Interacting with paper on the DigitalDesk
Communications of the ACM - Special issue on computer augmented environments: back to the real world
User-independent online gesture recognition by relative motion extraction
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
Proceedings of the 1st ACM international workshop on Human-centered multimedia
An Improved Algorithm of Hand Gesture Recognition under Intricate Background
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
Hand gesture recognition using a neural network shape fitting technique
Engineering Applications of Artificial Intelligence
A novel interactive method of virtual reality system based on hand gesture recognition
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A person independent system for recognition of hand postures used in sign language
Pattern Recognition Letters
Local contour descriptors around scale-invariant keypoints
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Information Processing and Management: an International Journal
Adaptive mixture-of-experts models for data glove interface with multiple users
Expert Systems with Applications: An International Journal
Attention Based Detection and Recognition of Hand Postures Against Complex Backgrounds
International Journal of Computer Vision
Hi-index | 0.00 |
Our paper proposes a vision-based hand gesture recognition system with interactive training, aimed to achieve a user-independent application by on-line supervised training. Usual recognition systems involve a preliminary off-line training phase, separated from the recognition phase. If the system recognizes unknown (non-trainer) users the recognition rate of gesture classes could decrease. The recognition has to be suspended and all gestures need to be retrained with an improved training set, resulting in inconveniences. Our new approach introduces an on-line training method embedded into the recognition process, being interactively controlled by the user and adapting to his/her gestures. Our main goal is that any non-trainer users be able to use the system instantly and if the recognition accuracy decreases only the faulty detected gestures be retrained realizing fast adaptation. We implement the proposed system as a camera-projector system in which users can directly interact with the projected image by hand gestures, realizing an augmented reality tool in a multi-user environment. The emphasis is on the novel approach of dynamic and quick follow-up training capabilities instead of handling large pre-trained databases. We also conducted tests on several users in real environments for a practical application.