The fourth edition of , which celebrates the book’s 40th anniversary, continues its cutting-edge
focus on contemporary developments in all mainstream areas of image processing. It focuses on material that is
fundamental and has a broad scope of application.
About the Author
Rafael C. Gonzalez received the B.S.E.E. degree from the University of Miami in 1965 and the M.E. and Ph.D. degrees in electrical engineering from the University of Florida, Gainesville, in 1967 and 1970, respectively. He joined the Electrical and Computer Engineering Department at University of Tennessee, Knoxville (UTK) in 1970, where he became Associate Professor in 1973, Professor in 1978, and Distinguished Service Professor in 1984.He is currently a Professor Emeritus at UTK.
Gonzalez is the founder of the Image & Pattern Analysis Laboratory and the Robotics & Computer Vision Laboratory at the University of Tennessee.
Richard E. Woods earned his B.S., M.S., and Ph.D. degrees in Electrical Engineering from the University of Tennessee, Knoxville. His professional experiences range from entrepreneurial to the more traditional academic, consulting; governmental, and industrial pursuits. Most recently, he founded MedData Interactive, a high technology company specializing in the development of hand-held computer systems for medical applications. He was also a founder and Vice President of Perceptics Corporation.
Features
1. Coverage of graph cuts and their application to segmentation.
2. A discussion of superpixels and their use in region segmentation.
3. 425 new images, 135 new drawings, 220 new exercises and 120 MATLAB projects.
4. Two new chapters:
a. A chapter dealing with active contours for image segmentation, including snakes and level sets.
b. A chapter that brings together wavelets, several new transforms, and many of the image transforms that were scattered throughout the book.
5. A complete update of the image pattern recognition chapter to incorporate new material on deep neural networks, backpropagation, deep learning, and especially, deep convolutional neural networks.
6. Coverage of feature extraction, including the Scale Invariant Feature Transform (SIFT, maximally stable extremal regions (MSERs), and corner detection.
7. Coverage of the fundamentals of spatial filtering, image transforms, and finite differences with a focus on edge detection.
Table Content
Chapter 1 Introduction
Chapter 2 Digital Image Fundamentals
Chapter 3 Intensity Transformations and Spatial Filtering
Chapter 4 Filtering in the Frequency Domain
Chapter 5 Image Restoration and Reconstruction
Chapter 6 Wavelet and Other Image Transforms
Chapter 7 Color Image Processing
Chapter 8 Image Compression and Watermarking
Chapter 9 Morphological Image Processing
Chapter 10 Image Segmentation I: Edge Detection,
Chapter 11 Image Segmentation II: Active Contours: Snakes and Level Sets
Chapter 12 Feature Extraction
Chapter 13 Image Pattern Classification
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