Supervised unsupervised Pattern Recognition Feature Extraction and Computational

This volume describes the application of supervised and unsupervised pattern recognition schemes to the classification of various types of waveforms and images. An optimization routine, ALOPEX, is used to train the network while decreasing the likelihood of local solutions. The chapters included in this volume bring together recent research of more than ten authors in the field of neural networks and pattern recognition. All of these contributions were carried out in the Neuroelectric and Neurocomputing Laboratories in the Department of Biomedical Engineering at Rutgers University. The chapters span a large variety of problems in signal and image processing, using mainly neural networks for classification and template matching. The inputs to the neural networks are features extracted from a signal or an image by sophisticated and proven state-of-the-art techniques from the fields of digital signal processing, computer vision, and image processing. In all examples and problems examined, the biological equivalents are used as prototypes and/or simulations of those systems were performed while systems that mimic the biological
functions are built.