Artificial Neural Networks Based Image Processing & Pattern Recognition: From Concepts to Real-World Applications

The main goal of this paper is to present artificial neural network potential, through main ANN models and based techniques, to solve real world industrial problems dealing with Image processing and pattern recognition fields.

[1]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[2]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[3]  Kurosh Madani,et al.  Image Processing Using RBF like Neural Networks: A ZISC-036 Based Fully Parallel Implementation Solving Real World and Real Complexity Industrial Problems , 2004, Applied Intelligence.

[4]  Kurosh Madani,et al.  Visual Probe Mark Inspection, Using Hardware Implementation of Artificial Neural Networks, in VLSI Production , 1997, IWANN.

[5]  William E. Faller,et al.  Real-time prediction of unsteady aerodynamics: Application for aircraft control and manoeuvrability enhancement , 1995, IEEE Trans. Neural Networks.

[6]  Volodymyr Turchenko,et al.  Sensor errors prediction using neural networks , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[7]  E.E. Pissaloux,et al.  Image Processing , 1994, Proceedings. Second Euromicro Workshop on Parallel and Distributed Processing.

[8]  Leonardo Maria Reyneri.  Weighted Radial Basis Functions for improved pattern recognition and signal processing , 2006, Neural Processing Letters.

[9]  Lawrence O'Gorman,et al.  Document Image Analysis , 1996 .

[10]  Thomas S. Huang,et al.  Image processing , 1971 .

[11]  Charles W. Anderson,et al.  Determining Mental State from EEG Signals Using Parallel Implementations of Neural Networks , 1995, Sci. Program..