Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast and Accurate Face Detector for Indexation of Face Images
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A New Robust Face Detection in Color Images
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Human iris detection using fast cooperative modular neural nets and image decomposition
Machine Graphics & Vision International Journal
Fast information processing over business networks
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
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 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 paper, 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. Furthermore, the effect of image normalization on the speed up ratio of pattern detection is presented. Simulation results show that local subimage normalization through weight normalization 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.