Handbook of image processing operators
Handbook of image processing operators
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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
Human iris detection using fast cooperative modular neural nets and image decomposition
Machine Graphics & Vision International Journal
Speeding-up normalized neural networks for face/object detection
Machine Graphics & Vision International Journal
New fast normalized neural networks for pattern detection
Image and Vision Computing
EURASIP Journal on Applied Signal Processing
Learning Patterns and Pattern Sequences by Self-Organizing Nets of Threshold Elements
IEEE Transactions on Computers
A Flexible Non-linear PCA Encoder for Still Image Compression
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Fast Code Detection Using High Speed Time Delay Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
A New Implementation for High Speed Normalized Neural Networks in Frequency Space
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I
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 technique for detecting dental diseases by using high speed artificial neural networks
WSEAS Transactions on Computers
A real-time intrusion detection algorithm for network security
WSEAS TRANSACTIONS on COMMUNICATIONS
New Fast Decision Tree Classifier for Identifying Protein Coding Regions
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Fast Time Delay Neural Networks for Detecting DNA Coding Regions
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
A novel high-speed neural model for fast pattern recognition
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Integrating neural networks and PCA for fast covert surveillance
CI'10 Proceedings of the 4th WSEAS international conference on Computational intelligence
A new approach for prediction by using integrated neural networks
AMERICAN-MATH'11/CEA'11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications
An intelligent approach for fast detection of biological viruses in DNA sequence
ACELAE'11 Proceedings of the 10th WSEAS international conference on communications, electrical & computer engineering, and 9th WSEAS international conference on Applied electromagnetics, wireless and optical communications
A universal PCA for image compression
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
| Hi-index | 0.00 |
For the purpose of efficient observation and monitoring in information security, the input image is required to be transferred from one place to another for decision making (higher administration). In this paper, the hybrid k-PCA method is used for efficient compression. However, some pixels in the input image may be missed or distorted during the transmission process. Therefore, Hopfield neural networks are used for retrieving the original of distorted images. Then fast feedforward neural networks (FFNNs) are applied for face detection in the received image. The speed of these neural networks is accelerated by modifying its algorithm. This is done by applying cross correlation in the frequency domain between the received image and the weights of neural networks. The result of cross correlation implemented in the frequency domain is the same as that one obtained in time domain. Moreover, the speed of operation is faster than performing cross correlation in time domain. It is proved mathematically and practically that the proposed algorithm is fast and efficient in retrieving missed pixels in distorted images.