|
|
|
|
| LEADER |
00000cam a2200000 i 4500 |
| 001 |
ocn889305696 |
| 003 |
OCoLC |
| 005 |
20240809213013.0 |
| 006 |
m o d |
| 007 |
cr cn||||||||| |
| 008 |
140721t20142014enka obf 001 0 eng d |
| 040 |
|
|
|a E7B
|b eng
|e rda
|e pn
|c E7B
|d YDXCP
|d OCLCF
|d EBLCP
|d DG1
|d N$T
|d RECBK
|d DEBSZ
|d OCLCO
|d COO
|d OCLCQ
|d OCLCO
|d OCL
|d OCLCO
|d DEBBG
|d OCLCO
|d OCLCQ
|d COCUF
|d DG1
|d LIP
|d PIFBY
|d ZCU
|d MERUC
|d OCLCQ
|d U3W
|d OCLCQ
|d STF
|d ICG
|d INT
|d VT2
|d OCLCQ
|d WYU
|d TKN
|d OCLCQ
|d DKC
|d OCLCQ
|d UKAHL
|d VLY
|d TUHNV
|d OCLCO
|d OCLCQ
|d OCLCO
|d OCLCL
|
| 016 |
7 |
|
|a 016786362
|2 Uk
|
| 019 |
|
|
|a 884014933
|a 889728014
|a 894731733
|a 961582953
|a 962702761
|a 992821813
|a 1055392933
|a 1081228812
|a 1162464852
|
| 020 |
|
|
|a 9781118578575
|q (e-book)
|
| 020 |
|
|
|a 1118578570
|q (e-book)
|
| 020 |
|
|
|a 1118578635
|
| 020 |
|
|
|a 9781118578636
|
| 020 |
|
|
|a 1118578678
|
| 020 |
|
|
|a 9781118578674
|
| 020 |
|
|
|z 9781848213548
|
| 020 |
|
|
|z 1848213549
|
| 035 |
|
|
|a (OCoLC)889305696
|z (OCoLC)884014933
|z (OCoLC)889728014
|z (OCoLC)894731733
|z (OCoLC)961582953
|z (OCoLC)962702761
|z (OCoLC)992821813
|z (OCoLC)1055392933
|z (OCoLC)1081228812
|z (OCoLC)1162464852
|
| 050 |
|
4 |
|a TK7872.D48
|b .A677 2014eb
|
| 072 |
|
7 |
|a TEC
|x 009070
|2 bisacsh
|
| 049 |
|
|
|a HCDD
|
| 100 |
1 |
|
|a Appriou, Alain,
|e author.
|
| 245 |
1 |
0 |
|a Uncertainty theories and multisensor data fusion /
|c Alain Appriou.
|
| 264 |
|
1 |
|a London [England] ;
|a Hoboken, New Jersey :
|b ISTE :
|b Wiley,
|c 2014.
|
| 264 |
|
4 |
|c ©2014
|
| 300 |
|
|
|a 1 online resource (278 pages) :
|b illustrations, graphs
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 490 |
1 |
|
|a Instrumentation and measurement series
|
| 504 |
|
|
|a Includes bibliographical references and index.
|
| 588 |
0 |
|
|a Print version record.
|
| 505 |
0 |
|
|a Cover; Title Page; Copyright; Contents; Introduction; Chapter 1. Multisensor Data Fusion; 1.1. Issues at stake; 1.2. Problems; 1.2.1. Interpretation and modeling of data; 1.2.2. Reliability handling; 1.2.3. Knowledge propagation; 1.2.4. Matching of ambiguous data; 1.2.5. Combination of sources; 1.2.6. Decision-making; 1.3. Solutions; 1.3.1. Panorama of useful theories; 1.3.2. Process architectures; 1.4. Position of multisensor data fusion; 1.4.1. Peculiarities of the problem; 1.4.2. Applications of multisensor data fusion; Chapter 2. Reference Formalisms; 2.1. Probabilities; 2.2. Fuzzy sets.
|
| 505 |
8 |
|
|a 2.3. Possibility theory2.4. Belief functions theory; 2.4.1. Basic functions; 2.4.2. A few particularly useful cases; 2.4.3. Conditioning/deconditioning; 2.4.4. Refinement/coarsening; Chapter 3. Set Management and Information Propagation; 3.1. Fuzzy sets: propagation of imprecision; 3.2. Probabilities and possibilities: the same approach to uncertainty; 3.3. Belief functions: an overarching vision in terms of propagation; 3.3.1. A generic operator: extension; 3.3.2. Elaboration of a mass function with minimum specificity; 3.3.3. Direct exploitation of the operator of extension.
|
| 505 |
8 |
|
|a 3.4. Example of application: updating of knowledge over timeChapter 4. Managing The Reliability of Information; 4.1. Possibilistic view; 4.2. Discounting of belief functions; 4.3. Integrated processing of reliability; 4.4. Management of domains of validity of the sources; 4.5. Application to fusion of pixels from multispectral images; 4.6. Formulation for problems of estimation; Chapter 5. Combination of Sources; 5.1. Probabilities: a turnkey solution, Bayesian inference; 5.2. Fuzzy sets: a grasp of axiomatics; 5.3. Possibility theory: a simple approach to the basic principles.
|
| 505 |
8 |
|
|a 5.4. Theory of belief functions: conventional approaches5.5. General approach to combination: any sets and logics; 5.6. Conflict management; 5.7. Back to Zadeh's paradox; Chapter 6. Data Modeling; 6.1. Characterization of signals; 6.2. Probabilities: immediate taking into account; 6.3. Belief functions: an open-ended and overarching framework; 6.3.1. Integration of data into the fusion process; 6.3.2. Generic problem: modeling of Cij values; 6.3.3. Modeling measurements with stochastic learning; 6.3.4. Modeling measurements with fuzzy learning; 6.3.5. Overview of models for belief functions.
|
| 505 |
8 |
|
|a 6.4. Possibilities: a similar approach6.5. Application to a didactic example of classification; Chapter 7. Classification: Decision-Making And Exploitation of the Diversity of Information Sources; 7.1. Decision-making: choice of the most likely hypothesis; 7.2. Decision-making: determination of the most likely set of hypotheses; 7.3. Behavior of the decision operator: some practical examples; 7.4. Exploitation of the diversity of information sources: integration of binary comparisons.
|
| 520 |
|
|
|a Addressing recent challenges and developments in this growing field, Multisensor Data Fusion Uncertainty Theory first discusses basic questions such as: Why and when is multiple sensor fusion necessary? How can the available measurements be characterized in such a case? What is the purpose and the specificity of information fusion processing in multiple sensor systems? Considering the different uncertainty formalisms, a set of coherent operators corresponding to the different steps of a complete fusion process is then developed, in order to meet the requirements identified in the first part of the book.
|
| 546 |
|
|
|a English.
|
| 650 |
|
0 |
|a Multisensor data fusion.
|
| 650 |
|
0 |
|a Multisensor data fusion
|v Congresses.
|
| 650 |
|
0 |
|a Multisensor data fusion
|v Handbooks, manuals, etc.
|
| 650 |
|
7 |
|a TECHNOLOGY & ENGINEERING
|x Mechanical.
|2 bisacsh
|
| 650 |
|
7 |
|a Multisensor data fusion
|2 fast
|
| 655 |
|
7 |
|a handbooks.
|2 aat
|
| 655 |
|
7 |
|a Conference papers and proceedings
|2 fast
|
| 655 |
|
7 |
|a Handbooks and manuals
|2 fast
|
| 655 |
|
7 |
|a Handbooks and manuals.
|2 lcgft
|
| 655 |
|
7 |
|a Guides et manuels.
|2 rvmgf
|
| 758 |
|
|
|i has work:
|a Uncertainty theories and multisensor data fusion (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCFxddJDbYDV4H6JdVVtVG3
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Appriou, Alain.
|t Uncertainty theories and multisensor data fusion.
|d London, [England] ; Hoboken, New Jersey : ISTE : Wiley, ©2014
|h xiv, 262 pages
|k Instrumentation and measurement series.
|z 9781848213548
|
| 830 |
|
0 |
|a Instrumentation and measurement series.
|
| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=1734296
|y Click for online access
|
| 903 |
|
|
|a EBC-AC
|
| 994 |
|
|
|a 92
|b HCD
|