Academic literature on the topic 'Information fusion'
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Journal articles on the topic "Information fusion"
Zhang, Xin, Li Yang, and Yan Zhang. "Multi-Source Information Fusion Based on Data Driven." Applied Mechanics and Materials 40-41 (November 2010): 121–26. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.121.
Full textYao, JingTao, Vijay V. Raghavan, and Zonghuan Wu. "Web information fusion." Information Fusion 9, no. 4 (October 2008): 444–45. http://dx.doi.org/10.1016/j.inffus.2008.05.001.
Full textAnderson, Peter Meade, Luisa-Marie Manning, Brian Rubin, Timothy A. Chan, Scott E. Kilpatrick, Rabi Hanna, and Zheng Jin Tu. "Nested set information derived from fusion genes in Ewing sarcoma and other cancers." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e23521-e23521. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e23521.
Full textPopovich, V. V. "Information fusion and geographic information systems." Herald of the Russian Academy of Sciences 77, no. 4 (August 2007): 429–30. http://dx.doi.org/10.1134/s1019331607040181.
Full textChiu, Readman, Ka Ming Nip, and Inanc Birol. "Fusion-Bloom: fusion detection in assembled transcriptomes." Bioinformatics 36, no. 7 (December 2, 2019): 2256–57. http://dx.doi.org/10.1093/bioinformatics/btz902.
Full textRoss, Arun, and Anil Jain. "Information fusion in biometrics." Pattern Recognition Letters 24, no. 13 (September 2003): 2115–25. http://dx.doi.org/10.1016/s0167-8655(03)00079-5.
Full textDas, Subrata. "Agent-based information fusion." Information Fusion 11, no. 3 (July 2010): 216–19. http://dx.doi.org/10.1016/j.inffus.2010.01.008.
Full textSnidaro, Lauro, Jesus Garcia, and Juan Manuel Corchado. "Context-based information fusion." Information Fusion 21 (January 2015): 82–84. http://dx.doi.org/10.1016/j.inffus.2014.02.001.
Full textWillett, P. K. "FUSION 2000 third international conference on information fusion." IEEE Aerospace and Electronic Systems Magazine 16, no. 2 (February 2001): 21–25. http://dx.doi.org/10.1109/maes.2001.904240.
Full textXue, Ying Hua, and Jing Li. "Distributed Information Fusion Structure Based on Data Fusion Tree." Advanced Materials Research 225-226 (April 2011): 488–91. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.488.
Full textDissertations / Theses on the topic "Information fusion"
Xu, Philippe. "Information fusion for scene understanding." Thesis, Compiègne, 2014. http://www.theses.fr/2014COMP2153/document.
Full textImage understanding is a key issue in modern robotics, computer vison and machine learning. In particular, driving scene understanding is very important in the context of advanced driver assistance systems for intelligent vehicles. In order to recognize the large number of objects that may be found on the road, several sensors and decision algorithms are necessary. To make the most of existing state-of-the-art methods, we address the issue of scene understanding from an information fusion point of view. The combination of many diverse detection modules, which may deal with distinct classes of objects and different data representations, is handled by reasoning in the image space. We consider image understanding at two levels : object detection ans semantic segmentation. The theory of belief functions is used to model and combine the outputs of these detection modules. We emphazise the need of a fusion framework flexible enough to easily include new classes, new sensors and new object detection algorithms. In this thesis, we propose a general method to model the outputs of classical machine learning techniques as belief functions. Next, we apply our framework to the combination of pedestrian detectors using the Caltech Pedestrain Detection Benchmark. The KITTI Vision Benchmark Suite is then used to validate our approach in a semantic segmentation context using multi-modal information
Kaupp, Tobias. "Probabilistic Human-Robot Information Fusion." Thesis, The University of Sydney, 2008. http://hdl.handle.net/2123/2554.
Full textKaupp, Tobias. "Probabilistic Human-Robot Information Fusion." University of Sydney, 2008. http://hdl.handle.net/2123/2554.
Full textThis thesis is concerned with combining the perceptual abilities of mobile robots and human operators to execute tasks cooperatively. It is generally agreed that a synergy of human and robotic skills offers an opportunity to enhance the capabilities of today’s robotic systems, while also increasing their robustness and reliability. Systems which incorporate both human and robotic information sources have the potential to build complex world models, essential for both automated and human decision making. In this work, humans and robots are regarded as equal team members who interact and communicate on a peer-to-peer basis. Human-robot communication is addressed using probabilistic representations common in robotics. While communication can in general be bidirectional, this work focuses primarily on human-to-robot information flow. More specifically, the approach advocated in this thesis is to let robots fuse their sensor observations with observations obtained from human operators. While robotic perception is well-suited for lower level world descriptions such as geometric properties, humans are able to contribute perceptual information on higher abstraction levels. Human input is translated into the machine representation via Human Sensor Models. A common mathematical framework for humans and robots reinforces the notion of true peer-to-peer interaction. Human-robot information fusion is demonstrated in two application domains: (1) scalable information gathering, and (2) cooperative decision making. Scalable information gathering is experimentally demonstrated on a system comprised of a ground vehicle, an unmanned air vehicle, and two human operators in a natural environment. Information from humans and robots was fused in a fully decentralised manner to build a shared environment representation on multiple abstraction levels. Results are presented in the form of information exchange patterns, qualitatively demonstrating the benefits of human-robot information fusion. The second application domain adds decision making to the human-robot task. Rational decisions are made based on the robots’ current beliefs which are generated by fusing human and robotic observations. Since humans are considered a valuable resource in this context, operators are only queried for input when the expected benefit of an observation exceeds the cost of obtaining it. The system can be seen as adjusting its autonomy at run-time based on the uncertainty in the robots’ beliefs. A navigation task is used to demonstrate the adjustable autonomy system experimentally. Results from two experiments are reported: a quantitative evaluation of human-robot team effectiveness, and a user study to compare the system to classical teleoperation. Results show the superiority of the system with respect to performance, operator workload, and usability.
Johansson, Ronnie. "Large-Scale Information Acquisition for Data and Information Fusion." Doctoral thesis, Stockholm, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3890.
Full textJohansson, Ronnie. "Information Acquisition in Data Fusion Systems." Licentiate thesis, KTH, Numerical Analysis and Computer Science, NADA, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1673.
Full textBy purposefully utilising sensors, for instance by a datafusion system, the state of some system-relevant environmentmight be adequately assessed to support decision-making. Theever increasing access to sensors o.ers great opportunities,but alsoincurs grave challenges. As a result of managingmultiple sensors one can, e.g., expect to achieve a morecomprehensive, resolved, certain and more frequently updatedassessment of the environment than would be possible otherwise.Challenges include data association, treatment of con.ictinginformation and strategies for sensor coordination.
We use the term information acquisition to denote the skillof a data fusion system to actively acquire information. Theaim of this thesis is to instructively situate that skill in ageneral context, explore and classify related research, andhighlight key issues and possible future work. It is our hopethat this thesis will facilitate communication, understandingand future e.orts for information acquisition.
The previously mentioned trend towards utilisation of largesets of sensors makes us especially interested in large-scaleinformation acquisition, i.e., acquisition using many andpossibly spatially distributed and heterogeneous sensors.
Information acquisition is a general concept that emerges inmany di.erent .elds of research. In this thesis, we surveyliterature from, e.g., agent theory, robotics and sensormanagement. We, furthermore, suggest a taxonomy of theliterature that highlights relevant aspects of informationacquisition.
We describe a function, perception management (akin tosensor management), which realizes information acquisition inthe data fusion process and pertinent properties of itsexternal stimuli, sensing resources, and systemenvironment.
An example of perception management is also presented. Thetask is that of managing a set of mobile sensors that jointlytrack some mobile targets. The game theoretic algorithmsuggested for distributing the targets among the sensors proveto be more robust to sensor failure than a measurement accuracyoptimal reference algorithm.
Keywords:information acquisition, sensor management,resource management, information fusion, data fusion,perception management, game theory, target tracking
Nouranian, Saman. "Information fusion for prostate brachytherapy planning." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58305.
Full textApplied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
Dalmas, Tiphaine. "Information fusion for automated question answering." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/27860.
Full textOreshkin, Boris. "Distributed information fusion in sensor networks." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86916.
Full textFor the distributed average consensus algorithm a memory based acceleration methodology is proposed. The convergence of the proposed methodology is investigated. For the two important settings of this methodology, optimal values of system parameters are determined and improvement with respect to the standard distributed average consensus algorithm is theoretically characterized. The theoretical improvement characterization matches well with the results of numerical experiments revealing significant and well scaling gain. The practical distributed on-line initialization scheme is devised. Numerical experiments reveal the feasibility of the proposed initialization scheme and superior performance of the proposed methodology with respect to several existing acceleration approaches.
For the collaborative signal and information processing methodology a number of theoretical performance guarantees is obtained. The collaborative signal and information processing framework consists in activating only a cluster of wireless sensors to perform target tracking task in the cluster head using particle filter. The optimal cluster is determined at every time instant and cluster head hand-off is performed if necessary. To reduce communication costs only an approximation of the filtering distribution is sent during hand-off resulting in additional approximation errors. The time uniform performance guarantees accounting for the additional errors are obtained in two settings: the subsample approximation and the parametric mixture approximation hand-off.
Cette thèse aborde le problème de la conception et l'analyse d'algorithmes distribuès servant à l'agrégation efficace et la fusion de l'information dans des reséaux capteurs sans fil. Ces algorithmes distribuès servent à addresser un bon nombre d'inconvénients qu'ont les approches de fusion centralisée telles que le point de défaillance unique, les protocoles de routage complexe, la consommation de puissance inégale dans les noeuds de capteurs, l'utilisation inefficace des voies de transmission sans-fil et l'extensibilité limitée. Ces inconvénients de l'approche centralisée ont comme effet de réduire la durée de vie du reséau, la robustesse des noeuds face aux défaillances et la capacité du réseau. Les algorithmes distribuès atténuent ces problèmes en utilisant des simples protocoles de messageries entre les noeuds ainsi que du traitement d'information localisé. Toutefois, pour ces algorithmes, les pertes de précision et/ou de temps nécessaire pour effectuer une tâche peuvent être importantes. C'est pourquoi la conception et l'analyse d'algorithmes distribuès rapide et précis est importante. Dans cette thèse, deux problèmes spécifiques associés à l'analyse et le conception de tels algorithms sont abordés.
En ce qui concerne l'algorithme de consensus sur la moyenne distribuè, une méthode d'accélération fondé sur la mémoire est proposée et sa convergence analysée. Pour les deux paramètres importants de cette méthodologie, les valeurs optimales pour le système sont déterminées et l'amélioration par rapport à l'algorithme de consensus de base est caractérisée de façon théorique. Cette caractérisation correspond aux resultants d'expériences numériques et révèlent des gains importants et extensibles. Le régime distribuè d'initialisation en ligne est conçu. Des expériences numériques révèlevent la faisabilité du régime d'initilisation proposé ainsi qu'un rendement supérieur à plusieurs approches existantes.
Pour la méthodologie de traitement de signaux et d'information collaborative, un certain nombre de garanties théoriques de performance sont obtenues. Ce cadre de travail consiste à activer seulement une grappe de capteurs sans fil pour effectuer les tâches de pistage d'objet au niveau deu chef de groupe en utilisant un filtre particulaire. La grappe optimale est déterminée à chaque intervale de temps et le transfert du titre de chef de groupe est réalisé au besoin. Pour réduire les coûts de communication, seulement une approximation de la distribution du filtre est envoyé pendant le transfert de responsabilités ce qui entraîne des erreurs supplémentaires. Les garanties de performance uniformes dans le temps tenant compte de ces erreurs supplémentaires sont obtenues dans deux contextes.
Peacock, Andrew M. "Information fusion for improved motion estimation." Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/428.
Full textCavanaugh, Andrew F. "Bayesian Information Fusion for Precision Indoor Location." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/157.
Full textBooks on the topic "Information fusion"
Li, Jinxing, Bob Zhang, and David Zhang. Information Fusion. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8976-5.
Full textPopovich, Vasily V., Christophe Claramunt, Manfred Schrenk, and Kyrill V. Korolenko, eds. Information Fusion and Geographic Information Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00304-2.
Full textPopovich, Vasily V., Christophe Claramunt, Thomas Devogele, Manfred Schrenk, and Kyrill Korolenko, eds. Information Fusion and Geographic Information Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19766-6.
Full textPopovich, Vasily V., Manfred Schrenk, and Kyrill V. Korolenko, eds. Information Fusion and Geographic Information Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37629-3.
Full textM, Jordan John, ed. Human-centered information fusion. Boston: Artech House, 2010.
Find full textBouchon-Meunier, Bernadette, Ronald R. Yager, and Lotfi A. Zadeh, eds. Information, Uncertainty and Fusion. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5209-3.
Full textSnidaro, Lauro, Jesús García, James Llinas, and Erik Blasch, eds. Context-Enhanced Information Fusion. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28971-7.
Full text1948-, Bouchon-Meunier B., Yager Ronald R. 1941-, and Zadeh Lotfi Asker, eds. Information, uncertainty, and fusion. Boston: Kluwer Academic Publishers, 2000.
Find full textBouchon-Meunier, Bernadette. Information, Uncertainty and Fusion. Boston, MA: Springer US, 2000.
Find full textPopovich, Vasily, Jean-Claude Thill, Manfred Schrenk, and Christophe Claramunt, eds. Information Fusion and Intelligent Geographic Information Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31608-2.
Full textBook chapters on the topic "Information fusion"
Papini, Odile. "Information Fusion." In Lecture Notes in Computer Science, 20–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15951-0_7.
Full textShekhar, Shashi, and Hui Xiong. "Information Fusion." In Encyclopedia of GIS, 571. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_632.
Full textSteinhauer, H. Joe, and Alexander Karlsson. "Information Fusion." In Studies in Big Data, 61–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97556-6_4.
Full textNguyen, Tuan Tran. "Information Fusion." In AutoUni – Schriftenreihe, 95–116. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-26949-4_6.
Full textZhang, Limao, Yue Pan, Xianguo Wu, and Mirosław J. Skibniewski. "Information Fusion." In Lecture Notes in Civil Engineering, 95–124. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2842-9_5.
Full textLi, Jinxing, Bob Zhang, and David Zhang. "Information Fusion Based on Gaussian Process Latent Variable Model." In Information Fusion, 51–99. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8976-5_3.
Full textLi, Jinxing, Bob Zhang, and David Zhang. "Information Fusion Based on Deep Learning." In Information Fusion, 197–256. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8976-5_7.
Full textLi, Jinxing, Bob Zhang, and David Zhang. "Information Fusion Based on Score/Weight Classifier Fusion." In Information Fusion, 175–96. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8976-5_6.
Full textLi, Jinxing, Bob Zhang, and David Zhang. "Information Fusion Based on Sparse/Collaborative Representation." In Information Fusion, 13–50. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8976-5_2.
Full textLi, Jinxing, Bob Zhang, and David Zhang. "Information Fusion Based on Metric Learning." In Information Fusion, 131–74. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8976-5_5.
Full textConference papers on the topic "Information fusion"
Rogova, G. L., and E. Bosse. "Information quality in information fusion." In 2010 13th International Conference on Information Fusion (FUSION 2010). IEEE, 2010. http://dx.doi.org/10.1109/icif.2010.5711857.
Full textTamke, Martin, Morten Myrup Jensen, Jakob Beetz, Thomas Krijnen, and Dag Fjeld Edvardsen. "Building Information Deduced - State and potentials for Information query in Building Information Modelling." In eCAADe 2014: Fusion. eCAADe, 2014. http://dx.doi.org/10.52842/conf.ecaade.2014.2.375.
Full textAristov, Mikhail, Benjamin Noack, Uwe D. Hanebeck, and Jorn Muller-Quade. "Encrypted Multisensor Information Filtering." In 2018 International Conference on Information Fusion (FUSION). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455449.
Full text"Session Information." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190359.
Full textBarbu, C., Jing Peng, and G. Seetharaman. "Boosting information fusion." In 2010 13th International Conference on Information Fusion (FUSION 2010). IEEE, 2010. http://dx.doi.org/10.1109/icif.2010.5711976.
Full textHintz, Kenneth J., and Steven Darcy. "Cross-Domain Pseudo-Sensor Information Measure." In 2018 International Conference on Information Fusion (FUSION). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455354.
Full textKrenc, Ksawery. "Polymorphic Information Exchange Model for the purpose of Multi-level Fusion of Hard and Soft Information." In 2018 International Conference on Information Fusion (FUSION). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455324.
Full textHintz, Kenneth J., and Steven Darcy. "Valued situation information in IBSM." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009850.
Full textBlasch, Erik P., and David Braines. "Scalable Information Fusion Trust." In 2021 IEEE 24th International Conference on Information Fusion (FUSION). IEEE, 2021. http://dx.doi.org/10.23919/fusion49465.2021.9626986.
Full textDe Shon, Markus. "Information Security Analysis as Data Fusion." In 2019 22th International Conference on Information Fusion (FUSION). IEEE, 2019. http://dx.doi.org/10.23919/fusion43075.2019.9011237.
Full textReports on the topic "Information fusion"
Bray, O. H. Information integration for data fusion. Office of Scientific and Technical Information (OSTI), January 1997. http://dx.doi.org/10.2172/444047.
Full textCorkill, Daniel D. Collaborative Software for Information Fusion. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada437538.
Full textVeeravalli, Venugopal, Biao Chen, and Prakash Ishwar. Dynamic Information Collection and Fusion. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ad1003771.
Full textKnoblock, Craig A. InfoFuse: Interleaved Information Gathering and Reasoning for Information Fusion. Fort Belvoir, VA: Defense Technical Information Center, June 2010. http://dx.doi.org/10.21236/ada528556.
Full textKokar, Mieczyslaw M., Christopher J. Matheus, Kenneth Baclawski, Jerzy A. Letkowski, Michael Hinman, and John Salerno. Use Cases for Ontologies in Information Fusion. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada444552.
Full textYager, Ronald R. Information Fusion and Aggregation for Cooperative Systems. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada486686.
Full textYager, Ronald R. On Methods for Higher Order Information Fusion. Fort Belvoir, VA: Defense Technical Information Center, February 2005. http://dx.doi.org/10.21236/ada430888.
Full textThuraisingham, Bhavani. Secure Sensor Semantic Web and Information Fusion. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada610634.
Full textBERG, TIMOTHY M. Fisher Information: Its Flow, Fusion, and Coordination. Office of Scientific and Technical Information (OSTI), June 2002. http://dx.doi.org/10.2172/801006.
Full textZhang, Zhenliang, Edwin K. Chong, Ali Pezeshki, William Moran, and Stephen D. Howard. Information Fusion and Control in Hierarchical Systems. Fort Belvoir, VA: Defense Technical Information Center, May 2013. http://dx.doi.org/10.21236/ada584390.
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