Intro
Acknowledgements
Contents
Acronyms and Notations
Abbreviations and Concepts
General Notations Used in the Book
Specific Notations Used in MRF/CXM Models
Specific Notations Used in MPP Models
1 Introduction
2 Fundamentals
2.1 Measurement Representation and Problem Formulations
2.2 Markovian Classification Models
2.2.1 Markov Random Fields, Gibbs Potentials, and Observation Processes
2.2.2 Bayesian Labeling Approach and the Potts Model
2.2.3 MRF-Based Image Segmentation
2.2.4 MRF Optimization
2.2.5 Mixed Markov Models

2.3 Object Population Extraction with Marked Point Processes
2.3.1 Definition of Marked Point Processes
2.3.2 MPP Energy Functions
2.3.3 MPP Optimization
2.4 Methodological Contributions of the Book
3 Bayesian Models for Dynamic Scene Analysis
3.1 Dynamic Scene Perception
3.2 Foreground Extraction in Video Sequences
3.2.1 Related Work in Video-Based Foreground Detection
3.2.2 MRF Model for Foreground Extraction
3.2.3 Probabilistic Model of the Background and Shadow Processes
3.2.4 Microstructural Features
3.2.5 Foreground Probabilities

3.2.6 Parameter Settings
3.2.7 MRF Optimization
3.2.8 Results
3.2.9 Summary and Applications of Foreground Segmentation
3.3 People Localization in Multi-camera Systems
3.3.1 A New Approach on Multi-view People Localization
3.3.2 Silhouette-Based Feature Extraction
3.3.3 3D Marked Point Process Model
3.3.4 Evaluation of Multi-camera People Localization
3.3.5 Applications and Alternative Ways of 3D Person Localization
3.4 Foreground Extraction in Lidar Point Cloud Sequences
3.4.1 Problem Formulation and Data Mapping
3.4.2 Background Model

3.4.3 DMRF Approach on Foreground Segmentation
3.4.4 Evaluation of DMRF-Based Foreground-Background Separation
3.4.5 Application of the DMFR Method for Person and Activity Recognition
3.5 Conclusions
4 Multi-layer Label Fusion Models
4.1 Markovian Fusion Models in Computer Vision
4.2 A Label Fusion Model for Object Motion Detection
4.2.1 2D Image Registration
4.2.2 Change Detection with 3D Approach
4.2.3 Feature Selection
4.2.4 Multi-layer Segmentation Model
4.2.5 L3Mrf Optimization
4.2.6 Experiments on Object Motion Detection

4.3 Long-Term Change Detection in Aerial Photos
4.3.1 Image Model and Feature Extraction
4.3.2 A Conditional Mixed Markov Image Segmentation Model
4.3.3 Experiments on Long-Term Change Detection
4.4 Parameter Settings in Multi-layer Segmentation Models
4.5 Conclusions
5 Multitemporal Data Analysis with Marked Point Processes
5.1 Introducing the Time Dimension in MPP Models
5.2 Object-Level Change Detection
5.2.1 Building Development Monitoring-Problem Definition
5.2.2 Feature Selection
5.2.3 Multitemporal MPP Configuration Model and Optimization