Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color Invariant Edge Detection
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Color and Scale: The Spatial Structure of Color Images
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Robust Computation and Parametrization of Multiple View Relations
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Augmented Reality: Merging Real and Virtual Worlds
Spatial Augmented Reality: Merging Real and Virtual Worlds
Projection defocus analysis for scene capture and image display
ACM SIGGRAPH 2006 Papers
Practical Multi-projector Display Design
Practical Multi-projector Display Design
Performance evaluation of local colour invariants
Computer Vision and Image Understanding
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Augmented Reality is an emerging research field, that aims for the composition of real and virtual imagery, by means of a camera and display device. Spatial augmented reality employs data projectors to augment the real world. In this setting, traditional tracking methods fall short due to the interference caused by the projector. Recent works assume a calibration process to model the projector and assume continuity in movement of the object being tracked. In this paper we present a tracking-by-detection system that does not require such a procedure and makes use of natural features represented by SIFT descriptors. We evaluate a set of photometric invariants that have previously been shown to improve the performance of object recognition, added to the descriptor to reduce the influence of the projector. We evaluate the descriptors based on precision-recall under projector distortion and the total system based on its tracking performance. Results show tracking is significantly more precise using one of the invariants.