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...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London : Hoboken, NJ :
ISTE ; Wiley
2008.
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| Edición: | 1st edition |
| Colección: | ISTE
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627849606719 |
Tabla de Contenidos:
- Information Fusion in Signal and Image Processing; Table of Contents; Preface; Chapter 1. Definitions; 1.1. Introduction; 1.2. Choosing a definition; 1.3. General characteristics of the data; 1.4. Numerical/symbolic; 1.4.1. Data and information; 1.4.2. Processes; 1.4.3. Representations; 1.5. Fusion systems; 1.6. Fusion in signal and image processing and fusion in other fields; 1.7. Bibliography; Chapter 2. Fusion in Signal Processing; 2.1. Introduction; 2.2. Objectives of fusion in signal processing; 2.2.1. Estimation and calculation of a law a posteriori
- 2.2.2. Discriminating between several hypotheses and identifying2.2.3. Controlling and supervising a data fusion chain; 2.3. Problems and specificities of fusion in signal processing; 2.3.1. Dynamic control; 2.3.2. Quality of the information; 2.3.3. Representativeness and accuracy of learning and a priori information; 2.4. Bibliography; Chapter 3. Fusion in Image Processing; 3.1. Objectives of fusion in image processing; 3.2. Fusion situations; 3.3. Data characteristics in image fusion; 3.4. Constraints; 3.5. Numerical and symbolic aspects in image fusion; 3.6. Bibliography
- Chapter 4. Fusion in Robotics4.1. The necessity for fusion in robotics; 4.2. Specific features of fusion in robotics; 4.2.1. Constraints on the perception system; 4.2.2. Proprioceptive and exteroceptive sensors; 4.2.3. Interaction with the operator and symbolic interpretation; 4.2.4. Time constraints; 4.3. Characteristics of the data in robotics; 4.3.1. Calibrating and changing the frame of reference; 4.3.2. Types and levels of representation of the environment; 4.4. Data fusion mechanisms; 4.5. Bibliography; Chapter 5. Information and Knowledge Representation in Fusion Problems
- 5.1. Introduction5.2. Processing information in fusion; 5.3. Numerical representations of imperfect knowledge; 5.4. Symbolic representation of imperfect knowledge; 5.5. Knowledge-based systems; 5.6. Reasoning modes and inference; 5.7. Bibliography; Chapter 6. Probabilistic and Statistical Methods; 6.1. Introduction and general concepts; 6.2. Information measurements; 6.3. Modeling and estimation; 6.4. Combination in a Bayesian framework; 6.5. Combination as an estimation problem; 6.6. Decision; 6.7. Other methods in detection; 6.8. An example of Bayesian fusion in satellite imagery
- 6.9. Probabilistic fusion methods applied to target motion analysis6.9.1. General presentation; 6.9.2. Multi-platform target motion analysis; 6.9.3. Target motion analysis by fusion of active and passive measurements; 6.9.4. Detection of a moving target in a network of sensors; 6.10. Discussion; 6.11. Bibliography; Chapter 7. Belief Function Theory; 7.1. General concept and philosophy of the theory; 7.2. Modeling; 7.3. Estimation of mass functions; 7.3.1. Modification of probabilistic models; 7.3.2. Modification of distance models
- 7.3.3. A priori information on composite focal elements (disjunctions)
