Professor Victor-Emil Neagoe
Polytechnic University of Bucharest,
ROMANIA

Email: victoremil@gmail.com
Tel.+40 721 23 50 20

Abstract: We present the model of Concurrent Neural Classifiers (CNC) representing a collection of small neural networks, which use a global winner-takes-all strategy. Each neural module is trained to correctly classify the patterns of one class only and the number of modules equals the number “M” of classes. One considers the case of choosing the SOM (Self-Organized-Map) as a neural module. The CNC training technique is a supervised one, but for any individual net, the SOM specific unsupervised training algorithm is used. We built “M” training pattern sets and each neural module is trained with the pattern set characterized by the corresponding class label.

Several presented CNC applications are dedicated to biometrics; first one has as target the recognition of color facial images and second belongs to iris recognition. One also considers a CNC application corresponding to the case of decision fusion by implementation of a multimodal biometric model.

Second series of applications focuse on the CNC model for pattern recognition in multispectral satellite imagery. The implemented neural classifiers are evaluated using some LANDSAT ETM+ images composed by a set of multispectral pixels, each pixel corresponding to one of several categories (vegetation, buildings, water, and so on).

Third kind of considered CNC applications correspond to visual identification of road direction of an autonomous vehicle. We present the experimental results obtained by computer simulation . We have also performed, trained and tested a real time neural path follower based on CNC model, implemented on a mobile robot (car toy).


Brief Biography of the Speaker:
Dr. Victor-Emil Neagoe is a Professor of the Department of Electronics, Telecommunications, and Information Technology at the Polytechnic University of Bucharest, Romania.
He teaches the following courses : Pattern Recognition and Artificial Intelligence; Digital Signal Processing; Computational Intelligence ; Detection and Estimation for Information Processing. He co-ordinates 12 Ph.D. candidates.
His research interest corresponds to the fields of pattern recognition, computational intelligence, biometric technology , satellite image analysis and sampling theory.
Prof. Neagoe is author of more than 110 published papers.
His has internationally recognized results concerning concurrent self-organized maps, face recognition, optimum color conversion, syntactical self-organized maps, nonuniform sampling theorems, inversion of the Van der Monde matrix, predictive ordering and linear approximation for image data compression, Legendre descriptors for classification of polygonal closed curves.
He has been included in Who’s Who in the World and Europe 500 and he has been nominated by the American Biographical Institute for American Medal of Honor and for World Medal of Honor.
He has been a Member IEEE since 1978 and a Senior Member IEEE since 1984.