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Burger W., Burge M.J. Principles of Digital Image Processing. Advanced Methods

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Burger W., Burge M.J. Principles of Digital Image Processing. Advanced Methods
Издательство Springer, 2013, -374 pp.
This is the 3rd volume of the authors’ textbook series on Principles of Digital Image Processing that is predominantly aimed at undergraduate study and teaching:
Volume 1: Fundamental Techniques (/file/584236/),
Volume 2: Core Algorithms (/file/584329/),
Volume 3: Advanced Methods (this volume).
While it builds on the previous two volumes and relies on the their proven format, it contains all new material published by the authors for the first time. The topics covered in this volume are slightly more advanced and should thus be well suited for a follow-up undergraduate or Master-level course and as a solid reference for experienced practitioners in the field.
The topics of this volume range over a variety of image processing applications, with a general focus on classic techniques that are in wide use but are at the same time challenging to explore with the existing scientific literature. In choosing these topics, we have also considered input received from students, lecturers and practitioners over several years, for which we are very grateful. While it is almost unfeasible to cover all recent developments in the field, we focused on popular workhorse techniques that are available in many image processing systems but are often used without a thorough understanding of their inner workings. This particularly applies to the contents of the first five chapters on automatic thresholding, filters and edge detectors for color images, and edge-preserving smoothing. Also, an extensive part of the book is devoted to David Lowe’s popular SIFT method for invariant local feature detection, which has found its way into so many applications and has become a standard tool in the industry, despite (as the text probably shows) its inherent sophistication and complexity. An additional bonus chapter on Synthetic Gradient Noise, which could not be included in the print version, is available for download from the book’s website.
As in the previous volumes, our main goal has been to provide accurate, understandable, and complete algorithmic descriptions that take the reader all the way from the initial idea through the formal description to a working implementation. This may make the text appear bloated or too mathematical in some places, but we expect that interested readers will appreciate the high level of detail and the decision not to omit the (sometimes essential) intermediate steps. Wherever reasonable, general prerequisites and more specific details are summarized in the Appendix, which should also serve as a quick reference that is supported by a carefully compiled Index. While space constraints did not permit the full source code to be included in print, complete (Java) implementations for each chapter are freely available on the book’s website (see below). Again we have tried to make this code maximally congruent with the notation used in the text, such that readers should be able to easily follow, execute, and extend the described steps.
Introduction
Automatic Thresholding
Filters for Color Images
Edge Detection in Color Images
Edge-Preserving Smoothing Filters
Fourier Shape Descriptors
SIFT—Scale-Invariant Local Features
A. Mathematical Symbols and Notation
B. Vector Algebra and Calculus
C. Statistical Prerequisites
D. Gaussian Filters
E. Color Space Transformations
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