Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
A Project for Hand Gesture Recognition
SIBGRAPI '00 Proceedings of the 13th Brazilian Symposium on Computer Graphics and Image Processing
Probabilistic visual learning for object detection
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Principal Manifolds and Bayesian Subspaces for Visual Recognition
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recognizing Hand Gesture using Fourier Descriptors
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Gesture Recognition Using Temporal Template Based Trajectories
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
High-Speed Human Motion Recognition Based on a Motion History Image and an Eigenspace
IEICE - Transactions on Information and Systems
Low-Cost Gesture-Based Interaction for Intelligent Environments
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
A tennis training application using 3d gesture recognition
AMDO'12 Proceedings of the 7th international conference on Articulated Motion and Deformable Objects
Gesture recognition system using 2D-invariant moment feature and Elman neural network
International Journal of Artificial Intelligence and Soft Computing
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Some groups of handicapped persons cannot reliably move the mouse and do the necessary operation on it to control the computer. However they can do some 3-d hand motions. Variety of tools has been presented for these users to interact with computer. Hand gesture recognition is one of the proper methods for this purpose. This paper presents a new algorithm for hand gesture recognition. In this algorithm, after constructing motion history image of video frames for each gesture and applying necessary processing on this image, motion orientation histogram vector is extracted. These vectors are then used for the training of Hidden Markov Model and hand gesture recognition. We tested the proposed algorithm with different hand gestures and results showed the correct gesture recognition rate of 90 percent. Comparing the results of proposed method with those of other methods showed that in addition to eliminating traditional problems in this area, recognition rate has been improved up to 4 percent.