Reconstruction of articulated objects from point correspondences in a single uncalibrated image
Computer Vision and Image Understanding
Recognition of gestures in Arabic sign language using neuro-fuzzy systems
Artificial Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An investigation of neural networks for linear time-series forecasting
Computers and Operations Research
Motion capture assisted animation: texturing and synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Application of four-layer neural network on information extraction
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Enriching a motion collection by transplanting limbs
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
A similarity measure for motion stream segmentation and recognition
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
A neural-network approach for an automatic LED inspection system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using neural network ensembles for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Taiwan sign language (TSL) recognition based on 3D data and neural networks
Expert Systems with Applications: An International Journal
A probabilistic integrated object recognition and tracking framework
Expert Systems with Applications: An International Journal
Handy: A real-time three color glove-based gesture recognizer with learning vector quantization
Expert Systems with Applications: An International Journal
Automatic recognition of object size and shape via user-dependent measurements of the grasping hand
International Journal of Human-Computer Studies
| Hi-index | 12.05 |
The effectiveness of a neural network function depends on the network architecture and parameters. For discussing the relationship of parameters and performance, this study proposes a novel hand gesture recognition system (HGRS) combining the VICON and the back propagation neural network (BPNN). In this study, different numbers of hidden layer neurons and different numbers of layers were compared for effects on system performance. Too many or too few neurons reduced the recognition rate. Further, the hidden layer was needed for improving the system performance of the system. The training epoch size affects the general ability of the system. If the epoch size is too large, the system ''over fit'' the training set, and its general ability is impaired. However, an overly small epoch size would impair system recognition. The learning rate and system momentum affect the RMSE of the trained system. A higher learning rate and reduced momentum decrease RMSE.