An FPGA-based face detector using neural network and a scalable floating point unit
CSECS'06 Proceedings of the 5th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing
Implementation of a neuro-fuzzy network with on-chip learning and its applications
Expert Systems with Applications: An International Journal
Evolvable block-based neural network design for applications in dynamic environments
VLSI Design - Special issue on selected papers from the midwest symposium on circuits and systems
AGV robot control by using FPGA
ACMOS'05 Proceedings of the 7th WSEAS international conference on Automatic control, modeling and simulation
Neural network implementation in hardware using FPGAs
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Neural identification of dynamic systems on FPGA with improved PSO learning
Applied Soft Computing
A model of analogue K-winners-take-all neural circuit
Neural Networks
| Hi-index | 0.00 |
The advantage of parallel computing of artificial neural networks can be combined with the potentials of VLSI circuits in order to design a real time detection and tracking system applied to video images. Based on these facts, a real-time localization and tracking algorithm has been developed for detecting human hands in video images. Due to the real time aspect, a single-pixel-based classification is aspired, so that a continuous data stream can be processed. Consequently, no storage of full images or parts of them is necessary. The classification, whether a pixel belongs to a hand or to the background, is done by analyzing the RGB-values of a single pixel by means of an artificial neural network. To obtain the full processing speed of this neural network a hardware solution is realized in a Field Programmable Gate Array (FPGA).