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E-grāmata: Guide to Signals and Patterns in Image Processing: Foundations, Methods and Applications

Apurba Das

Formāts: PDF+DRM
Izdošanas datums: 22-Apr-2015
Izdevniecība: Springer International Publishing AG
Valoda: eng
ISBN-13: 9783319141725

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Formāts: PDF+DRM
Izdošanas datums: 22-Apr-2015
Izdevniecība: Springer International Publishing AG
Valoda: eng
ISBN-13: 9783319141725

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This text reviews the field of digital image processing from the different perspectives offered by the separate domains of signal processing and pattern recognition. The book describes a rich array of applications, representing the latest trends in industry and academic research. To inspire further interest in the field, a selection of worked-out numerical problems is also included in the text. The content is presented in an accessible manner, examining each topic in depth without assuming any prior knowledge from the reader, and providing additional background material in the appendices. Features: covers image enhancement techniques in the spatial domain, the frequency domain, and the wavelet domain; reviews compression methods and formats for encoding images; discusses morphology-based image processing; investigates the modeling of object recognition in the human visual system; provides supplementary material, including MATLAB and C++ code, and interactive GUI-based modules, at an associated website.

1 Introduction to Digital Image

(42)

1.1 Formation of an Image

(2)

1.2 Definition of Signal

(3)

1.3 Analog and Digital Image as 2D Signal

(8)

1.3.1 Continuous Time Continuous Valued Electrical Signal

(1)

1.3.2 Continuous Space Continuous Intensity (CSCI) Image

(1)

1.3.3 Sampling: Discrete Time Continuous Valued (DTCV) Electrical Signal

(1)

1.3.4 Concept of Sampling in Images (2D Signal)

(1)

1.3.5 Quantization: Discrete Time Discrete Valued (DTDV) Electrical Signal

(2)

1.3.6 Quantization in Images (2D Signal)

(2)

1.3.7 Encoding in Images (2D Signal)

(1)

1.4 Relationships Between Pixels

(4)

1.4.1 Neighborhood

(2)

1.4.2 Adjacency

(1)

1.4.3 Distance Measures

(2)

1.5 Geometric Transformations

(13)

1.5.1 Translation

(3)

1.5.2 Scaling

(1)

1.5.3 Rotation

(3)

1.5.4 Homogeneous Transformation

(2)

1.5.5 Concatenation of Transformation

(3)

1.5.6 Affine Transformation

(1)

1.6 Convolution

(5)

1.6.1 Transformed Domain Simplicity

(2)

1.6.2 2D Convolution: Convolution in Image Processing

(1)

1.7 Correlation

(4)

1.7.1 Case Study: Pattern (Shape Feature) Matching Between Two Objects Using Cross-Correlation

(3)

1.8 MATLAB Codes

(3)

1.8.1 Sampling of a Sweep Image

(1)

1.8.2 Resolution of Image

(1)

1.8.3 Quantization of Image

(1)

1.8.4 Correlation Subroutine

(1)

Reference

(1)

2 Image Enhancement in Spatial Domain

(50)

2.1 Intensity Transformations

(5)

2.1.1 Linear Transformation

(2)

2.1.2 Contrast Stretching and Thresholding

(1)

2.1.3 Negative Intensity Transform

(1)

2.1.4 Logarithmic Intensity Transformation

(1)

2.1.5 Power-Law Intensity Transform and Gamma Correction

(2)

2.2 Histogram of an Image

(3)

2.2.1 Skewness

(1)

2.2.2 Kurtosis

(1)

2.3 Histogram Equalization and Histogram Specification

(6)

2.4 Image Smoothing

(5)

2.4.1 Mean Filter

(3)

2.4.2 Ordered Statistics Filter

(2)

2.5 Image Sharpening

(5)

2.5.1 Image Sharpening by Gradient Mask: First-Order Derivative

(2)

2.5.2 Image Sharpening by Laplacian mask: Second-Order Derivative

(1)

2.6 Image Interpolation and Resampling

(20)

2.6.1 B-Spline Function

(2)

2.6.2 Interpolation of 1D Signal by B-Spline

(6)

2.6.3 Interpolation of 2D Image

(8)

2.7 MATLAB codes

(5)

2.7.1 Image Transformation Without Interpolation

(1)

2.7.2 Image Rotation with Different Interpolation Techniques

(1)

2.7.3 Mean and Median Filter Response on Noisy Image

(1)

2.7.4 Image Sharpening by Laplacian Mask

(1)

Reference

(2)

3 Interpretation and Processing of Image in Frequency Domain

(56)

3.1 Concept of Frequency in Image

(6)

3.1.1 Fourier Series

(5)

3.1.2 Interpretation and Direction of Frequency in Image

(1)

3.2 Phase Congruency and Edge Detection in an Image

100

(6)

3.3 Fourier Transform for Continuous and Discrete Time Signals

106

(4)

3.3.1 Discrete Time Fourier Transform

107

(2)

3.3.2 DFT and FFT

109

(1)

3.4 DFT of Digital Image

110

(2)

3.5 Translation and Scaling Properties of 2D Fourier Transform

112

(8)

3.5.1 Translation: Dragging the LF (DC) Component at the Center of the 2D Spectra

112

(2)

3.5.2 Scaling: Space-Frequency Relationship in Image

114

(6)

3.6 Concept of Image Filtering in Frequency Domain

120

(4)

3.7 Smoothing Filter

124

(6)

3.7.1 Ideal LPF

125

(1)

3.7.2 Butterworth LPF

126

(3)

3.7.3 Gaussian LPF

129

(1)

3.8 Sharpening Filter

130

(6)

3.8.1 Ideal HPF

132

(1)

3.8.2 Butterworth HPF

133

(2)

3.8.3 Gaussian HPF

135

(1)

3.9 Case Studies

136

(7)

3.9.1 Importance of Phase over Amplitude in DFT Spectrum

136

(2)

3.9.2 DFT over DFT

138

(5)

3.10 Matlab Codes

143

(6)

3.10.1 Ideal 2D Filters in Frequency Domain

143

(2)

3.10.2 Subroutine of 2D Butterworth Filter

145

(1)

3.10.3 Subroutine of 2D Gaussian Filter

145

(1)

3.10.4 Importance of Phase over Amplitude in Image Spectrum

146

(1)

References

147

(2)

4 Color Science and Color Technology

149

(42)

4.1 Light and Primary Colors

149

(9)

4.1.1 Device-Dependent Primary Colors: Additive Color Model

151

(1)

4.1.2 Device-Dependent Primary Colors: Subtractive Color Model

152

(2)

4.1.3 Reflectance and Its Spectra

154

(4)

4.2 Psycho-Visual Color: Human Vision System

158

(3)

4.2.1 Photoreceptors: Rods and Cones

158

(3)

4.3 Color Description Systems

161

(5)

4.3.1 Munsell System

162

(1)

4.3.2 Pantone System

163

(3)

4.4 Colorimetry: CIE Standards

166

(4)

4.4.1 CIE Standard Illuminant

167

(2)

4.4.2 CIE Standard Observer

169

(1)

4.5 CIE Color Spaces

170

(7)

4.5.1 Non-uniform Perceptual Color Spaces

171

(3)

4.5.2 Uniform Perceptual Color Spaces

174

(2)

4.5.3 Xerox/YES Color Space

176

(1)

4.6 Halftone Screening

177

(4)

4.6.1 Moire Pattern and Screen Angle

178

(1)

4.6.2 Growth Sequence of Halftone Dot

179

(2)

4.7 Color Management

181

(8)

4.7.1 Profile Connection Space (PCS)

184

(1)

4.7.2 Gamut Mapping

184

(1)

4.7.3 Rendering Intents

185

(4)

4.8 Matlab Codes

189

(2)

4.8.1 Halftone Screening by Error Diffusion

189

(1)

4.8.2 Error Diffusion Subroutine

190

(1)

Reference

190

(1)

5 Wavelets: Multiresolution Image Processing

191

(32)

5.1 Introduction

191

(1)

5.2 Short-Time Fourier Transform

191

(5)

5.2.1 Continuous-time STFT

194

(1)

5.2.2 Discrete-time STFT

194

(1)

5.2.3 Spectrogram

194

(1)

5.2.4 Limitation

195

(1)

5.3 Wavelet Function and Scaling Function

196

(6)

5.4 Wavelet Series

202

(1)

5.5 Discrete Wavelet Transform and Multiresolution analysis

203

(4)

5.5.1 Analysis Filter Bank

205

(1)

5.5.2 Synthesis Filter Bank

206

(1)

5.6 Image Decomposition Using DWT

207

(3)

5.6.1 Concept of 2D Signal Decomposition Using Analysis Filter

207

(1)

5.6.2 DWT on Images (Fig. 5.16)

208

(2)

5.7 Image Compression Using DWT: EZW Encoding

210

(8)

5.7.1 Relationship Between Decomposed Sub-bands

211

(1)

5.7.2 Successive Approximation Quantization in EZW

212

(1)

5.7.3 EZW Encoding Algorithm

213

(1)

5.7.4 Image Compression using EZW: An Example

214

(2)

5.7.5 Experimental Results of Image Compression Using EZW

216

(2)

5.8 MATLAB Programs

218

(5)

5.8.1 Haar Scaling and Wavelet Function

218

(1)

5.8.2 Wavelet Series Expansion

219

(1)

5.8.3 Wavelet Decomposition of Image (4 level)

220

(1)

5.8.4 Image Compression by EZW Encoding

221

(1)

References

221

(2)

6 Compression and Encoding of Image: Image Formats

223

(46)

6.1 Redundancy: Fundamentals of Compression

224

(2)

6.2 Entropy: The Measure of Information

226

(1)

6.3 Entropy Coding

227

(3)

6.3.1 Shannon--Fano Coding

228

(1)

6.3.2 Huffman Coding

229

(1)

6.4 Lossy Compression

230

(6)

6.4.1 Block Truncation Compression (BTC)

230

(3)

6.4.2 Vector Quantization Compression (VQC)

233

(3)

6.5 Lossless Compression

236

(2)

6.5.1 Run Length Coding (RLC)

236

(1)

6.5.2 Block Coding

237

(1)

6.6 QPAC: Quality Preserving Adaptive Compression

238

(1)

6.7 Some Common Image Formats

239

(19)

6.7.1 C ++ Code for Reading BMP Image

240

(5)

6.7.2 JPEG

245

(9)

6.7.3 GIF

254

(4)

6.8 Matlab Codes and Pseudocodes

258

(11)

6.8.1 Block Truncation Compression (BTC)

258

(3)

6.8.2 JPEG Compression

261

(5)

6.8.3 GIF: LZW Compression

266

(1)

6.8.4 GIF: LZW Decompression

267

(1)

References

267

(2)

7 Morphology-Based Image Processing

269

(30)

7.1 Basics of Set Theory

269

(2)

7.2 Logic Operations on Binary Images

271

(2)

7.3 Dilation and Erosion

273

(6)

7.3.1 Dilation

273

(3)

7.3.2 Erosion

276

(3)

7.4 Opening and Closing

279

(1)

7.5 Hit-Miss Transform

280

(1)

7.6 Morphological Algorithms for Feature Extraction

280

(13)

7.6.1 Boundary Extraction

282

(1)

7.6.2 Region Filling

283

(1)

7.6.3 Pixel Connectivity

284

(1)

7.6.4 Convex Hull

285

(1)

7.6.5 Thinning

286

(2)

7.6.6 Thickening

288

(1)

7.6.7 Object Skeletons

289

(1)

7.6.8 Pruning

290

(3)

7.7 Case Studies

293

(3)

7.7.1 Boundary Detection

293

(2)

7.7.2 Region Filling

295

(1)

7.7.3 Binary Skeleton

295

(1)

7.8 MATLAB Codes

296

(3)

7.8.1 Dilation

296

(1)

7.8.2 Erosion

297

(1)

7.8.3 Boundary Detection

297

(1)

Reference

298

(1)

8 Patterns in Images and Their Applications

299

(42)

8.1 Introduction to Pattern

299

(1)

8.2 Features

300

(2)

8.2.1 Feature Selection and Extraction

301

(1)

8.3 Principal Component Analysis

302

(6)

8.3.1 Algorithm of PC A

303

(2)

8.3.2 Application of PC A in Face Recognition

305

(2)

8.3.3 Limitations of PCA-Based Face Recognition

307

(1)

8.4 Face Detection Based on Haar-Like Features

308

(3)

8.5 Elastic Branch Graph Matching and Face Manifold

311

(3)

8.6 Decision Tree and Feature Hierarchy

314

(5)

8.6.1 Information Gain

314

(1)

8.6.2 Information Gain Ratio

315

(1)

8.6.3 Selection of Optimized Set of Features

316

(1)

8.6.4 Feature Hierarchy for Gabor Features in Face Recognition

317

(2)

8.7 Scale Invariant Feature Transform

319

(13)

8.7.1 Scale-Space Concept: Multiscale Singularity Tree

320

(2)

8.7.2 SIFT: Representation of Image in Scale--Space

322

(2)

8.7.3 SIFT: Detection of Local Scale--Space Extrema

324

(1)

8.7.4 SIFT: Accurate Keypoint Localization

325

(1)

8.7.5 SIFT: Orientation Assignment

326

(1)

8.7.6 SIFT: Keypoint Descriptor

327

(1)

8.7.7 SIFT: Results

328

(4)

8.8 Histogram of Oriented Gradient

332

(4)

8.8.1 HOG: Dividing Image into Blocks

333

(1)

8.8.2 HOG: Quantization of Gradient Histogram

333

(2)

8.8.3 HOG: Feature Vector Synthesis

335

(1)

8.8.4 HOG: Design of Classifier by Training

335

(1)

8.9 Matlab Codes

336

(5)

8.9.1 PCA of a 2D data set

336

(1)

8.9.2 Scale--Space: Multiscale Singularity Tree

337

(1)

Reference

338

(3)

9 Psycho-visual pattern recognition: Computer Vision

341

(24)

9.1 Introduction

341

(1)

9.2 Receptive Field

342

(5)

9.2.1 On-Center Off-Surround

343

(1)

9.2.2 Off-Center On-Surround

344

(1)

9.2.3 Edge Detection in Retinal Receptive Field

345

(2)

9.3 Modeling of Retinal Receptive Field from Optical Illusions

347

(5)

9.3.1 Optical Illusions: A study

347

(1)

9.3.2 Illustration of the Illusions in Terms of DoG Model of Retinal Receptive Field

348

(4)

9.4 Three Levels of Psycho-Visual System for Pattern Recognition

352

(1)

9.5 Neuro-Visually Inspired Figure-Ground Segregation

353

(5)

9.5.1 The Detailed Algorithm for NFGS

355

(3)

9.6 "Where" and "What" Visual Pathways: Modeling in Computer Vision

358

(7)

References

362

(3)

10 Appendix A: Digital Differentiation and Edge Detection

365

(18)

10.1 Edge in an Image

365

(1)

10.2 Digital Differentiation

366

(3)

10.2.1 Digital Differentiation of One-Dimensional (1D) Signal

367

(2)

10.3 Digital Differentiation for Edge Detection

369

(2)

10.4 Convolution and Correlation for Edge Detection

371

(3)

10.5 Prewitt and Sobel Mask for Edge Detection of Digital Image

374

(1)

10.6 Canny Edge Detector

375

(3)

10.6.1 Noise Reduction

375

(1)

10.6.2 Non-Maxima Suppression

376

(1)

10.6.3 Hysteresis Thresholding

376

(2)

10.7 MATLAB Codes

378

(5)

10.7.1 Digital Differentiation of 1D Signal

378

(1)

10.7.2 Detection of Edges in Orthogonal Directions by Convolution Interpretation of Digital Differentiation

379

(2)

References

381

(2)

11 Appendix B: Elementary Probability Theory

383

(16)

11.1 Concept of Probability

385

(1)

11.1.1 Random Experiments and Sample Space

385

(1)

11.1.2 Events

385

(1)

11.1.3 Probability: Understanding Approaches

386

(1)

11.2 Random Variable

386

(1)

11.3 Mean, Variance, Skewness, and Kurtosis

387

(2)

11.4 Cumulative Distribution Function

389

(3)

11.5 Probability Density Function

392

(1)

11.5.1 Uniform PDF

393

(1)

11.6 Frequently Used Probability Distribution

393

(6)

References

397

(2)

12 Appendix C: Frequently Used MATLAB Functions

399

(14)

12.1 plot()

399

(1)

12.1.1 Syntax

399

(1)

12.1.2 Description

399

(1)

12.2 imshow()

400

(1)

12.2.1 Syntax

400

(1)

12.2.2 Description

400

(1)

12.3 drawnow()

401

(1)

12.3.1 Syntax

401

(1)

12.3.2 Description

401

(1)

12.4 stairs()

401

(1)

12.5 int2str()

402

(1)

12.5.1 Syntax

402

(1)

12.5.2 Description

402

(1)

12.6 conv()

402

(1)

12.7 conv2()

403

(1)

12.8 Two-Dimensional Convolution

403

(1)

12.9 ginput()

404

(1)

12.10 bitget()

405

(1)

12.11 bitset()

405

(1)

12.12 dec2bin()

406

(1)

12.12.1 Syntax

406

(1)

12.12.2 Description

406

(1)

12.13 fft2()

407

(1)

12.13.1 Syntax

407

(1)

12.13.2 Description

407

(1)

12.14 fftshift()

407

(2)

12.14.1 Syntax

407

(1)

12.14.2 Description

408

(1)

12.15 wavefun()

409

(3)

12.15.1 Syntax

409

(1)

12.15.2 Description

410

(2)

12.16 Fourier Synthesizer GUI

412

(1)

Reference

412

(1)

Index

413

Apurba Das is a Technical Specialist in the Image Processing Lab at HCL Technologies, Chennai, India. He has previously served as a Scientist in the Advanced Image Processing Lab at the Centre for Development of Advanced Computing, a R&D organization of the Indian Government Ministry of Communications and Information Technology. His other Springer publications include the books Signal Conditioning: An Introduction to Continuous Wave Communication and Signal Processing and Digital Communication: Principles and System Modelling.

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E-grāmata: Guide to Signals and Patterns in Image Processing: Foundations, Methods and Applications

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