Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gesture recognition with a Time-Of-Flight camera
International Journal of Intelligent Systems Technologies and Applications
Computer Vision and Image Understanding
Virtual Human Hand: Grasping and Simulation
ICDHM '09 Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009
Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning
IEEE Transactions on Neural Networks
Fingertip detection with morphology and geometric calculation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A survey of multilinear subspace learning for tensor data
Pattern Recognition
3D hand pose reconstruction with ISOSOM
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Both Hands' Fingers' Angle Calculation from Live Video
International Journal of Computer Vision and Image Processing
Bent fingers' angle calculation using supervised ANN to control electro-mechanical robotic hand
Computers and Electrical Engineering
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This paper describes GREFIT (Gesture REcognition based on FInger Tips), a neural network-based system which recognizes continuous hand postures from gray-level video images (posture capturing). Our approach yields a full identification of all finger joint angles (making, however, some assumptions about joint couplings to simplify computations). This allows a full reconstruction of the three-dimensional (3-D) hand shape, using an articulated hand model with 16 segments and 20 joint angles. GREFIT uses a two-stage approach to solve this task. In the first stage, a hierarchical system of artificial neural networks (ANNs) combined with a priori knowledge locates the two-dimensional (2-D) positions of the finger tips in the image. In the second stage, the 2-D position information is transformed by an ANN into an estimate of the 3-D configuration of an articulated hand model, which is also used for visualization. This model is designed according to the dimensions and movement possibilities of a natural human hand. The virtual hand imitates the user's hand to an remarkable accuracy and can follow postures from gray scale images at a frame rate of 10 Hz.