Grid-based outdoor object recognition for augmented reality

Brian Adrian Flowers, University of Rhode Island

Abstract

Augmented Reality of an outdoor scene as a topic has gained a great deal of popularity in recent years. This work will focus on markerless hybrid Outdoor Augmented Reality (OAR) systems. In general OAR is performed through a classical statistical approach. Strong features are calculated from images of the object, located, and tracked in the scene. Gathering such features requires specialized knowledge of Computer Vision techniques; keeping OAR from finding commercial success. Model-based approaches rely less on previously gathered data, increasingly the accessibility of such techniques, but require extensive scene understanding to correctly parse the scene. The proposed model-based approach minimizes the required scene understanding allowing for augmentation of an environment with minimal input.^

Subject Area

Computer Science

Recommended Citation

Brian Adrian Flowers, "Grid-based outdoor object recognition for augmented reality" (2014). Dissertations and Master's Theses (Campus Access). Paper AAI1560279.
http://digitalcommons.uri.edu/dissertations/AAI1560279

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