Image Processing: Principles and Applications

Image processing—from basics to advanced applications
Learn how to master image processing and compression with this outstanding state–of–the–art reference. From fundamentals to sophisticated applications, Image Processing: Principles and Applications covers multiple topics and provides a fresh perspective on future directions and innovations in the field, including:Image transformation techniques, including wavelet transformation and developmentsImage enhancement and restoration, including noise modeling and filteringSegmentation schemes, and classification and recognition of objectsTexture and shape analysis techniquesFuzzy set theoretical approaches in image processing, neural networks, etc.Content–based image retrieval and image miningBiomedical image analysis and interpretation, including biometric algorithms such as face recognition and signature verificationRemotely sensed images and their applicationsPrinciples and applications of dynamic scene analysis and moving object detection and trackingFundamentals of image compression, including the JPEG standard and the new JPEG2000 standard
Additional features include problems and solutions with each chapter to help you apply the theory and techniques, as well as bibliographies for researching specialized topics. With its extensive use of examples and illustrative figures, this is a superior title for students and practitioners in computer science, wireless and multimedia communications, and engineering.

Spis treści:
Preface.
1. Introduction.
1.1 Fundamentals of Image Processing.
1.2 Applications of Image Processing.
1.2.1 Automatic visual inspection system.
1.2.2 Remotely sensed scene interpretation.
1.2.3 Biomedical Imaging Techniques.
1.2.4 Defence surveillance.
1.2.5 Moving Object tracking.
1.3 Human Visual Perception.
1 .3.1 Eyes detect motion.
1.3.2 Structure of Eyes.
1.3.3 Nervous Aspects of the Visual Sense.
1.3.4 Intuitionistic Philosophy.
1.3.5 Gray and Color Perception.
1.4 Components of an Image Processing System.
1.4.1 Digital Camera.
1.4.2 Capturing Colors.
1.5 Organization of this book.
1.6 How is this book different ? 16.
1.7 Summary 17.
References 17.
2. Image Formation and Representation.
2.1 Introduction.
2.2 Image formation.
2.2.1 Illumination.
2.2.2 Reflectance Models.
2.3 Sampling and Quantization.
2.3.1 Image Quantization.
2.4 Binary Image.
2.4.1 Geometric properties.
2.5 Connected component labeling.
2.5.1 Three Dimensional imaging.
2.5.2 Stereo images.
2.5.3 Point Spread Function.
2.6 Image fled formats.
2.7 Some Important Notes.
2.8 Types of Image Processing Operations.
2.9 Summary.
References.
3. Color and Color Imagery.
3.1 Introduction.
3.2 Perception of Colors and Spectral sensitivity of human eyes.
3.3 Color Space Quantization and the Just Noticeable Difference.
(JND).
3.3.1 Need for color spaces.
3.4 Color Space and Transformation.
3.4.1 CMYK space.
3.4.2 NTSC or YIQ color space.
3.4.3 Y CbCr color space.
3.4.4 Perceptually uniform color space.
3.4.5 Need for perceptually uniform color space.
3.4.6 CIELAB color Space.
3.5 Color Interpolation or Demosaicing.
3.5.1 Non–adaptive color interpolation algorithms.
3.5.2 Adaptive algorithms.
3.5.3 A Fuzzy Assignment Based Adaptive Algorithm.
3.5.4 Experimental Results.
3.6 Summary.
References.
4. Image Transformation.
4.1 Introduction.
4.2 Fourier Transforms.
4.2.1 One–Dimensional Fourier Transform.
4.2.2 Two–Dimensional Fourier Transform.
4.2.3 Discrete Fourier Transforms (DFT).
4.2.4 Transformation Kernels.
4.2.5 Matrix Form Representation.
4.2.6 Properties.
4.2. 7 Fast Fourier Transforms.
4.3 Discrete Cosine Transform.
4.4 Walsh Hadamard Transform (WHT).
4.5 Karhaunen–Loeve Transform or Principal Component Analysis.
4.5.1 Covariance Matrix.
4.5.2 Eigen vector and Eigen values.
4.5.3 Principal Component Analysis.
4.5.4 Singular Value Decomposition.
4.6 Summary.
References.
5. Discrete Wavelet Transform.
5.1 Introduction.
5.2 Wavelet Transforms.
5.2.1 Discrete Wavelet Transforms.
5.2.2 Concept of Multiresolution Analysis.
5.2.3 Implementation by Filters and the Pyramid Algorithm.
5.3 Extension to Two–Dimensional Signals.
5.4 Lifting Implementation of the DWT.
5.4.1 Finite Impulse Response Filter and Z–transform.
5.4.2 Euclidean Algorithm for Laurent Polynomials.
5.4.3 Perfect Reconstruction and Polyphase Representation of Filters.
5.4.4 Lifting.
5.4.5 Data Dependency Diagram for Lifting Computation.
5.5 Why Do We Care About Lifting?
5.6 Applications Areas in Image Processing.
5.7 Summary.
References.
6. Image Enhancement and Restoration.
6.1 Introduction.
6.2 Distinction between image enhancement and restoration.
6.3 Spatial Image Enhancement Techniques.
6.3.1 Unsharp Masking and Crisping.
6.3.2 Spatial Low Pass and High Pass Filtering.
6.3.3 Image Contrast Enhancement.
6.3.4 Local Area Histogram Equalization.
6.3.5 Histogram Hyperbolization.
6.3.6 Arithmatic/Logic operation for Enhancement.
6.4 Noise Filtering.
6.5 Image Enhancement – Frequency Domain approach.
6.5.1 Averaging and Spatial Low Pass Filtering.
6.5.2 Directional Smoothing.
6.5.3 Median Filtering.
6.5.4 Homomorphic Filter.
6.6 Noise Modeling.
6.6.1 Types of Noise in an Image and Their Characteristics.
6.7 Image Restoration.
6.7.1 Image Restoration of impulse noise embedded images.
6.7.2 Restoration of blurred image.
6.7.3 Inverse Filtering.
6.7.4 Wiener Filter.
6.7.5 Singular Value Decomposition.
6.8 Summary.
References.
7. Image Segmentation.
7.1 Preliminaries.
7.2 Edge, Line, and Point Detection.
7.3 Edge Detector.
7.3.1 Robert Operator Based Edge Detector.
7.3.2 Sobel Operator Based Edge Detector.
7.3.3 Prewitt Operator Based Edge Detector.
7.3.4 Kirsch operator.
7.3.5 Canny′s Edge Detector.
7.3.6 Operators Based on Second Derivative.
7.4 Image Thresholding Techniques.
7.4.1 Problems encountered and possible solutions.
7.4.2 Entropy Based Thresholding.
7.4.3 Region Growing.
7.4.4 Clustering of Multiband images.
7.5 Color Image Segmentation.
7.6 Waterfall algorithm for segmentation.
7.7 Document Image segmentation.
7.7.1 Match–based segmentation.
7.8 Summary.
References.
8. Recognition of Image Patterns.
8.1 Introduction.
8.1.1 Decision Theoretic Pattern Classification.
8.2 Bayesian Decision Theory.
8.2.1 Parameter estimation.
8.2.2 Minimum Distance Classification.
8.3 Non–parametric Classification.
8.3.1 K–Nearest–Neighbor Classification.
8.4 Unsupervised Classification Strategies – clustering.
8.4.1 Single Linkage Clustering.
8.4.2 Complete Linkage clustering.
8.4.3 Average Linkage Clustering.
8.5 K–means Clustering Algorithm.
8.5.1 Syntactic Pattern Classification.
8.6 Primitive selection Strategies.
8.7 High Dimensional Pattern Grammars.
8.8 Formal Linguistic model.
8.9 Automata Theory.
8.9.1 Grammatical Inference.
8.10 Structural recognition of imprecise Patterns.
8.11 Symbolic Projection Method.
8.12 Classification using Neural Networks.
8.12.1 Error Backpropagation.
8.13 Crisp Neural Networks For Scene Classification.
8.14 Architecture of Back propagation network.
8.14.1 Kohonen′s Self–Organizing Feature Map.
8.14.2 Counter propagation Neural Network.
8.15 Research Direction.
8.16 Summary.
References.