book

Image Analysis

Name: Image Analysis
ISBN: 9783110524284

by Tsinghua University Tsinghua University Press, Yujin Zhang

August 2017

Beginner to intermediate

310 pages

10h 17m

English

De Gruyter

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Cover
Title Page
Copyright
Contents
1 Introduction to Image Analysis
1.1 Image and Image Engineering1.1.1 Image Foundation1.1.2 Image Engineering1.2 The Scope of Image Analysis1.2.1 Definition and Research Content of Image Analysis1.2.2 Image Analysis System1.3 Digitization in Image Analysis1.3.1 Discrete Distance1.3.2 Connected Component1.3.3 Digitizing Model1.3.4 Digital Arcs and Chords1.4 Distance Transforms1.4.1 Definition and Property1.4.2 Computation of Local Distances1.4.3 Implementation of Discrete Distance Transformation1.5 Overview of the Book1.6 Problems and Questions1.7 Further Reading
2 Image Segmentation
2.1 Definition and Classification2.1.1 Definition of Segmentation2.1.2 Classification of Algorithms2.2 Basic Technique Groups2.2.1 Boundary-Based Parallel Algorithms2.2.2 Boundary-Based Sequential Algorithms2.2.3 Region-based Parallel Algorithms2.2.4 Region-based Sequential Algorithms2.3 Extension and Generation2.3.1 Extending 2-D Algorithms to 3-D2.3.2 Generalization of Some Techniques
2.4 Segmentation Evaluation
2.4.1 A Survey on Evaluation Methods2.4.2 An Effective Evaluation Method2.4.3 Systematic Comparison2.5 Problems and Questions2.6 Further Reading
3 Object Representation and Description
3.1 Classification of Representation and Description3.2 Boundary-Based Representation3.2.1 Taxonomy of Boundary-Based Representation3.2.2 Chain Codes3.2.3 Boundary Segments3.2.4 Polygonal Approximation3.2.5 Boundary Signatures3.2.6 Landmark Points3.3 Region-Based Representation3.3.1 Taxonomy of Region-Based Representation3.3.2 Bounding Regions3.3.3 Quad-Trees3.3.4 Pyramids3.3.5 Skeletons3.4 Transform-Based Representation3.4.1 Technique Classification3.4.2 Fourier Boundary Representation3.5 Descriptors for Boundary3.5.1 Some Straightforward Descriptors
3.5.2 Shape Numbers
3.5.3 Boundary Moments3.6 Descriptors for Regions3.6.1 Some Basic Descriptors3.6.2 Topological Descriptors
3.7 Problems and Questions
3.8 Further Reading

4 Feature Measurement and Error Analysis
4.1 Direct and Indirect Measurements4.1.1 Direct Measurements4.1.2 Derived Measurements4.1.3 Measurement Combinations4.2 Accuracy and Precision4.2.1 Definitions4.2.2 Relationships4.2.3 Statistical Error and Systematic Error4.3 Two Types of Connectivity4.3.1 Boundary Points and Internal Points4.3.2 Object Points and Background Points4.3.3 Separating Connected Components4.3.4 Open Set and Closed Set4.4 Feature Measurement Error4.4.1 Different Factors Influencing Measurement Accuracy4.4.2 Influence of Optical Lens Resolution4.4.3 Influence of Sampling Density4.4.4 Influence of Segmentation4.4.5 Influence of Computation Formulas4.4.6 Combined Influences4.5 Error Analysis4.5.1 Upper and Lower Bounds of an 8-Digital Straight Segment4.5.2 Approximation Errors4.6 Problems and Questions4.7 Further Reading
5 Texture Analysis
5.1 Concepts and Classification5.1.1 Meanings and Scale5.1.2 Research and Application Related to Texture5.1.3 Approaches for Texture Analysis5.2 Statistical Approaches5.2.1 Co-occurrence Matrix5.2.2 Descriptors Based on a Gray-Level Co-occurrence Matrix5.2.3 Law’s Texture Energy Measurements
5.3 Structural Approaches
5.3.1 Two Basic Components5.3.2 Typical Structural Methods5.3.3 Local Binary Mode5.4 Spectral Approaches5.4.1 Fourier Spectrum5.4.2 Bessel-Fourier Spectrum5.4.3 Gabor Spectrum
5.5 Texture Segmentation
5.5.1 Supervised Texture Segmentation5.5.2 Unsupervised Texture Segmentation5.5.3 Texture Classification Based on Wavelet Transformation5.6 Problems and Questions5.7 Further Reading
6 Shape Analysis
6.1 Definitions and Tasks6.1.1 Definition of Shape6.1.2 Shape Analysis Tasks6.2 Different Classes of 2-D Shapes6.2.1 Classification of Shapes6.2.2 Further Discussions6.3 Description of Shape Property6.3.1 Compactness Descriptors6.3.2 Complexity Descriptors6.4 Technique-Based Descriptors6.4.1 Polygonal Approximation-Based Shape Descriptors6.4.2 Curvature-Based Shape Descriptors
6.5 Wavelet Boundary Descriptors
6.5.1 Definition and Normalization6.5.2 Properties of the Wavelet Boundary Descriptor6.5.3 Comparisons with Fourier Descriptors6.6 Fractal Geometry6.6.1 Set and Dimension6.6.2 The Box-Counting Approach6.6.3 Implementing the Box-Counting Method6.6.4 Discussions6.7 Problems and Questions6.8 Further Reading
7 Motion Analysis
7.1 The Purpose and Subject of Motion Analysis7.1.1 Motion Detection7.1.2 Locating and Tracking Moving Objects7.1.3 Moving Object Segmentation and Analysis7.1.4 Three-Dimensional Scene Reconstruction and Motion Understanding7.2 Motion Detection7.2.1 Motion Detection Using Image Differences7.2.2 Model-Based Motion Detection7.3 Moving Object Detection7.3.1 Background Modeling7.3.2 Optical Flow
7.3.3 Detecting Specific Movement Pattern
7.4 Moving Object Segmentation7.4.1 Object Segmentation and Motion Information Extraction7.4.2 Dense Optical Flow Algorithm7.4.3 Parameter and Model-Based Segmentation7.5 Moving Object Tracking7.5.1 Typical Technology
7.5.2 Subsequences Decision Strategy
7.6 Problems and Questions7.7 Further Reading
8 Mathematical Morphology
8.1 Basic Operations of Binary Morphology8.1.1 Binary Dilation and Erosion8.1.2 Binary Opening and Closing8.2 Combined Operations of Binary Morphology8.2.1 Hit-or-Miss Transform8.2.2 Binary Composition Operations
8.3 Practical Algorithms of Binary Morphology
8.3.1 Noise Removal8.3.2 Corner Detection8.3.3 Boundary Extraction8.3.4 Object Detection and Localization8.3.5 Region Filling8.3.6 Extraction of Connected Components8.3.7 Calculation of Region Skeleton8.4 Basic Operations of Grayscale Morphology8.4.1 Grayscale Dilation and Erosion8.4.2 Grayscale Opening and Closing8.5 Combined Operations of Grayscale Morphology8.5.1 Morphological Gradient8.5.2 Morphological Smoothing8.5.3 Top Hat Transform and Bottom Hat Transform8.5.4 Morphological Filters8.5.5 Soft Morphological Filters
8.6 Practical Algorithms of Grayscale Morphology
8.6.1 Background Estimation and Elimination8.6.2 Morphological Edge Detection8.6.3 Cluster Fast Segmentation8.7 Problems and Questions8.8 Further Reading
Answers to Selected Problems and Questions
References
Index

Content preview from Image Analysis

$\begin{matrix} \begin{array}{l} L_{7} = [1 6 15 20 15 6 1] \\ E_{7} = [- 1 - 4 - 5 0 5 4 1] \\ S_{7} = [- 1 - 2 1 4 1 - 2 - 1] \\ W_{7} = [- 1 0 3 0 - 3 0 1] \\ R_{7} = [1 - 2 - 1 4 - 1 - 2 1] \\ O_{7} = [- 1 6 - 15 20 - 15 6 - 1] \end{array} & (5.32) \end{matrix}$

In many practical cases, a set of nine 5 × 5 convolution masks are used to compute the texture energy. To obtain these nine masks, the vectors L5, E5, S5, and R5 are used. L5 gives a center-weighted local average, E5 detects edges, S5 detects spots, and R5 detects ripples. The 2-D convolution masks are obtained by computing the outer product of each pair of vectors. For example, the mask $E_{5}^{T} L_{5}$ E5T and L5 as follows:

$[\begin{matrix} \end{matrix}$

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ISBN: 9783110524284

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Image Analysis

by Tsinghua University Tsinghua University Press, Yujin Zhang

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.