Techniques for disparity measurement
CVGIP: Image Understanding
Computer vision and applications: a guide for students and practitioners
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Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Gesture Recognition Using 3D Appearance and Motion Features
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 10 - Volume 10
Sign Language Recognition by Combining Statistical DTW and Independent Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Method Evaluation for Hidden Markov Model Based Real-Time Gesture Recognition
ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
Improving Hand Gesture Recognition Using 3D Combined Features
ICMV '09 Proceedings of the 2009 Second International Conference on Machine Vision
Recognizing and interpreting gestures on a mobile robot
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Head pose estimation using stereo vision for human-robot interaction
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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IEEE Transactions on Robotics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Recently a lot of hand held devices have been commercially employed, but they suffer from the problems such as un-natural interaction and comfort of use. The computer vision based HCI (Human Computer Interaction) is an effective and straightforward method. We have used a new effective gesture recognition method with the stereo vision technique. The stereoscopic system is implemented using two standard (VGA) webcams. The webcams are calibrated using chessboard pattern and subsequently the rectified images, disparity and depth images are established. We applied two types of techniques and compared outcome on the basis of feature extraction and gesture recognition. The first technique is block matching and the second method utilizes three dimensional position, velocity, acceleration and orientation features with Euclidean distance metrics. To extract features from three dimensional information is computationally complex process but provides better recognition results. Our experimentation with above mentioned approaches provided far better results than gesture recognition techniques using one webcam. Hence stereo vision based system can be used for real-time applications in a simple and cost effective way.