Published by Pearson (January 1, 2022) © 2016
Price Reduced From: $299.99
For courses in Image Processing and Computer Vision.
Introduce your students to image processing with the industry’s most prized text
For 40 years, Image Processing has been the foundational text for the study of digital image processing. The book is suited for students at the college senior and first-year graduate level with prior background in mathematical analysis, vectors, matrices, probability, statistics, linear systems, and computer programming. As in all earlier editions, the focus of this edition of the book is on fundamentals.
The 4th Edition, which celebrates the book’s 40th anniversary, is based on an extensive survey of faculty, students, and independent readers in 150 institutions from 30 countries. Their feedback led to expanded or new coverage of topics such as deep learning and deep neural networks, including convolutional neural nets, the scale-invariant feature transform (SIFT), maximally-stable extremal regions (MSERs), graph cuts, k-means clustering and superpixels, active contours (snakes and level sets), and exact histogram matching. Major improvements were made in reorganizing the material on image transforms into a more cohesive presentation, and in the discussion of spatial kernels and spatial filtering. Major revisions and additions were made to examples and homework exercises throughout the book. For the first time, we added MATLAB projects at the end of every chapter, and compiled support packages for students and faculty containing, solutions, image databases, and sample code.
Support materials for this title can be found here: http://www.imageprocessingplace.com
1. Introduction
What Is Digital Image Processing?
The Origins of Digital Image Processing
Examples of Fields that Use Digital Image Processing
Fundamental Steps in Digital Image Processing
Components of an Image Processing System
2. Digital Image Fundamentals
Elements of Visual Perception
Light and the Electromagnetic Spectrum. Image Sensing and Acquisition
Image Sampling and Quantization
Some Basic Relationships Between Pixels
An Introduction to the Mathematical Tools Used in Digital Image Processing
3. Intensity Transformations and Spatial Filtering
Background
Some Basic Intensity Transformation Functions
Histogram Processing. Fundamentals of Spatial Filtering
Smoothing Spatial Filters
Sharpening Spatial Filters
Combining Spatial Enhancement Methods
Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering
4. Filtering in the Frequency Domain
Background
Preliminary Concepts
Sampling and the Fourier Transform of Sampled Functions
The Discrete Fourier Transform (DFT) of One Variable
Extension to Functions of Two Variables
Some Properties of the 2-D Discrete Fourier Transform
The Basics of Filtering in the Frequency Domain
Image Smoothing Using Frequency Domain Filters
Image Sharpening Using Frequency Domain Filters
Selective Filtering
Implementation
5. Image Restoration and Reconstruction
A Model of the Image Degradation/Restoration Process
Noise Models
Restoration in the Presence of Noise Only–Spatial Filtering
Periodic Noise Reduction by Frequency Domain Filtering
Linear, Position-Invariant Degradations. Estimating the Degradation Function
Inverse Filtering
Minimum Mean Square Error (Wiener) Filtering
Constrained Least Squares Filtering. Geometric Mean Filter
Image Reconstruction from Projections.
6. Color Image Processing
Color Fundamentals
Color Models
Pseudocolor Image Processing
Basics of Full-Color Image Processing
Color Transformations. Smoothing and Sharpening
Image Segmentation Based on Color
Noise in Color Images
Color Image Compression
7. Wavelets and Multiresolution Processing
Background
Multiresolution Expansions
Wavelet Transforms in One Dimension
The Fast Wavelet Transform
Wavelet Transforms in Two
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