The NATO Advanced Research Workshop on Signal Processing and Pattern Recognition in Nondestructive Evaluation (NOE) of Materials was held August 19-22, 1987 at the Manoir St-Castin, Lac Beauport, Quebec, Canada. Modern signal processing, pattern recognition and artificial intelligence have been playing an increasingly important role in improving nondestructive evaluation and testing techniques. The cross fertilization of the two major areas can lead to major advances in NOE as well as presenting a new research area in signal processing. With this in mind, the Workshop provided a good review of progress and comparison of potential techniques, as well as constructive discussions and suggestions for effective use of modern signal processing to improve flaw detection, classification and prediction, as well as material characterization. This Proceedings volume includes most presentations given at the Workshop. This publication, like the meeting itself, is unique in the sense that it provides extensive interactions among the interrelated areas of NOE. The book starts with research advances on inverse problems and then covers different aspects of digital waveform processing in NOE and eddy current signal analysis. These are followed by four papers of pattern recognition and AI in NOE, and five papers of image processing and reconstruction in NOE. The last two papers deal with parameter estimation problems. Though the list of papers is not extensive, as the field of NOE signal processing is very new, the book has an excellent collection of both tutorial and research papers in this exciting new field.

The overall goal of this project has been to further investigate signal processing and pattern recognition techniques as to how they apply in the nondestructive evaluation of materials for classifying ultrasonic pulse echo waveforms. Computer programs were developed to implement algorithms to generate power spectrum, cepstrum, and auto-correlation waveforms from the ultrasonic pulse echo waveforms. These algorithms have a firm statistical foundation and also have properties associated with them that allow the Fast Fourier Transform to be utilized in an efficient manner. Also, statistical features were extracted from the waveforms. The features were then input to pattern recognition techniques in order to classify the data into appropriate material defects. The procedure outlined above was implemented with 49 ultrasonic pulse echo waveforms obtained from flat-bottom holes of eight different diameters. A recognition accuracy of 98% has been attained when the flat-bottom holes are classified into two categories using only one feature from the original ultrasonic pulse echo waveforms reflected from the flat-bottom holes. The same results are achieved when the one feature is either the maximum amplitude, the root-mean-square value, or the variance of the waveform. An unexpected result was also observed when a time series method was applied to the portions of the ultrasonic pulse echo waveforms that were reflected from the backwalls instead of the flat-bottom holes.

In this research the use of modern signal processing and digital pattern recognition in ultrasonic NDE of materials is examined. The high resolution Burg's maximum entropy spectral analysis is shown to provide vital information on the differences among various defect geometries in an aluminum plate. A number of important features computed from the power spectrum have been identified, which in conjunction with correlation matching and by using multi-transducer data can effectively classify the test specimen into three major categories: flat, angular cut, and circular holes and to determine the angles in a multi-stage classification procedure. Size estimation is still a problem that requires further study. Three estimation is still a problem that requires further study. Three deconvolved results. The research clearly shows that the effectiveness of ultrasonic spectroscopy as pioneered by O. Gericke can be significantly improved by using modern digital signal processing and digital pattern recognition.

Ultrasonic methods have been very popular in nondestructive testing and characterization of materials. This book deals with both industrial ultrasound and medical ultrasound. The advantages of ultrasound include flexibility, low cost, in-line operation, and providing data in both signal and image formats for further analysis. The book devotes 11 chapters to ultrasonic methods. However, ultrasonic methods can be much less effective with some applications. So the book also has 14 chapters catering to other or advanced methods for nondestructive testing or material characterization. Topics like structural health monitoring, Terahertz methods, X-ray and thermography methods are presented. Besides different sensors for nondestructive testing, the book places much emphasis on signal/image processing and pattern recognition of the signals acquired.

The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference. This indispensable handbook will continue to serve as an authoritative and comprehensive guide in the field.

"The book provides an up-to-date and authoritative treatment of pattern recognition and computer vision, with chapters written by leaders in the field. On the basic methods in pattern recognition and computer vision, topics range from statistical pattern recognition to array grammars to projective geometry to skeletonization, and shape and texture measures."--BOOK JACKET.