Synopses & Reviews
Course is taught in both EE and CS Departments. This book is comprehensive enough to use in two courses: Image Processing (usually EE) and Machine Vision (EE or CS). This comprehensive text provides deep and wide coverage of the full range of topics encountered in the field of image processing and machine vision.Sonka's encyclopedic coverage of topics is wider than that found in any competing book on the market. In addition, while advanced mathematics is not needed to understand basic concepts, rigorous mathematical coverage is included for more advanced readers. The book is especially strong and up-to-date in its treatment of 3D vision, with many topics not covered at all in competing books. The book is also distinguished by the way the authors use easy-to-understand algorithm descriptions to explain difficult concepts, and a wealth of carefully selected problems and examples that can be worked with any general-purpose image processing software package or programming environment.
Synopsis
Image Processing, Analysis and Machine Vision represent an exciting part of modern cognitive and computer science. Following an explosion of inter est during the Seventies, the Eighties were characterized by the maturing of the field and the significant growth of active applications; Remote Sensing, Technical Diagnostics, Autonomous Vehicle Guidance and Medical Imaging are the most rapidly developing areas. This progress can be seen in an in creasing number of software and hardware products on the market as well as in a number of digital image processing and machine vision courses offered at universities world-wide. There are many texts available in the areas we cover - most (indeed, all of which we know) are referenced somewhere in this book. The subject suffers, however, from a shortage of texts at the 'elementary' level - that appropriate for undergraduates beginning or completing their studies of the topic, or for Master's students - and the very rapid developments that have taken and are still taking place, which quickly age some of the very good text books produced over the last decade or so. This book reflects the authors' experience in teaching one and two semester undergraduate and graduate courses in Digital Image Processing, Digital Image Analysis, Machine Vision, Pattern Recognition and Intelligent Robotics at their respective institutions."
Description
Includes bibliographical references (p. 534-542) and index.
Table of Contents
LIST OF ALGORITHMS / LIST OF SYMBOLS AND ABBREVIATIONS / PREFACE / COURSE CONTENTS 1. INTRODUCTION Summary / Exercises / References 2. THE DIGITIZED IMAGE AND ITS PROPERTIES Basic Concepts / Image Digitization / Digital Image Properties / Summary / Exercises / References 3. DATA STRUCTURES FOR IMAGE ANALYSIS Levels of Image Data Representation / Traditional Image Data Structures / Hierarchical Data Structures / Summary / Exercises / References 4. IMAGE PRE-PROCESSING Pixel Brightness Transformations / Geometric Transformations / Local Pre-Processing / Image Restoration / Summary / Exercises / References 5. SEGMENTATION Thresholding / Edge-Based Segmentation / Region-Based Segmentation / Matching / Advanced Optimal Border and Surface Detection Approaches / Summary / Exercises / References 6. SHAPE REPRESENTATION AND DESCRIPTION Region Identification / Contour-Based Shape Representation and Description / Region-Based Shape Representation and Description / Shape Classes / Summary / Exercises / References 7. OBJECT RECOGNITION Knowledge Representation / Statistical Pattern Recognition / Neural Nets / Syntactic Pattern Recognition / Recognition As Graph Matching / Optimization Techniques In Recognition / Fuzzy Systems / Summary / Exercises / References 8. IMAGE UNDERSTANDING Image Understanding Control Strategies / Active Contour Models-Snakes / Point Distribution Models / Pattern Recognition Methods In Image Understanding / Scene Labeling and Constraint Propagation / Semantic Image Segmentation and Understanding / Hidden Markov Models / Summary / Exercises / References 9. 3D VISION, GEOMETRY, AND RADIOMETRY 3D Vision Tasks / Geometry for 3D Vision / Radiometry and 3D Vision / Summary / Exercises / References 10. USE OF 3D VISION Shape From X / Full 3D Objects / 3D Model-Based Vision / 2D View-Based Representations of A 3D Scene / Summary / Exercises / References 11. MATHEMATICAL MORPHOLOGY Basic Morphological Concepts / Four Morphological Principles / Binary Dilation and Erosion / Gray-Scale Dilation and Erosion / Skeletons and Object Marking / Granulometry / Morphological Segmentation and Watersheds / Summary / Exercises / References 12. LINEAR DISCRETE IMAGE TRANSFORMS Basic Theory / Fourier Transform / Hadamard Transform / Discrete Cosine Transform / Wavelets / Other Orthogonal Image Transforms / Applications of Discrete Image Transforms / Summary / Exercises / References 13. IMAGE DATA COMPRESSION Image Data Properties / Discrete Image Transforms In Image Data Compression / Predictive Compression Methods / Vector Quantization / Hierarchical and Progressive Compression Methods / Comparison of Compression Methods / Other Techniques / Coding / JPEG and MPEG Image Compression / Summary / Exercises / References 14. TEXTURE Statistical Texture Description / Syntactic Texture Description Methods / Hybrid Texture Description Methods / Texture Recognition Method Applications / Summary / Exercises / References 15. MOTION ANALYSIS Differential Motion Analysis Methods / Optical Flow / Analysis Based on Correspondence of Interest Points / Kalman Filters / Summary / Exercises / References 16. CASE STUDIES An Optical Music Recognition System / Automated Image Analysis In Cardiology / Automated Identification of Airway Trees / Passive Surveillance / References / INDEX