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

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).Content: Chapter 1 Definitions (pages 13–24): Isabelle Bloch and Henri MaitreChapter 2 Fusion in Signal Processing (pages 25–46): Jean?Pierre Le Cadre, Vincent Nimier and Roger ReynaudChapter 3 Fusion in Image Processing (pages 47–56): Isabelle Bloch and Henri MaitreChapter 4 Fusion in Robotics (pages 57–64): Michele RombautChapter 5 Information and Knowledge Representation in Fusion Problems (pages 65–75): Isabelle Bloch and Henri MaitreChapter 6 Probabilistic and Statistical Methods (pages 77–106): Isabelle Bloch, Jean?Pierre Le Cadre and Henri MaitreChapter 7 Belief Function Theory (pages 107–133): Isabelle BlochChapter 8 Fuzzy Sets and Possibility Theory (pages 135–197): Isabelle BlochChapter 9 Spatial Information in Fusion Methods (pages 199–212): Isabelle BlochChapter 10 Multi?Agent Methods: An Example of an Architecture and its Application for the Detection, Recognition and Identification of Targets (pages 213–243): Fabienne Ealet, Bertrand Collin and Catherine GarbayChapter 11 Fusion of Non?Simultaneous Elements of Information: Temporal Fusion (pages 245–258): Michele RombautChapter 12 Conclusion (pages 259–262): Isabelle BlochChapter A Probabilities: A Historical Perspective (pages 263–281): Isabelle BlochChapter B Axiomatic Inference of the Dempster?Shafer Combination Rule (pages 283–290): Isabelle Bloch