Advances in Computer Vision and Pattern Recognition- Statistical and Neural Classifiers An Integrated Approach to Design

Neural networks have not only provided a variety of novel or supplementary approaches for pattern recognition tasks, but have also offered architectures on which many well-known statistical pattern recognition algorithms can be mapped for efficient (hardware) implementation.



The classification of patterns is an important area of research which is central to all pattern recognition fields, including speech, image, robotics, and data analysis. Neural networks have been used successfully in a number of these fields, but so far their application has been based on a "black box approach", with no real understanding of how they work.In this book, Sarunas Raudys - an internationally respected researcher in the area - provides an excellent mathematical and applied introduction to how neural network classifiers work and how they should be used to optimal effect. Among the topics covered are:- Different types of neural network classifiers;- A taxonomy of pattern classification algorithms;- Performance capabilities and measurement procedures;- Which features should be extracted from raw data for the best classification results.This book will provide essential reading for anyone researching or studying relevant areas of pattern recognition (such as image processing, speech recognition, robotics, and multimedia). It will also be of interest to anyone studing or researching in applied neural networks.