Image Processing and Machine Learning, Volume 2

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface Volume II
1 Morphological Operations
	1.1 Shrinkage and Growth of Structures
		1.1.1 Neighborhood Types Between Pixels
	1.2 Fundamental Morphological Operations
		1.2.1 The Structure of Reference
		1.2.2 Point Set
		1.2.3 Dilation
		1.2.4 Erosion
		1.2.5 Properties of Dilatation and Erosion
		1.2.6 Design of Morphological Filters
	1.3 Edge Detection in Binary Images
	1.4 Combination of Morphological Operations
		1.4.1 Opening
		1.4.2 Closing
		1.4.3 Properties of the Open and Close Operations
		1.4.4 The Hit-or-Miss Transformation
	1.5 Morphological Filters for Grayscale Images
		1.5.1 Reference Structure
		1.5.2 Dilation and Erosion for Intensity Images
		1.5.3 Open and Close Operations with Grayscale Images
		1.5.4 Top-Hat and Bottom-Hat Transformation
	1.6 MATLAB Functions for Morphological Operations
		1.6.1 Strel Function
		1.6.2 MATLAB Functions for Dilation and Erosion
		1.6.3 MATLAB Functions Involving the Open and Close Operations
		1.6.4 The Transformation of Success or Failure ('Hit-or-Miss')
		1.6.5 The bwmorph Function
		1.6.6 Labeling of Convex Components
	Notes
	References
2 Color Images
	2.1 RGB Images
		2.1.1 Composition of Color Images
		2.1.2 Full-Color Images
		2.1.3 Indexed Images
	2.2 Histogram of an RGB Image
		2.2.1 Histogram of RGB Images in MATLAB
	2.3 Color Models and Color Space Conversions
		2.3.1 Converting an RGB Image to Grayscale
		2.3.2 RGB Images without Color
		2.3.3 Reducing Saturation of a Color Image
		2.3.4 HSV and HSL Color Model
		2.3.5 Conversion From RGB to HSV
		2.3.6 Conversion From HSV to RGB
		2.3.7 Conversion From RGB to HLS
		2.3.8 Conversion From HLS to RGB
		2.3.9 Comparison of HSV and HSL Models
	2.4 The YUV, YIQ, and YCbCr Color Models
		2.4.1 The YUV Model
		2.4.2 The YIQ Model
		2.4.3 The YC[sub(b)]C[sub(r)] Model
	2.5 Useful Color Models for Printing Images
		2.5.1 Transformation From CMY to CMYK (Version 1)
		2.5.2 Transformation From CMY to CMYK (Version 2)
		2.5.3 Transformation From CMY to CMYK (Version 3)
	2.6 Colorimetric Models
		2.6.1 The CIEXYZ Color Space
		2.6.2 The CIE Color Diagram
		2.6.3 Lighting Standards
		2.6.4 Chromatic Adaptation
		2.6.5 The Gamut
	2.7 Variants of the CIE Color Space
	2.8 The CIE L*a*b* Model
		2.8.1 Transformation CIEXYZ → L*a*b*
		2.8.2 Transformation L*a*b* → CIEXYZ
		2.8.3 Determination of Color Difference
	2.9 The sRGB Model
	2.10 MATLAB Functions for Color Image Processing
		2.10.1 Functions for Handling RGB and Indexed Images
		2.10.2 Functions for Color Space Conversion
	2.11 Color Image Processing
	2.12 Linear Color Transformations
		2.12.1 Linear Color Transformation Using MATLAB
	2.13 Spatial Processing in Color Images
		2.13.1 Color Image Smoothing
		2.13.2 Smoothing Color Images with MATLAB
		2.13.3 Sharpness Enhancement in Color Images
		2.13.4 Sharpening Color Images with MATLAB
	2.14 Vector Processing of Color Images
		2.14.1 Edge Detection in Color Images
		2.14.2 Edge Detection in Color Images Using MATLAB
	Note
	References
3 Geometric Operations in Images
	3.1 Coordinate Transformation
		3.1.1 Simple Transformations
		3.1.2 Homogeneous Coordinates
		3.1.3 Affine Transformation (Triangle Transformation)
		3.1.4 Projective Transformation
		3.1.5 Bilinear Transformation
		3.1.6 Other Nonlinear Geometric Transformations
	3.2 Reassignment of Coordinates
		3.2.1 Source-Destination Mapping
		3.2.2 Destination-Source Mapping
	3.3 Interpolation
		3.3.1 Simple Interpolation Methods
		3.3.2 Ideal Interpolation
		3.3.3 Cubic Interpolation
	3.4 Aliases
	3.5 Functions for Geometric Transformation in MATLAB
		3.5.1 Application Example
	References
4 Comparison and Recognition of Images
	4.1 Comparison in Grayscale Images
		4.1.1 Distance between Patterns
		4.1.2 Distance and Correlation
		4.1.3 The Normalized Cross-Correlation
		4.1.4 Correlation Coefficient
	4.2 Pattern Recognition Using the Correlation Coefficient
		4.2.1 Implementation of the Pattern Recognition System by the Correlation Coefficient
	4.3 Comparison of Binary Images
		4.3.1 The Transformation of Distance
		4.3.2 Chamfer Algorithm
	4.4 Chamfer Index Relationship
		4.4.1 Implementation of the Chamfer Relation Index
	References
5 Mean-Shift Algorithm for Segmentation
	5.1 Introduction
	5.2 Kernel Density Estimation (KDE) and the Mean-Shift Method
		5.2.1 Concentration Map Generation
	5.3 Density Attractors Points
	5.4 Segmentation with Camshift
		5.4.1 Feature Definition
		5.4.2 Operative Data Set
		5.4.3 Operation of the MS Algorithm
		5.4.4 Inclusion of the Inactive Elements
		5.4.5 Merging of Not Representative Groups
		5.4.6 Computational Process
	5.5 Results of the Segmentation Process
		5.5.1 Experimental Setup
		5.5.2 Performance Criterion
		5.5.3 Comparison Results
	References
6 Singular Value Decomposition in Image Processing
	6.1 Introduction
	6.2 Computing the SVD Elements
	6.3 Approximation of the Data Set
	6.4 SVD for Image Compression
	6.5 Principal Component Analysis
	6.6 Principal Components through Covariance
	6.7 Principal Components through Correlation
	References
Index