A computational introduction to digital image processing /

Highly Regarded, Accessible Approach to Image Processing Using Open-Source and Commercial Software A Computational Introduction to Digital Image Processing, Second Edition explores the nature and use of digital images and shows how they can be obtained, stored, and displayed. Taking a strictly eleme...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: McAndrew, Alasdair
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Boca Raton : Taylor & Francis Group, CRC Press, 2016.
Edición:Second edition.
Temas:
MATLAB.
MATLAB
Image processing > Digital techniques > Mathematics.
Traitement d'images > Techniques numériques > Mathématiques.
TECHNOLOGY & ENGINEERING > Imaging Systems.
MATHEMATICS > Graphic Methods.
Image processing > Digital techniques > Mathematics
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Machine generated contents note: 1. Introduction
  • 1.1. Images and Pictures
  • 1.2. What Is Image Processing?
  • 1.3. Image Acquisition and Sampling
  • 1.4. Images and Digital Images
  • 1.5. Some Applications
  • 1.6. Image Processing Operations
  • 1.7. An Image Processing Task
  • 1.8. Types of Digital Images
  • 1.9. Image File Sizes
  • 1.10. Image Perception
  • Exercises
  • 2. Images Files and File Types
  • 2.1. Opening and Viewing Grayscale Images
  • 2.2. RGB Images
  • 2.3. Indexed Color Images
  • 2.4. Numeric Types and Conversions
  • 2.5. Image Files and Formats
  • 2.6. Programs
  • Exercises
  • 3. Image Display
  • 3.1. Introduction
  • 3.2. The imshow Function
  • 3.3. Bit Planes
  • 3.4. Spatial Resolution
  • 3.5. Quantization and Dithering
  • 3.6. Programs
  • Exercises
  • 4. Point Processing
  • 4.1. Introduction
  • 4.2. Arithmetic Operations
  • 4.3. Histograms
  • 4.4. Lookup Tables
  • Exercises
  • 5. Neighborhood Processing
  • 5.1. Introduction
  • 5.2. Notation
  • 5.3. Filtering in MATLAB and Octave.
  • Note continued: 5.4. Filtering in Python
  • 5.5. Frequencies; Low and High Pass Filters
  • 5.6. Gaussian Filters
  • 5.7. Edge Sharpening
  • 5.8. Non-Linear Filters
  • 5.9. Edge-Preserving Blurring Filters
  • 5.10. Region of Interest Processing
  • 5.11. Programs
  • Exercises
  • 6. Image Geometry
  • 6.1. Interpolation of Data
  • 6.2. Image Interpolation
  • 6.3. General Interpolation
  • 6.4. Enlargement by Spatial Filtering
  • 6.5. Scaling Smaller
  • 6.6. Rotation
  • 6.7. Correcting Image Distortion
  • Exercises
  • 7. The Fourier Transform
  • 7.1. Introduction
  • 7.2. Background
  • 7.3. The One-Dimensional Discrete Fourier Transform
  • 7.4. Properties of the One-Dimensional DFT
  • 7.5. The Two-Dimensional DFT
  • 7.6. Experimenting with Fourier Transforms
  • 7.7. Fourier Transforms of Synthetic Images
  • 7.8. Filtering in the Frequency Domain
  • 7.9. Homomorphic Filtering
  • 7.10. Programs
  • Exercises
  • 8. Image Restoration
  • 8.1. Introduction
  • 8.2. Noise
  • 8.3. Cleaning Salt and Pepper Noise.
  • Note continued: 8.4. Cleaning Gaussian Noise
  • 8.5. Removal of Periodic Noise
  • 8.6. Inverse
  • 8.7. Wiener Filtering
  • Exercises
  • 9. Image Segmentation
  • 9.1. Introduction
  • 9.2. Thresholding
  • 9.3. Applications of Thresholding
  • 9.4. Choosing an Appropriate Threshold Value
  • 9.5. Adaptive Thresholding
  • 9.6. Edge Detection
  • 9.7. Derivatives and Edges
  • 9.8. Second Derivatives
  • 9.9. The Canny Edge Detector
  • 9.10. Corner Detection
  • 9.11. The Hough and Radon Transforms
  • Exercises
  • 10. Mathematical Morphology
  • 10.1. Introduction
  • 10.2. Basic Ideas
  • 10.3. Dilation and Erosion
  • 10.4. Opening and Closing
  • 10.5. The Hit-or-Miss Transform
  • 10.6. Some Morphological Algorithms
  • 10.7.A Note on the bwmorph Function in MATLAB and Octave
  • 10.8. Grayscale Morphology
  • 10.9. Applications of Grayscale Morphology
  • 10.10. Programs
  • Exercises
  • 11. Image Topology
  • 11.1. Introduction
  • 11.2. Neighbors and Adjacency
  • 11.3. Paths and Components.
  • Note continued: 11.4. Equivalence Relations
  • 11.5.Component Labeling
  • 11.6. Lookup Tables
  • 11.7. Distances and Metrics
  • 11.8. Skeletonization
  • 11.9. Programs
  • Exercises
  • 12. Shapes and Boundaries
  • 12.1. Introduction
  • 12.2. Chain Codes and Shape Numbers
  • 12.3. Fourier Descriptors
  • Exercises
  • 13. Color Processing
  • 13.1. What Is Color?
  • 13.2. Color Models
  • 13.3. Manipulating Color Images
  • 13.4. Pseudocoloring
  • 13.5. Processing of Color Images
  • 13.6. Programs
  • Exercises
  • 14. Image Coding and Compression
  • 14.1. Lossless and Lossy Compression
  • 14.2. Huffman Coding
  • 14.3. Run Length Encoding
  • 14.4. Dictionary Coding: LZW Compression
  • 14.5. The JPEG Algorithm
  • 14.6. Programs
  • Exercises
  • 15. Wavelets
  • 15.1. Waves and Wavelets
  • 15.2.A Simple Wavelet: The Haar Wavelet
  • 15.3. Wavelets and Images
  • 15.4. The Daubechies Wavelets
  • 15.5. Image Compression Using Wavelets
  • 15.6. High Pass Filtering Using Wavelets
  • 15.7. Denoising Using Wavelets.
  • Note continued: Exercises
  • 16. Special Effects
  • 16.1. Polar Coordinates
  • 16.2. Ripple Effects
  • 16.3. General Distortion Effects
  • 16.4. Pixel Effects
  • 16.5. Color Images
  • Exercises
  • A. Introduction to MATLAB and Octave
  • A.1. Introduction
  • A.2. Basic Use
  • A.3. Variables and the Workspace
  • A.4. Dealing with Matrices
  • A.5. Plots
  • A.6. Online Help
  • A.7. Programming
  • Exercises
  • B. Introduction to Python
  • B.1. Basic Use
  • B.2. Arrays
  • B.3. Graphics and Plots
  • B.4. Programming
  • C. The Fast Fourier Transform.

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