Synopses & Reviews
This easy-to-follow textbook is the third of three volumes which provide a modern, algorithmic introduction to digital image processing, designed to be used both by learners desiring a firm foundation on which to build, and practitioners in search of critical analysis and concrete implementations of the most important techniques. This volume builds upon the introductory material presented in the first two volumes (Fundamental Techniques and Core Algorithms) with additional key concepts and methods in image processing. Features and topics: Practical examples and carefully constructed chapter-ending exercises drawn from the authors' years of experience teaching this materialReal implementations, concise mathematical notation, and precise algorithmic descriptions designed for programmers and practitionersEasily adaptable Java code and completely worked-out examples for easy inclusion in existing (and rapid prototyping of new) applicationsUses ImageJ, the image processing system developed, maintained, and freely distributed by the U.S. National Institutes of Health (NIH)Provides a supplementary website with the complete Java source code, test images, and corrections--www.imagingbook.comAdditional presentation tools for instructors including a complete set of figures, tables, and mathematical elementsThis thorough, reader-friendly text will equip undergraduates with a deeper understanding of the topic and will be invaluable for further developing knowledge via self-study. Wilhelm Burger, Ph.D., is the director of the Digital Media degree programs at the Upper Austria University of Applied Sciences at Hagenberg. Mark J. Burge, Ph.D., is a senior principal at MITRE in Washington, D.C.
Synopsis
This easy-to-follow textbook is the third of 3 volumes which provide a modern, algorithmic introduction to digital image processing, designed to be used both by learners desiring a firm foundation on which to build, and practitioners in search of critical analysis and modern implementations of the most important techniques.
The reader-friendly text builds on the contents of the previous volumes, presenting additional advanced methods, including those for performing dynamic point operations, non-linear edge-preserving filters and filters for color images, advanced edge detection operators, local feature extraction, curve fitting, etc. This volume continues to provide readers with proven concepts, offering a selection of advanced standard techniques and recent methods that are widely used in research and practical applications.
This reader-friendly text will equip undergraduates with a deeper understanding of the topic as well as being valuable for further developing knowledge for self-study.
Synopsis
This textbook is the third of three volumes which provide a modern, algorithmic introduction to digital image processing, designed to be used both by learners desiring a firm foundation on which to build, and practitioners in search of critical analysis and concrete implementations of the most important techniques. This volume builds upon the introductory material presented in the first two volumes with additional key concepts and methods in image processing. Features: practical examples and carefully constructed chapter-ending exercises; real implementations, concise mathematical notation, and precise algorithmic descriptions designed for programmers and practitioners; easily adaptable Java code and completely worked-out examples for easy inclusion in existing applications; uses ImageJ; provides a supplementary website with the complete Java source code, test images, and corrections; additional presentation tools for instructors including a complete set of figures, tables, and mathematical elements.
Synopsis
This book offers key concepts and methods in image processing, examples and exercises, Java code and worked-out examples for easy inclusion in existing applications, and a supplementary website with complete Java source code, test images, and corrections.
Table of Contents
Introduction
Automatic Thresholding
Filters for Color Images
Edge Detection in Color Images
Edge-Preserving Smoothing Filters
Fourier Shape Descriptors
SIFT--Scale-Invariant Local Features
Mathematical Symbols and Notation
Vector Algebra and Calculus
Statistical Prerequisites
Gaussian Filters
Color Space Transformations