Book description
This book develops the stochastic geometry framework for image analysis purpose. Two main frameworks are described: marked point process and random closed sets models. We derive the main issues for defining an appropriate model. The algorithms for sampling and optimizing the models as well as for estimating parameters are reviewed. Numerous applications, covering remote sensing images, biological and medical imaging, are detailed. This book provides all the necessary tools for developing an image analysis application based on modern stochastic modeling.
Table of contents
- Cover
- Title Page
- Copyright
- Chapter 1: Introduction
- Chapter 2: Marked Point Processes for Object Detection
- Chapter 3: Random Sets for Texture Analysis
-
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
- Chapter 6: How to Set Up a Point Process?
- Chapter 7: Population Counting
- Chapter 8: Structure Extraction
- Chapter 9: Shape Recognition
- Bibliography
- List of Authors
- Index
Product information
- Title: Stochastic Geometry for Image Analysis
- Author(s):
- Release date: December 2011
- Publisher(s): Wiley
- ISBN: 9781848212404
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