A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recent Advances in Augmented Reality
IEEE Computer Graphics and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Algorithm Design
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Multilevel Banded Graph Cuts Method for Fast Image Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Effciently Solving Dynamic Markov Random Fields Using Graph Cuts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Online camera pose estimation in partially known and dynamic scenes
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
A mobile markerless AR system for maintenance and repair
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Natural feature tracking for augmented reality
IEEE Transactions on Multimedia
Large document, small screen: a camera driven scroll and zoom control for mobile devices
Proceedings of the 2008 symposium on Interactive 3D graphics and games
Space-Time Multi-Resolution Banded Graph-Cut for Fast Segmentation
Proceedings of the 30th DAGM symposium on Pattern Recognition
AR Record&Replay: situated compositing of video content in mobile augmented reality
Proceedings of the 24th Australian Computer-Human Interaction Conference
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We present an efficient and accurate object tracking algorithm based on the concept of graph cut segmentation. The ability to track visible objects in real-time provides an invaluable tool for the implementation of markerless Augmented Reality. Once an object has been detected, it's location in future frames can be used to position virtual content, and thus annotate the environment. Unlike many object tracking algorithms, our approach does not rely on a preexisting 3D model or any other information about the object or its environment. It takes, as input, a set of pixels representing an object in an initial frame and uses a combination of optical flow and graph cut segmentation to determine the corresponding pixels in each future frame. Experiments show that our algorithm robustly tracks objects of disparate shapes and sizes over hundreds of frames, and can even handle difficult cases where an object contains many of the same colors as its background. We further show how this technology can be applied to practical AR applications.