A Course on Digital Image Processing with MATLAB® (Second Edition).

Format:

Book

Author/Creator:

Thiruvikraman, P. K.

Series:

IOP Ebooks Series

Language:

English

Subjects (All):

Image processing.

Digital images.

Physical Description:

1 online resource (333 pages)

Edition:

2nd ed.

Place of Publication:

Bristol : Institute of Physics Publishing, 2024.

Summary:

Designed for a one semester course, the aim of this book is to concentrate on the principles and techniques of image processing. This second edition includes important updates to the first edition, as well as two entirely new chapters, making the book ideal for advanced students in physics and engineering.

Contents:

Intro

Acknowledgements

Author biography

P K Thiruvikraman

Chapter Introduction

1.1 The scope and importance of digital image processing

1.2 Images

1.3 Digital images

1.4 Processes involved in image processing and recognition

1.5 Applications of image processing

Exercises

Chapter Image enhancement in the spatial domain

2.1 Enhancement of contrast

2.2 Gray level transformations

2.2.1 Thresholding

2.2.2 Power law, log, and exp transformations

2.2.3 Piecewise linear transformations

2.2.4 Gray level slicing

2.3 Bit plane slicing

2.4 Histogram processing

2.4.1 Histogram equalization

2.4.2 Histogram specification

2.5 Filtering in the spatial domain

2.5.1 Averaging

2.5.2 Median filter

2.6 Sharpening in the spatial domain

Exercises:

Chapter Filtering in the Fourier domain

3.1 From the Fourier series to the Fourier transform

3.2 Meaning of the Fourier transform

3.3 The impulse function

3.4 Fourier transform of a train of impulses

3.5 The convolution theorem

3.6 The discrete Fourier transform (DFT)

3.7 Additional properties of the DFT

3.8 Filtering in the Fourier domain

3.9 Low-pass filters

3.10 Other low-pass filters

3.11 High-pass filters

3.12 The FFT

3.13 Comparison of the FFT with convolution

Chapter Image compression

4.1 Basics of image compression

4.2 Basics of coding theory

4.3 Uniquely decodable codes (UDCs), instantaneously decodable codes (IDCs), and all that

4.4 Kraft's inequality

4.5 Efficiency of instantaneous codes

4.6 Information theory

4.7 Huffman coding: algorithm

4.8 Huffman coding: implementation

4.9 Nearly optimal codes

4.9.1 B code

4.9.2 Shift codes

4.9.3 Shannon-Elias-Fano coding

4.10 Reducing interpixel redundancy: run-length coding.

4.10.1 Other methods for reducing interpixel redundancy

4.11 LZW coding

4.12 Arithmetic coding

4.13 Transform coding

Chapter Image analysis and object recognition

5.1 Image analysis

5.2 Detection of points and lines

5.3 The Hough transform

5.4 Segmentation: edge detection

5.4.1 The Marr-Hildreth edge detection algorithm

5.4.2 The Canny edge detector

5.5 Thresholding

5.6 A global view of image analysis and pattern recognition

5.7 Representation of objects

5.7.1 Chain codes

5.7.2 Signatures

5.7.3 Statistical moments

5.7.4 Regional descriptors

5.8 Texture

5.9 Skeletonization or medial axis transformation (MAT)

5.10 Principal component analysis (PCA)

5.10.1 PCA for color images

5.10.2 Image reconstruction from principal components

5.10.3 Application of PCA for optical character recognition (OCR)

5.11 Pattern recognition

Chapter Image restoration

6.1 Analyzing motion blur

6.2 Inverse filtering

6.3 Noise

6.4 Removal of noise by morphological operations

6.4.1 Erosion

6.4.2 Dilation

6.4.3 Opening and closing

6.5 Alternative method for extracting and labeling connected components

6.6 Image reconstruction from projections

6.6.1 CT scan

6.6.2 The Radon transform

6.6.3 The Fourier slice theorem

Chapter Wavelets

7.1 Wavelets versus the Fourier transform

7.2 The Haar wavelet transform

7.3 An alternative view of wavelets

Chapter Color image processing

8.1 The RGB color model

8.2 The CMY and CMYK color models

8.3 The hue, saturation, and intensity (HSI) color model

Chapter Introduction to MATLAB®

9.1 Introduction

9.2 Help with MATLAB®

9.3 Variables

9.4 Mathematical operations

9.5 Loops and control statements

9.6 Built-in MATLAB® functions.

9.7 Some more useful MATLAB® commands and programming practices

9.8 Functions

Chapter The image processing toolbox

10.1 Introduction

10.2 Reading from an image file and writing into an image file

10.3 Fourier domain processing

10.4 Calculation of entropy

10.5 Huffman code

10.6 Arithmetic code

10.7 Segmentation

10.8 Hough transform

10.9 Some common error messages in MATLAB

10.10 Using the MATLAB AI chat playground to generate MATLAB code

10.11 Exercises

Chapter Video processing

11.1 Introduction

11.2 Extracting frames from a video

11.3 Video compression

11.4 Detection and analysis of motion: optical flows

11.5 Exercises:

Chapter Applications of machine learning

12.1 Linear discriminant analysis

12.2 Clustering using the k-means algorithm

12.3 K nearest neighbours

12.4 Artificial neural networks

12.5 Multilayer feedforward neural networks

12.6 Matrix formulation of ANNs

12.7 Back propagation for training neural networks

Chapter Error correcting codes and cryptography

13.1 Mutual information and channel capacity

13.1.1 Hamming distance

13.2 Linear codes

13.3 Decoding linear codes

13.4 Cyclic codes

13.5 Galois fields

13.6 Cryptography

13.7 Encryption techniques

13.8 Public key cryptography

Chapter Solutions to selected exercises

14.1 Solutions to Chapter 1 exercises

14.2 Solutions to Chapter 2 exercises

14.3 Solutions to Chapter 3 exercises

14.4 Solutions to Chapter 4 exercises

14.5 Solutions to Chapter 5 exercises

14.6 Solutions to Chapter 6 exercises

14.7 Solutions to Chapter 7 exercises

14.8 Solutions to chapter 12 exercises

14.9 Solutions to chapter 13 exercises.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Other Format:

Print version: Thiruvikraman, P. K. A Course on Digital Image Processing with MATLAB® (Second Edition)

ISBN:

9780750359696

OCLC:

1543212746