Stochastic Geometry for Image Analysis

Table of Contents

Chapter 1. Introduction

Chapter 2. Marked Point Processes for Object Detection

2.1. Principal definitions

2.2. Density of a point process

2.3. Marked point processes

2.4. Point processes and image analysis

Chapter 3. Random Sets for Texture Analysis

3.1. Introduction

3.2. Random sets

3.3. Some geostatistical aspects

3.4. Some morphological aspects

3.5. Appendix: demonstration of Miles’ formulae for the Boolean model

Chapter 4. Simulation and Optimization

4.1. Discrete simulations: Markov chain Monte Carlo algorithms

4.2. Continuous simulations

4.3. Mixed simulations

4.4. Simulated annealing

Chapter 5. Parametric Inference for Marked Point Processes in Image Analysis

5.1. Introduction

5.2. First question: what and where are the objects in the image?

5.3. Second question: what are the parameters of the point process that models the objects observed in the image?

5.4. Conclusion and perspectives

5.5. Acknowledgments

Chapter 6. How to Set Up a Point Process?

6.1. From disks to polygons, via a discussion of segments

6.2. From no overlap to alignment

6.3. From the likelihood to a hypothesis test

6.4. From Metropolis–Hastings to multiple births and deaths

Chapter 7. Population Counting

7.1. Detection of Virchow–Robin spaces

7.2. Evaluation of forestry resources

7.3. Counting a population of famingos

7.4. Counting the boats at a port

Chapter 8. Structure Extraction

8.1. Detection of the road network

8.2. Extraction of building footprints

8.3. Representation of natural ...