Ebook: Principles of Digital Image Processing: Core Algorithms

This easy-to-follow textbook is the second 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 extends the introductory material presented in the first volume (Fundamental Techniques) with additional techniques that form part of the standard image-processing toolbox.

Features and topics:

• Practical examples and carefully constructed chapter-ending exercises drawn from the authors' years of experience teaching this material
• 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 (and rapid prototyping of new) applications
• Uses 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.com
• Additional presentation tools for instructors including a complete set of figures, tables, and mathematical elements

This 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 in the Center for National Security and Intelligence at Noblis in Washington, D.C.

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This book explains the principal techniques of data mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. This will benefit readers of all levels, from those who use data mining via commercial packages, right through to academic researchers. The book aims to help the general reader develop the necessary understanding to use commercial data mining packages, and to enable advanced readers to understand or contribute to future technical advances. Includes exercises and glossary Introduction.- Regions in Binary Images.- Detecting Simple Curves.- Corner Detection.- Color Quantization.-Colorimetric Color Spaces.- Introduction to spectral techniques.- The Discrete Fourier Transform in 2D.- The Discrete Cosine Transform (DCT).- Geometric Operations.- Comparing Images.- Appendix A: Mathematical Notation.- Appendix B: Source Code.- Bibliography