A Maximum Likelihood Framework for Determining Moving Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Development of a real time image based object recognition method for mobile AR-devices
Proceedings of the 2nd international conference on Computer graphics, virtual Reality, visualisation and interaction in Africa
Polyhedral Object Detection and Pose Estimation for Augmented Reality Applications
CA '02 Proceedings of the Computer Animation
Stable Real-Time 3D Tracking Using Online and Offline Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Line Tracking with Multiple Hypotheses for Augmented Reality
ISMAR '05 Proceedings of the 4th IEEE/ACM International Symposium on Mixed and Augmented Reality
Monocular model-based 3D tracking of rigid objects
Foundations and Trends® in Computer Graphics and Vision
Kick-real, a mobile mixed reality game
Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology
Pose tracking from natural features on mobile phones
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
iAR: an exploratory augmented reality system for mobile devices
Proceedings of the 18th ACM symposium on Virtual reality software and technology
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This paper presents a markerless tracking technique targeted to the Windows Mobile Pocket PC platform. The primary aim of this work is to allow the development of standalone augmented reality applications for handheld devices based on natural feature tracking of fully 3-Dimensional objects. In order to achieve this goal, a model-based tracking approach that relies on edge information was adopted. Since it does not require high processing power, it is suitable for constrained devices such as handhelds. The OpenGL ES graphics library was used to detect the visible edges in a given frame, taking advantage of graphics hardware acceleration when available. In addition, a subset of two computer vision libraries was ported to the Pocket PC platform in order to provide some required algorithms to the markerless mobile solution. They were also adapted to use fixed-point math, with the purpose of improving the overall performance of the routines. The port of these libraries opens up the possibility of having other computer-vision tasks being executed on mobile platforms. An augmented reality application was created using the implemented technique and evaluations were done regarding tracking performance, accuracy and robustness. In most of the tests, the frame rates obtained are suitable for handheld augmented reality and a reasonable estimation of the object pose was provided.