Preface

Summary

The philosophical approaches pursued in this book can be divided into two groups: imaging problems involving relatively high signal-to-noise ratio (SNR) environments and problems associated with environments in which the images are corrupted by severe, usually non-Gaussian noise. The former class of problems led to the development of numerous approaches involving the so-called experimental design techniques The latter class of problems was addressed using techniques based on partition tests (Kurz, chapter III, Kersten and Kurz). The material in this book is based on experimental design techniques; it represents a graduate course offered by the senior author. The book considers the basic notions involving experimental design techniques. It is hoped that, in addition to being a text for a graduate course, the book will generate interest among imaging engineers and scientists, resulting in further development of algorithms and techniques for solving imaging problems.

The basic problems addressed in the book are line and edge detection, object detection, and image segmentation. The class of test statistics used is mainly based on various forms of the linear model involving analysis of variance (ANOVA) techniques in the framework of experimental designs. Though the statistical model is linear, the actual operations involving imaging data are nonlinear.