About the Book

A complete update of the image pattern recognition chapter to incorporate new material, including deep neural networks, backpropagation, deep learning, and, especially, deep convolutional neural networks.

Expanded coverage of feature extraction, including maximally stable extremal regions, and the Scale Invariant Feature Transform (SIFT).
A discussion of superpixels and their use in region segmentation.
Coverage of graph cuts and their application to segmentation.
An introduction to segmentation using active contours (snakes and level sets).
New material related to histogram matching.
Expanded coverage of the fundamentals of spatial filtering.
A more comprehensive and cohesive coverage of image transforms.
A more complete presentation of finite differences, with a focus on edge detection.
More homework problems at the end of the chapters.
More examples.
MATLAB computer projects.

Content updates

Chapter 1: Some figures were updated and parts of the text were rewritten to correspond to changes in later chapters.
Chapter 2: A new section dealing with random numbers and probability, with an emphasis on their application to image processing. Many sections and examples were rewritten for clarity.

12 new examples, 31 new images, 22 new drawings, 32 new exercises, and 10 new MATLAB projects.

Chapter 3: A new section on exact histogram matching, a discussion on separable filter kernels, expanded coverage on the properties of lowpass Gaussian kernels, and highpass, bandreject, and bandpass filters.

6 new examples, 67 new images, 18 new line drawings, 31 new exercises, and 10 new MATLAB projects.

Chapter 4: Several sections were revised to improve the clarity of presentation.

35 new images, 4 new line drawings, 25 new exercises, and 10 new MATLAB projects.

Chapter 6: A new chapter that brings together wavelets, several new transforms, and many of the image transforms that were scattered throughout the book. The emphasis of this chapter is on a cohesive presentation of these transforms from a unified point of view.

Chapter 7: Material dealing with color image processing was moved to this chapter. Several sections were clarified, and the explanation of the CMY and CMYK color models was expanded.

Chapter 8: Numerous clarifications and minor improvements to the presentation.

Chapter 9: A complete rewrite of several sections, including redrafting of several line drawings.

Chapter 10: Several sections were rewritten for clarity. Updated the chapter by adding coverage of finite differences, K-means clustering, superpixels, and graph cuts.

4 new examples, 31 new images, 3 new drawings, 8 new exercises, and 10 new MATLAB projects.

Chapter 11: A new chapter dealing with active contours for image segmentation, including snakes and level sets. An important feature in this chapter is that it presents a derivation of the fundamental snake equation as well as a derivation of the level set equation. Both equations are derived starting from basic principles, and the methods are illustrated with numerous examples in order to bring this material to a level that could be understood by beginners in the field.

17 new examples, 141 new images, 19 new drawings, 37 new problems, and 10 new MATLAB projects.

Chapter 12: Chapter on feature extraction, which was moved from its 11th position in the previous edition. Updated with numerous topics, improvements in the clarity of presentation, added coverage of slope change codes, expanded explanation of skeletons, medial axes, and the distance transform, and new basic descriptors of compactness, circularity, and eccentricity. New material includes coverage of the Harris-Stephens corner detector, and a presentation of maximally stable extremal regions. A major addition to the chapter is a comprehensive discussion dealing with the Scale-Invariant Feature Transform (SIFT).

65 new images, 15 new drawings, 4 new examples, 15 new exercises, and 10 new MATLAB projects.

Chapter 13: Image pattern recognition chapter that was Chapter 12 in the previous edition. Now includes coverage of deep convolutional neural networks, an extensive rewrite of neural networks, deep learning, and a comprehensive discussion on fully-connected, deep neural networks that includes derivation of backpropagation starting from basic principles.

Digital Image Processing, 4th edition