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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
3.2. 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
5.2. 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
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 ...