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Digital Image Processing 4th Edition By Rafael Gonzalez, Richard Woods

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Pages: 1,022
File size: 14.91 MB
Format: Digital PDF
Course: Digital Marketing
Category: eBook[PDF]

About this ebook

The 4th Edition of Digital Image Processing by Rafael Gonzalez and Richard Woods is structured into 13 comprehensive chapters. This major revision heavily updates core concepts and introduces new contemporary areas like deep learning, convolutional neural networks (CNNs), active contours, and graph cuts. [1, 2, 3, 4]

Here is the structured breakdown of the chapters and key topics covered in the textbook: [1]

1. Foundational Concepts

Chapter 1: Introduction — Definition and origins of digital image processing, components of an image processing system, and fields that use it (gamma-ray, X-ray, UV, visible, infrared, microwave, and radio band imaging). [1, 2]
Chapter 2: Digital Image Fundamentals — Elements of visual perception, light and the electromagnetic spectrum, image sensing/acquisition, sampling, quantization, basic pixel relationships, and introductory mathematical tools. This edition features expanded coverage of random numbers and probability applications. [1, 2, 3]

2. Image Enhancement, Filtering & Transforms

Chapter 3: Intensity Transformations and Spatial Filtering — Basic intensity transformation functions, histogram processing (including exact histogram matching), spatial filtering mechanics, smoothing/sharpening filters, and fuzzy techniques. [1, 2]
Chapter 4: Filtering in the Frequency Domain — Reorganized for better clarity; introduces the Fourier transform, the frequency domain, sampling, and frequency-domain smoothing and sharpening filters. [1, 2]
Chapter 5: Image Restoration and Reconstruction — Models of image degradation/noise, spatial noise filtering, periodic noise reduction, linear position-invariant degradations, inverse filtering, Wiener filtering, and constrained least squares filtering. [1, 2, 3]
Chapter 6: Wavelet and Other Image Transforms — A completely new chapter to this edition that provides a unified, cohesive look at wavelets alongside several foundational image transforms previously scattered throughout the text. [1]

3. Color, Compression & Morphology

Chapter 7: Color Image Processing — Color models (RGB, HSI, CMY, CMYK), pseudocolor and full-color image processing, color transformations, smoothing, sharpening, and segmentation.
Chapter 8: Image Compression and Watermarking — Fundamentals of image compression, coding redundancy, spatial and temporal redundancy, lossy/lossless compression standards, and digital image watermarking techniques.
Chapter 9: Morphological Image Processing — Binary morphology (erosion, dilation, opening, closing), hit-or-miss transform, morphological algorithms (boundary extraction, hole filling, thinning, thickening), and extension to grayscale morphology. [1]

4. Advanced Image Analysis & Machine Learning

Chapter 10: Image Segmentation I: Edge Detection, Thresholding, and Region Growing — Point, line, and edge detection, thresholding techniques, region-based segmentation, and updated material introducing K-means clustering and superpixels.
Chapter 11: Image Segmentation II: Active Contours: Snakes and Level Sets — An entirely new chapter focusing on advanced, deformable segmentation models, level sets, and the implementation of graph cuts.
Chapter 12: Feature Extraction — Boundary descriptors, regional descriptors (including texture), principal components analysis (PCA), and new contemporary additions like the Scale-Invariant Feature Transform (SIFT).
Chapter 13: Image Pattern Classification — Foundational pattern recognition concepts, structural methods, and a massive new section on deep learning, deep neural networks, and Convolutional Neural Networks (CNNs). [1, 2, 3, 4]