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Information Fusion in Signal and Image Processing: Major Probabilistic and Non-Probabilistic Numerical Approaches

Book Description

The area of information fusion has grown considerably during the last few years, leading to a rapid and impressive evolution. In such fast-moving times, it is important to take stock of the changes that have occurred. As such, this books offers an overview of the general principles and specificities of information fusion in signal and image processing, as well as covering the main numerical methods (probabilistic approaches, fuzzy sets and possibility theory and belief functions).

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Table of Contents
  5. Preface
  6. Chapter 1: Definitions
    1. 1.1. Introduction
    2. 1.2. Choosing a definition
    3. 1.3. General characteristics of the data
    4. 1.4. Numerical/symbolic
    5. 1.5. Fusion systems
    6. 1.6. Fusion in signal and image processing and fusion in other fields
    7. 1.7. Bibliography
  7. Chapter 2: Fusion in Signal Processing
    1. 2.1. Introduction
    2. 2.2. Objectives of fusion in signal processing
    3. 2.3. Problems and specificities of fusion in signal processing
    4. 2.4. Bibliography
  8. Chapter 3: Fusion in Image Processing
    1. 3.1. Objectives of fusion in image processing
    2. 3.2. Fusion situations
    3. 3.3. Data characteristics in image fusion
    4. 3.4. Constraints
    5. 3.5. Numerical and symbolic aspects in image fusion
    6. 3.6. Bibliography
  9. Chapter 4: Fusion in Robotics
    1. 4.1. The necessity for fusion in robotics
    2. 4.2. Specific features of fusion in robotics
    3. 4.3. Characteristics of the data in robotics
    4. 4.4. Data fusion mechanisms
    5. 4.5. Bibliography
  10. Chapter 5: Information and Knowledge Representation in Fusion Problems
    1. 5.1. Introduction
    2. 5.2. Processing information in fusion
    3. 5.3. Numerical representations of imperfect knowledge
    4. 5.4. Symbolic representation of imperfect knowledge
    5. 5.5. Knowledge-based systems
    6. 5.6. Reasoning modes and inference
    7. 5.7. Bibliography
  11. Chapter 6: Probabilistic and Statistical Methods
    1. 6.1. Introduction and general concepts
    2. 6.2. Information measurements
    3. 6.3. Modeling and estimation
    4. 6.4. Combination in a Bayesian framework
    5. 6.5. Combination as an estimation problem
    6. 6.6. Decision
    7. 6.7. Other methods in detection
    8. 6.8. An example of Bayesian fusion in satellite imagery
    9. 6.9. Probabilistic fusion methods applied to target motion analysis
    10. 6.10. Discussion
    11. 6.11. Bibliography
  12. Chapter 7: Belief Function Theory
    1. 7.1. General concept and philosophy of the theory
    2. 7.2. Modeling
    3. 7.3. Estimation of mass functions
    4. 7.4. Conjunctive combination
    5. 7.5. Other combination modes
    6. 7.6. Decision
    7. 7.7. Application example in medical imaging
    8. 7.8. Bibliography
  13. Chapter 8: Fuzzy Sets and Possibility Theory
    1. 8.1. Introduction and general concepts
    2. 8.2. Definitions of the fundamental concepts of fuzzy sets
    3. 8.3. Fuzzy measures
    4. 8.4. Elements of possibility theory
    5. 8.5. Combination operators
    6. 8.6. Linguistic variables
    7. 8.7. Fuzzy and possibilistic logic
    8. 8.8 Fuzzy modeling in fusion
    9. 8.9. Defining membership functions or possibility distributions
    10. 8.10. Combining and choosing the operators
    11. 8.11. Decision
    12. 8.12. Application examples
    13. 8.13. Bibliography
  14. Chapter 9: Spatial Information in Fusion Methods
    1. 9.1. Modeling
    2. 9.2. The decision level
    3. 9.3. The combination level
    4. 9.4. Application examples
    5. 9.5. Bibliography
  15. Chapter 10: Multi-Agent Methods: An Example of an Architecture and its Application for the Detection, Recognition and Identification of Targets
    1. 10.1. The DRI function
    2. 10.2. Proposed method: towards a vision system
    3. 10.3. The multi-agent system: platform and architecture
    4. 10.4. The control scheme
    5. 10.5. The information handled by the agents
    6. 10.6. The results
    7. 10.7. Bibliography
  16. Chapter 11: Fusion of Non-Simultaneous Elements of Information: Temporal Fusion
    1. 11.1. Time variable observations
    2. 11.2. Temporal constraints
    3. 11.3. Fusion
    4. 11.4. Dating measurements
    5. 11.5. Evolutionary models
    6. 11.6. Single sensor prediction-combination
    7. 11.7. Multi-sensor prediction-combination
    8. 11.8. Conclusion
    9. 11.9. Bibliography
  17. Chapter 12: Conclusion
    1. 12.1. A few achievements
    2. 12.2. A few prospects
    3. 12.3. Bibliography
  18. Appendix A: Probabilities: A Historical Perspective
    1. A.1. Probabilities through history
    2. A.2. Objectivist and subjectivist probability classes
    3. A.3. Fundamental postulates for an inductive logic
    4. A.4. Bibliography
  19. Appendix B: Axiomatic Inference of the Dempster-Shafer Combination Rule
    1. B.1. Smets's axioms
    2. B.2. Inference of the combination rule
    3. B.3. Relation with Cox's postulates
    4. B.4. Bibliography
  20. List of Authors
  21. Index