Image Processing and Pattern Recognition

by Cornelius T. Leondes

Overview

Image Processing and Pattern Recognition covers major applications in the field, including optical character recognition, speech classification, medical imaging, paper currency recognition, classification reliability techniques, and sensor technology. The text emphasizes algorithms and architectures for achieving practical and effective systems, and presents many examples. Practitioners, researchers, and students in computer science, electrical engineering, andradiology, as well as those working at financial institutions, will value this unique and authoritative reference to diverse applications methodologies.

Product Details

ISBN-13:	9780124438651
Publisher:	Elsevier Science
Publication date:	12/11/1997
Series:	Neural Network Systems Techniques and Applications , #5
Pages:	386
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

Cornelius T. Leondes received his B.S., M.S., and Ph.D. from the University of Pennsylvania and has held numerous positions in industrial and academic institutions. He is currently a Professor Emeritus at the University of California, Los Angeles. He has also served as the Boeing Professor at the University of Washington and as an adjunct professor at the University of California, San Diego. He is the author, editor, or co-author of more than 100 textbooks and handbooks and has published more than 200 technical papers. In addition, he has been a Guggenheim Fellow, Fulbright Research Scholar, IEEE Fellow, and a recipient of IEEE's Baker Prize Award and Barry Carlton Award.

Table of Contents

Lampinen, Pattern Recognition. Alpaydin and Gurgen, Comparison of Statistical and Neural Classifiers and their Applications to Optical Character Recognition and Speech Classification. Sun and Nekovei, MedicalImaging. Takeda and Omatu, Paper Currency Recognition. Cordella and Stefano, Neural Network Classification Reliability: Problems and Applications. Yagi, Kobayaski, and Matsumoto, Parallel Analog Image Processing: Solving Regularization Problems with Architecture Inspired by the Vertebrate Retinal Circuit. Setiono, Algorithmic Techniques and their Applications. Chen and Chang, Learning Algorithms and Applications of Principal Component Analysis. Merat and Villalobos, Learning Evaluation and Pruning Techniques.

Editorial Reviews

"A highly specialized work...but full of varied information" —P.W. Hawkes, Ultramicroscopy 80

From the Publisher