دانلود کتاب Data Science for Nano Image Analysis (International Series in Operations Research & Management Science, 308)

فهرست مطالب :

Foreword
Preface
Acknowledgments
Contents
Acronyms
1 Introduction
1.1 Examples of Nano Image Analysis
1.1.1 Example 1: Morphology
1.1.2 Example 2: Spacing
1.1.3 Example 3: Temporal Evolution
1.1.4 Example 4: Motions and Interactions
1.2 How This Book Is Organized
1.3 Who Should Read This Book
1.4 Online Book Materials
References
2 Image Representation
2.1 Types of Material Images
2.2 Functional Representation
2.3 Matrix Representation
2.4 Graph Representation
2.5 Set Representation
2.6 Example: Watershed Segmentation
References
3 Segmentation
3.1 Challenges of Segmenting Material Images
3.2 Steps for Material Image Segmentation
3.3 Image Binarization
3.3.1 Global Image Thresholding
3.3.2 Local Image Thresholding
3.3.3 Active Contour
3.3.4 Graph Cut
3.3.5 Background Subtraction
3.3.6 Numerical Comparison of Image Binarization Approaches for Material Images
3.4 Foreground Segmentation
3.4.1 Marker Generation
3.4.2 Initial Foreground Segmentation
3.4.3 Refine Foreground Segmentation with Shape Priors
3.5 Ensemble Method for Segmenting Low Contrast Images
3.5.1 Consensus and Conflicting Detections
3.5.2 Measure of Segmentation Quality
3.5.3 Optimization Algorithm for Resolving Conflicting Segmentations
3.6 Case Study: Ensemble Method for Nanoparticle Detection
3.6.1 Ensemble versus Individual Segmentation
3.6.2 Numerical Performance of the Ensemble Segmentation
References
4 Morphology Analysis
4.1 Basics of Shape Analysis
4.1.1 Landmark Representation
4.1.1.1 Kendall\'s Shape Representation
4.1.1.2 Procrustes Tangent Coordinates
4.1.1.3 Bookstein\'s Shape Coordinates
4.1.1.4 Related Issues
4.1.2 Parametric Curve Representation
4.1.2.1 Fourier Shape Descriptor
4.1.2.2 Square-Root Velocity Function (SRVF) Representation
4.2 Shape Analysis of Nanoparticles
4.2.1 Shape Analysis for Star-Shaped Nanoparticles
4.2.1.1 Embedding of the Shape Manifold to Euclidean Space
4.2.1.2 Semi-Supervised Clustering of Shapes
4.2.2 Shape Analysis for a Broader Class of Nanoparticles
4.2.2.1 Shape Representation
4.2.2.2 Parameter Estimation
4.2.2.3 Shape Classification
4.2.2.4 Shape Inference
4.2.3 Numerical Examples: Image Segmentation to Nanoparticle Shape Inference
4.3 Beyond Shape Analysis: Topological Data Analysis
References
5 Location and Dispersion Analysis
5.1 Basics of Mixing State Analysis
5.2 Quadrat Method
5.3 Distance Methods
5.3.1 The K Function and L Function
5.3.2 The Kmm Function
5.3.3 The F Function and G Function
5.3.4 Additional Notes
5.4 A Revised K Function
5.4.1 Discretiztaion
5.4.2 Adjustment of the Normalizing Parameter
5.4.3 Relation Between Discretized K and K\"0365K
5.4.4 Nonparametric Test Procedure
5.5 Case Study
5.5.1 A Single Image Taken at a Given Time Point
5.5.2 Multiple Images Taken at a Given Time Point
5.6 Dispersion Analysis of 3D Materials
References
6 Lattice Pattern Analysis
6.1 Basics of Lattice Pattern Analysis
6.2 Simple Spot Detection
6.3 Integrated Lattice Analysis
6.4 Solution Approach for the Integrated Lattice Analysis
6.4.1 Listing Lg\'s and Estimating τ
6.4.2 Choice of Stopping Condition Constant c and Related Error Bounds
6.4.3 Choice of Threshold ρ
6.4.4 Comparison to the Sparse Group Lasso
6.5 Numerical Examples with Synthetic Datasets
6.6 Lattice Analysis for Catalysts
6.7 Closing Remark
References
7 State Space Modeling for Size Changes
7.1 Motivating Background
7.1.1 The Problem of Distribution Tracking
7.1.2 Nanocrystal Growth Video Data
7.2 Single Frame Methods
7.2.1 Smoothed Histograms
7.2.2 Kernel Density Estimation
7.2.3 Penalized B-Splines
7.3 Multiple Frames Methods
7.3.1 Retrospective Analysis
7.3.2 Optimization for Density Estimation
7.4 State Space Modeling for Online Analysis
7.4.1 State Space Model for NPSD
7.4.2 Online Updating of State αt
7.4.3 Technical Details of the Gaussian Approximation
7.4.4 Curve Smoothness for Distribution Estimation
7.5 Parameter Estimation
7.5.1 Bayesian Modeling
7.5.2 MCMC Sampling
7.5.3 Select the Hyper-Parameters
7.6 Case Study
7.6.1 Analysis of the Three Videos
7.6.2 Comparison with Alternative Methods
7.7 Future Research Need: Learning-on-the-Fly
References
8 Dynamic Shape Modeling for Shape Changes
8.1 Problem of Shape Distribution Tracking
8.2 Dynamic Shape Distribution with Bookstein Shape Coordinates
8.2.1 Joint Estimation of Dynamic Shape Distribution
8.2.2 Autoregressive Model
8.3 Dynamic Shape Distribution with Procrustes Tangent Coordinates
8.4 Bayesian Linear Regression Model for Size and Shape
8.5 Dynamic Shape Distribution with Parametric Curves
8.5.1 Bayesian Regression Modeling for Dynamic Shape Distribution
8.5.2 Mixture of Regression Models for Nonparametric Dynamic Shape Distribution
8.6 Case Study: Dynamic Shape Distribution Tracking with Ex Situ Measurements
8.7 Case Study: Dynamic Shape Distribution Tracking with In Situ Measurements
References
9 Change Point Detection
9.1 Basics of Change Point Detection
9.1.1 Performance Metrics
9.1.2 Phase I Analysis Versus Phase II Analysis
9.1.3 Univariate Versus Multivariate Detection
9.2 Detection of Size Changes
9.2.1 Size Detection Approach
9.2.2 Sensitivity of Control Limit κ
9.2.3 Hybrid Modeling
9.3 Phase I Analysis of Shape Changes
9.3.1 Recap of the Shape Model and Notations
9.3.2 Mixture Priors for Multimode Process Characterization
9.3.3 Block Gibbs Sampler
9.4 Phase II Analysis of Shape Changes
9.5 Case Study
9.5.1 Phase I Result
9.5.2 Case I: αs Changed
9.5.3 Case II: Only Part of αs Changed
9.5.4 Case III: σ2 Changed
9.5.5 Case IV: ωs Changed
9.5.6 Application to Nanoparticle Self-Assembly Processes
References
10 Multi-Object Tracking Analysis
10.1 Basics of Multi-Object Tracking Analysis
10.2 Linear Assignment Problem for Data Association
10.3 Linear Assignment Approach for Tracking Objects with Degree-Two Interactions
10.4 Two-Stage Assignment Approach for Tracking Objects with Degree-Two Interactions
10.5 Multi-Way Minimum Cost Data Association
10.5.1 Special Properties of the Constraint Coefficient Matrix
10.5.2 Lagrange Dual Solution
10.6 Case Study: Data Association for Tracking Particle Interactions
10.6.1 Simulation Study
10.6.2 Tracking Nanoparticles in In Situ Microscope Images
10.7 Case Study: Pattern Analysis of Nanoparticle Oriented Attachments
10.7.1 Modeling Nanoparticle Oriented Attachments
10.7.2 Statistical Analysis of Nanoparticle Orientations
10.7.2.1 Maximum Likelihood Estimation
10.7.2.2 Goodness-of-Fit Test
10.7.2.3 Testing the Uniformity of Distribution
10.7.2.4 Testing the Mean Orientation
10.7.3 Results
References
11 Super Resolution
11.1 Multi-Frame Super Resolution
11.1.1 The Observation Model
11.1.2 Super-Resolution in the Frequency Domain
11.1.3 Interpolation-Based Super Resolution
11.1.4 Regularization-Based Super Resolution
11.2 Single-Image Super Resolution
11.2.1 Example-Based Approach
11.2.2 Locally Linear Embedding Method
11.2.3 Sparse Coding Approach
11.2.4 Library-Based Non-local Mean Method
11.2.5 Deep Learning
11.3 Paired Images Super Resolution
11.3.1 Global and Local Registration
11.3.2 Existing Super-Resolution Methods Applied to Paired Images
11.3.3 Paired LB-NLM Method for Paired Image Super-Resolution
11.4 Performance Criteria
11.5 Case Study
11.5.1 VSDR Trained on Downsampled Low-Resolution Images
11.5.2 Performance Comparison
11.5.3 Computation Time
11.5.4 Further Analysis
References
Index