Information Fusion in Signal and Image Processing ppt

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www.it-ebooks.infoThis page intentionally left blankwww.it-ebooks.infoInformation Fusion in Signal and Image Processing www.it-ebooks.infoThis page intentionally left blankwww.it-ebooks.infoInformation Fusion in Signal and Image ProcessingMajor Probabilistic and Non-probabilistic Numerical Approaches Edited byIsabelle Bloch www.it-ebooks.infoFirst published in France in 2003 by Hermes Science/Lavoisier entitled “Fusion d’informations en traitement du signal et des images” First published in Great Britain and the United States in 2008 by ISTE Ltd and John Wiley & Sons, Inc. Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address: ISTE Ltd John Wiley & Sons, Inc. 6 Fitzroy Square 111 River Street London W1T 5DX Hoboken, NJ 07030 UK USAwww.iste.co.uk www.wiley.com © ISTE Ltd, 2008 © LAVOISIER, 2003 The rights of Isabelle Bloch to be identified as the author of this work have been asserted by her in accordance with the Copyright, Designs and Patents Act 1988. Library of Congress Cataloging-in-Publication Data [Fusion d'informations en traitement du signal et des images English] Information fusion in signal and image processing / edited by Isabelle Bloch. p. cm. Includes index. ISBN 978-1-84821-019-6 1. Signal processing. 2. Image processing. I. Bloch, Isabelle. TK5102.5.I49511 2008 621.382'2 dc22 2007018231 British Library Cataloguing-in-Publication Data A CIP record for this book is available from the British Library ISBN: 978-1-84821-019-6 Printed and bound in Great Britain by Antony Rowe Ltd, Chippenham, Wiltshire. www.it-ebooks.infoTable of ContentsPreface 11Isabelle BLOCHChapter 1. Definitions 13Isabelle BLOCH and Henri MAÎTRE1.1. Introduction . 131.2. Choosing a definition . . . 131.3. General characteristics of the data . . 161.4. Numerical/symbolic 191.4.1.Dataandinformation 191.4.2.Processes 191.4.3. Representations 201.5.Fusionsystems 201.6. Fusion in signal and image processing and fusion in other fields . . . . 221.7.Bibliography 23Chapter 2. Fusion in Signal Processing 25Jean-Pierre LE CADRE, Vincent NIMIER and Roger REYNAUD2.1. Introduction . 252.2. Objectives of fusion in signal processing . . . . . . . . . 272.2.1. Estimation and calculation of a law a posteriori 282.2.2. Discriminating between several hypotheses and identifying . . . . 312.2.3. Controlling and supervising a data fusion chain . . 342.3. Problems and specificities of fusion in signal processing 372.3.1. Dynamic control . . 372.3.2. Quality of the information . . . . 422.3.3. Representativeness and accuracy of learning and a prioriinformation 432.4.Bibliography 435www.it-ebooks.info6 Information FusionChapter 3. Fusion in Image Processing 47Isabelle BLOCH and Henri MAÎTRE3.1. Objectives of fusion in image processing 473.2.Fusionsituations 503.3. Data characteristics in image fusion . 513.4. Constraints . 543.5. Numerical and symbolic aspects in image fusion . . . . 553.6.Bibliography 56Chapter 4. Fusion in Robotics 57Michèle ROMBAUT4.1. The necessity for fusion in robotics . . 574.2. Specific features of fusion in robotics 584.2.1. Constraints on the perception system 584.2.2. Proprioceptive and exteroceptive sensors . . . . . 584.2.3. Interaction with the operator and symbolic interpretation . . . . . 594.2.4. Time constraints . . 594.3. Characteristics of the data in robotics 614.3.1. Calibrating and changing the frame of reference . 614.3.2. Types and levels of representation of the environment . . . . . . . 624.4. Data fusion mechanisms . 634.5.Bibliography 64Chapter 5. Information and Knowledge Representation in FusionProblems 65Isabelle BLOCH and Henri MAÎTRE5.1. Introduction . 655.2. Processing information in fusion 655.3. Numerical representations of imperfect knowledge . . . 675.4. Symbolic representation of imperfect knowledge . . . . 685.5. Knowledge-based systems 695.6. Reasoning modes and inference . . . . 735.7.Bibliography 74Chapter 6. Probabilistic and Statistical Methods 77Isabelle BLOCH, Jean-Pierre LE CADRE and Henri MAÎTRE6.1. Introduction and general concepts . . 776.2. Information measurements 776.3. Modeling and estimation . 796.4. Combination in a Bayesian framework 806.5. Combination as an estimation problem 806.6. Decision 81www.it-ebooks.infoTable of Contents 76.7. Other methods in detection . . . . . . 816.8. An example of Bayesian fusion in satellite imagery . . 826.9. Probabilistic fusion methods applied to target motion analysis . . . . . 846.9.1. General presentation 846.9.2. Multi-platform target motion analysis . . . . . . . 956.9.3. Target motion analysis by fusion of active and passivemeasurements . 966.9.4. Detection of a moving target in a network of sensors . . . . . . . . 986.10. Discussion 1016.11.Bibliography 104Chapter 7. Belief Function Theory 107Isabelle BLOCH7.1. General concept and philosophy of the theory . . . . . . 1077.2. Modeling 1087.3.Estimationofmassfunctions 1117.3.1. Modification of probabilistic models . . . . . . . . 1127.3.2. Modification of distance models 1147.3.3. A priori information on composite focal elements (disjunctions) . 1147.3.4. Learning composite focal elements . . . . . . . . . 1157.3.5. Introducing disjunctions by mathematical morphology . . . . . . 1157.4. Conjunctive combination 1167.4.1. Dempster’s rule 1167.4.2. Conflict and normalization . . . 1167.4.3. Properties . . . . . . 1187.4.4. Discounting . . . . . 1207.4.5. Conditioning 1207.4.6. Separable mass functions . . . . 1217.4.7. Complexity 1227.5. Other combination modes 1227.6. Decision 1227.7. Application example in medical imaging 1247.8.Bibliography 131Chapter 8. Fuzzy Sets and Possibility Theory 135Isabelle BLOCH8.1. Introduction and general concepts . . 1358.2. Definitions of the fundamental concepts of fuzzy sets . 1368.2.1. Fuzzy sets . . . . . . 1368.2.2. Set operations: Zadeh’s original definitions . . . . 1378.2.3. α-cuts 1398.2.4. Cardinality . . . . . 1398.2.5. Fuzzy number . . . . 140www.it-ebooks.info8 Information Fusion8.3. Fuzzy measures . . . . . . 1428.3.1. Fuzzy measure of a crisp set . . 1428.3.2. Examples of fuzzy measures . . 1428.3.3. Fuzzy integrals . . . 1438.3.4. Fuzzy set measures . 1458.3.5. Measures of fuzziness . . . . . . 1458.4. Elements of possibility theory . . . . . 1478.4.1. Necessity and possibility . . . . 1478.4.2. Possibility distribution . . . . . . 1488.4.3. Semantics 1508.4.4. Similarities with the probabilistic, statistical and beliefinterpretations 1508.5. Combination operators . . 1518.5.1. Fuzzy complementation . . . . . 1528.5.2. Triangular norms and conorms . 1538.5.3. Mean operators . . . 1618.5.4. Symmetric sums 1658.5.5. Adaptive operators . 1678.6. Linguistic variables . . . . 1708.6.1. Definition . . . . . . 1718.6.2. An example of a linguistic variable . . . . . . . . . 1718.6.3. Modifiers 1728.7. Fuzzy and possibilistic logic . . . . . 1728.7.1. Fuzzy logic . . . . . 1738.7.2. Possibilistic logic . . 1778.8. Fuzzy modeling in fusion 1798.9. Defining membership functions or possibility distributions . . . . . . . 1808.10. Combining and choosing the operators . . . . . . . . . 1828.11. Decision 1878.12. Application examples 1888.12.1. Example in satellite imagery . . 1888.12.2. Example in medical imaging 1928.13.Bibliography 194Chapter 9. Spatial Information in Fusion Methods 199Isabelle BLOCH9.1. Modeling 1999.2. The decision level . . . . 2009.3. The combination level 2019.4. Application examples 2019.4.1. The combination level: multi-source Markovian classification . . 2019.4.2. The modeling and decision level: fusion of structure detectorsusing belief function theory . . . 202www.it-ebooks.info[...]... this model The system aspect of fusion will be discussed further in an example in Chapter 10 1.6 Fusion in signal and image processing and fusion in other fields Fusion in signal and image processing has specific features that need to be taken into account at every step when constructing a fusion process These specificities also require modifying and complexifying certain theoretical tools, often taken... chapter: fusion consists of combining information originating from several sources in order to improve decision making In the field of signal processing, the goal of information fusion is to obtain a system to assist decision making, whose main quality (among others) is to be robust when faced with various imprecisions, uncertainties and forms of incompleteness regarding the information sources The basic fusion. .. of information are contradictory, more specific information is preferred Finally, information can be static or dynamic, and again, this leads to different ways of modeling and describing it The information handled in a fusion process is comprised, on the one hand, of the elements of information we wish to fuse together and, on the other hand, of additional information used to guide or assist the combination... GDR-PRC ISIS and to their directors, Odile Macchi and Jean-Marc Chassery Its authors were the coordinators of the workgroup on information fusion and the related actions The GDR was the first initiative that led to bringing together the French community of people working on information fusion in signal and image processing, to build ties with other communities (man-machine communications, robotics and automation,... combining information originating from several sources in order to improve decision making Chapter written by Isabelle B LOCH and Henri M AÎTRE 13 www.it-ebooks.info 14 Information Fusion This definition, which is largely the result of discussions led within the GDR-PRC ISIS1 workgroup on information fusion, is general enough to encompass the diversity of fusion problems encountered in signal and image. .. spatial information in image fusion or in robotics These specificities will be discussed in detail in the case of fusion in signal, image and robotics in the following chapters The quality of the data to be processed and its heterogenity are often more significant than in other fields (problems in combining expert opinions, for example) This causes an additional level of complexity, which has to be taken into... updating Revising or updating consists of completing or modifying an element of information based on new information It can be considered as one of the fields of fusion Sometimes, fusion is considered in a stricter sense, where combination is symmetric As for revision, it is not symmetric and it draws a distinction between information known beforehand and new information Here, we will be considering... and Roger R EYNAUD 25 www.it-ebooks.info 26 Information Fusion The major concepts are directly related to information processing Data fusion systems rely mostly on a series of modeling, estimation, retiming and data association, combination (or fusion itself) of elements of information, and then decision making or supervision steps Going from the knowledge of a bit of information to a mathematical representation... Processing 27 fundamental It is therefore useful to rely on other forms of representing information in order to increase the model’s reliability by considering information of smaller meaning, or by adding mechanisms for sorting, windowing, etc., to authorize this semantic information to be taken into account A certain number of difficulties in data fusion are caused by generic problems that are independent... discussed: signal processing in Chapter 2, image processing in Chapter 3 and robotics in Chapter 4 The second part is concerned with the major theories of fusion After an overview of the modes of knowledge representation used in fusion (Chapter 5), we present the principles of probabilistic and statistical fusion in Chapter 6, of belief function theory in Chapter 7, of fuzzy and possibilistic fusion in Chapter . www.it-ebooks.infoThis page intentionally left blankwww.it-ebooks.info Information Fusion in Signal and Image Processing www.it-ebooks.infoThis page intentionally. 191.4.1.Dataandinformation 191.4.2.Processes 191.4.3. Representations 201.5.Fusionsystems 201.6. Fusion in signal and image processing and fusion in other
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Xem thêm: Information Fusion in Signal and Image Processing ppt, Information Fusion in Signal and Image Processing ppt, Information Fusion in Signal and Image Processing ppt, Chapter 2. Fusion in Signal Processing, Chapter 3. Fusion in Image Processing, Chapter 5. Information and Knowledge Representation in Fusion Problems, Chapter 6. Probabilistic and Statistical Methods, Chapter 8. Fuzzy Sets and Possibility Theory, Chapter 9. Spatial Information in Fusion Methods, Chapter 10. Multi-Agent Methods: An Example of an Architecture and its Application for the Detection, Recognition and Identification of Targets, Chapter 11. Fusion of Non-Simultaneous Elements of Information: Temporal Fusion, A. Probabilities: A Historical Perspective, B. Axiomatic Inference of the Dempster-Shafer Combination Rule

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