Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Applied image processing
Neural Networks
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Digital Image Processing
Vision-Based Gesture Recognition: A Review
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
Robust classification of hand postures against complex backgrounds
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Color-spatial image indexing and applications
Color-spatial image indexing and applications
Dynamic hand gesture recognition using the skeleton of the hand
EURASIP Journal on Applied Signal Processing
International Journal of Bioinformatics Research and Applications
Gesture recognition under small sample size
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Recognitive Aspects of Moment Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.00 |
This paper presents a novel technique for hand gesture recognition through human---computer interaction based on shape analysis. The main objective of this effort is to explore the utility of a neural network-based approach to the recognition of the hand gestures. A unique multi-layer perception of neural network is built for classification by using back-propagation learning algorithm. The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predefined finite number of gesture classes. The proposed system presents a recognition algorithm to recognize a set of six specific static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, preprocessing, feature extraction, and classification. In preprocessing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the first method, the hand contour is used as a feature which treats scaling and translation of problems (in some cases). The complex moment algorithm is, however, used to describe the hand gesture and treat the rotation problem in addition to the scaling and translation. The algorithm used in a multi-layer neural network classifier which uses back-propagation learning algorithm. The results show that the first method has a performance of 70.83% recognition, while the second method, proposed in this article, has a better performance of 86.38% recognition rate.