Digital Image Processing, Global Edition
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The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook.
Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed. For courses in Image Processing and Computer Vision. For 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 is based on an extensive survey of faculty, students, and independent readers in 5 institutions from 3 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), MERS, graph cuts, k-means clustering and superpiels, active contours (snakes and level sets), and each histogram matching.
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- Pearson International Content
- 9781292223070
- 9781292223049
- Page Fidelity (PDF)
- 4
- Rafael C. Gonzalez; Richard E. Woods
- English
- 2018-06-21
- 100
- 2
- 2
Kaflar
- Contents
- Preface
- Acknowledgments
- The Book Website
- The DIP4E Support Packages
- About the Authors
- 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
- Introduction to the Basic 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 (Lowpass) Spatial Filters
- Sharpening (Highpass) Spatial Filters
- Highpass, Bandreject, and Bandpass Filters from Lowpass Filters
- Combining Spatial Enhancement Methods
- 4 Filtering in the Frequency Domain
- Background
- Preliminary Concepts
- Sampling and the Fourier Transform of Sampled Functions
- The Discrete Fourier Transform of One Variable
- Extensions to Functions of Two Variables
- Some Properties of the 2-D DFT and IDFT
- The Basics of Filtering in the Frequency Domain
- Image Smoothing Using Lowpass Frequency Domain Filters
- Image Sharpening Using Highpass Filters
- Selective Filtering
- The Fast Fourier Transform
- 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 Using 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
- Color Image Smoothing and Sharpening
- Using Color in Image Segmentation
- Noise in Color Images
- Color Image Compression
- 7 Wavelet and Other Image Transforms
- Preliminaries
- Matrix-based Transforms
- Correlation
- Basis Functions in the Time-Frequency Plane
- Basis Images
- Fourier-Related Transforms
- Walsh-Hadamard Transforms
- Slant Transform
- Haar Transform
- Wavelet Transforms
- 8 Image Compression and Watermarking
- Fundamentals
- Huffman Coding
- Golomb Coding
- Arithmetic Coding
- LZW Coding
- Run-length Coding
- Symbol-based Coding
- Bit-plane Coding
- Block Transform Coding
- Predictive Coding
- Wavelet Coding
- Digital Image Watermarking
- 9 Morphological Image Processing
- Preliminaries
- Erosion and Dilation
- Opening and Closing
- The Hit-or-Miss Transform
- Some Basic Morphological Algorithms
- Morphological Reconstruction
- Summary of Morphological Operations on Binary Images
- Grayscale Morphology
- 10 Image Segmentation
- Fundamentals
- Point, Line, and Edge Detection
- Thresholding
- Segmentation by Region Growing and by Region Splitting and Merging
- Region Segmentation Using Clustering and Superpixels
- Region Segmentation Using Graph Cuts
- Segmentation Using Morphological Watersheds
- The Use of Motion in Segmentation
- 11 Feature Extraction
- Background
- Boundary Preprocessing
- Boundary Feature Descriptors
- Region Feature Descriptors
- Principal Components as Feature Descriptors
- Whole-Image Features
- Scale-Invariant Feature Transform (SIFT)
- 12 Image Pattern Classification
- Background
- Patterns and Pattern Classes
- Pattern Classification by Prototype Matching
- Optimum (Bayes) Statistical Classifiers
- Neural Networks and Deep Learning
- Deep Convolutional Neural Networks
- Some Additional Details of Implementation
- Bibliography
- Index
- Back Cover