobile phones [17].”
Pose tracking from natural features on mobile phones (2008, methodology): “recently implemented the SURF algorithm for mobile phones [24].”
Mobile Augmented Reality Survey: From Where We Are to Where We Go (2017, methodology): “Many MAR applications [28], [29], [140], [144], [248]used WWAN for data transition and”
Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices (2015, background): “For example, an application that helps a user assemble furniture can pinpoint and label each piece [53, 6], a driver assistance application that locates and labels road signs can improve driver safety [28], a tourist application that recognizes landmarks and buildings can improve user experience, an”
Robust text detection in natural images with edge-enhanced Maximally Stable Extremal Regions (2011, background): “In recent years, visual search systems have been developed for applications such as product recognition [1, 2] and landmark recognition [3].”