Fast Tracking of Hands and Fingertips in Infrared Images for Augmented Desk Interface
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Open Hand Detection in a Cluttered Single Image using Finger Primitives
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hand Shape Recognition Using Fingertips
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
A boosted classifier tree for hand shape detection
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Vision-based human-computer interaction (HCI) is a natural and human-centered way to make interaction between human and computer. Recently, with the miniaturization of projectors and the development of embedded systems, there has been an explosion of interest in systems which combine projection technology with computer vision. Associating a projector with a camera offers a cheap means to transform any surface into an interactive display surface. However, it is very hard to segment hand and recognize hand gesture due to self-occlusion, non-rigid tissue, even when the occlusion due to projection content can be avoided. In this paper, a novel approach is proposed to recognize finger stroke under the dynamic illumination circumstances. The approach is based on one projector and two heterogeneous cameras. First, an NIR camera is used to get the finger-tip and hand model, then, the hand model is used to get the interest points of the hand in the visible camera, then we can get the disparity of interest points from the two heterogeneous images. The disparity change is related to the depth change that can be used to determine when stroke event happens. Experiment results from a prototype system show that the approach can run in real-time without using special markers or gloves.