A new fast forecasting technique using high speed neural networks
WSEAS Transactions on Signal Processing
A novel fast Kolmogorov's spline complex network for pattern detection
WSEAS TRANSACTIONS on SYSTEMS
A new fast forecasting technique using high speed neural networks
SSIP'08 Proceedings of the 8th conference on Signal, Speech and image processing
A novel fast Kolmogorov's spline complex network for pattern detection
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
Fast image matching on web pages
CIS'09 Proceedings of the international conference on Computational and information science 2009
Design of anti-GPS for reasons of security
CIS'09 Proceedings of the international conference on Computational and information science 2009
A fast computerized method for automatic simplification of boolean functions
ISTASC'09 Proceedings of the 9th WSEAS International Conference on Systems Theory and Scientific Computation
Fast word detection in a speech using new high speed time delay neural networks
ISCGAV'09 Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision
Fast time delay neural networks for word detection in video conference
ECC'09 Proceedings of the 3rd international conference on European computing conference
An efficient electronic archiving approach for office automation
ECC'09 Proceedings of the 3rd international conference on European computing conference
Fast information processing over business networks
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
Fast image matching on web pages
WSEAS Transactions on Signal Processing
Fast word detection in a speech using new high speed time delay neural networks
WSEAS Transactions on Signal Processing
A new automated information retrieval system by using intelligent mobile agent
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
New fast principal component analysis for real-time face detection
Machine Graphics & Vision International Journal
Fast Karnough map for simplification of complex Boolean functions
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
Fast human motion tracking by using high speed neural
SSIP '09/MIV'09 Proceedings of the 9th WSEAS international conference on signal, speech and image processing, and 9th WSEAS international conference on Multimedia, internet & video technologies
A new expert system for pediatric respiratory diseases by using neural networks
AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
Surveillance of video signals over computer networks
ACC'11/MMACTEE'11 Proceedings of the 13th IASME/WSEAS international conference on Mathematical Methods and Computational Techniques in Electrical Engineering conference on Applied Computing
Real-time transmission of video streaming over computer networks
EHAC'12/ISPRA/NANOTECHNOLOGY'12 Proceedings of the 11th WSEAS international conference on Electronics, Hardware, Wireless and Optical Communications, and proceedings of the 11th WSEAS international conference on Signal Processing, Robotics and Automation, and proceedings of the 4th WSEAS international conference on Nanotechnology
A new hybrid system for information security
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
A new fast neural network model
ACACOS'12 Proceedings of the 11th WSEAS international conference on Applied Computer and Applied Computational Science
| Hi-index | 0.00 |
Neural networks have shown good results for detection of a certain pattern in a given image. In our previous work, a fast algorithm for object/face detection was presented. Such algorithm was designed based on cross correlation in the frequency domain between the input image and the weights of neural networks. In this paper, a simple design for solving the problem of local subimage normalization in the frequency domain is presented. This is done by normalizing the weights in the spatial domain off line. Furthermore, it is proved that local subimage normalization by normalizing the weights is faster than subimage normalization in the spatial domain. Moreover, the overall speed up ratio of the detection process is increased as the normalization of weights is done off line.