Statistical color models with application to skin detection
International Journal of Computer Vision
A Robust Hybrid Tracking System for Outdoor Augmented Reality
VR '04 Proceedings of the IEEE Virtual Reality 2004
Segment-Based Hand Pose Estimation
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Real-time Hand Pose Recognition Using Low-Resolution Depth Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Wearable mobile augmented reality: evaluating outdoor user experience
Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
Carpus: a non-intrusive user identification technique for interactive surfaces
Proceedings of the 25th annual ACM symposium on User interface software and technology
A method for hand detection using internal features and active boosting-based learning
Proceedings of the Fourth Symposium on Information and Communication Technology
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For interactive augmented reality, vision-based and hand-gesture-based interface are most desirable due to being natural and human-friendly. However, detecting hands and recognizing hand gestures in cluttered background are still challenging. Especially, if the background includes a large skin-colored region, the problem becomes more difficult. In this paper, we focus on detecting a hand reliably and propose an effective method. Our method is basically based on the assumption that a hand-forearm region (including a hand and part of a forearm) has different brightness from other skin-colored regions. Specifically, we first segment the hand-forearm region from other skin-colored regions based on the brightness difference which is represented by edges in this paper. Then, we extract the hand region from the hand-forearm region by detecting a feature point which indicates the wrist. Finally, we extract the hand by using the brightness-based segmentation which is slightly different from the hand-forearm region detection. We verify the effectiveness of our method by implementing a simple hand gesture interface based on our method and applying it to augmented reality applications.