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

Preface

Over the past few years, the field of information fusion has gone through considerable and rapid change. While it is difficult to write a book in such a dynamic environment, this book is justified by the fact that the field is currently at a turning point. After a phase of questions, debates, and even mistakes, during which the field of fusion in signal and image processing was not well defined, we are now able to efficiently use basic tools (often imported from other fields) and it is now possible to both design entire applications, and develop more complex and sophisticated tools. Nevertheless, there remains much theoretical work to be done in order to broaden the foundations of these methods, as well as experimental work to validate their use.

The objectives of this book are to present, on the one hand, the general ideas of fusion and its specificities in signal and image processing and in robotics, and on the other hand, the major methods and tools, which are essentially numerical. This book does not intend, of course, to compete with those devoted entirely to one of these tools, or one of these applications, but instead tries to underline the assets of the different theories in the intended application fields.

With a book like this one, we cannot aspire to be comprehensive. We will not discuss methods based on expert or multi-agent systems (however, an example will be given to illustrate them), on neural networks and all of the symbolic methods expressed in logical ...