Relevant bibliographies by topics / Information fusion

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Information fusion'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Information fusion"

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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.

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Take data driven method as theoretical basis, study multi-source information fusion technology. Using online and off-line data of the fusion system, does not rely on system's mathematical model, has avoided question about system modeling by mechanism. Uses principal component analysis method, rough set theory, Support Vector Machine(SVM) and so on, three method fusions and supplementary, through information processing and feature extraction to system's data-in, catches the most important information to lower dimensional space, realizes knowledge reduction. From data level, characteristic level, decision-making three levels realize information fusion. The example indicated that reduced computational complexity, reduced information loss in the fusion process, and enhanced the fusion accuracy.
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Yao, 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.

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Anderson, 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.

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e23521 Background: Ewing family tumors including Ewing sarcoma (ES) and desmoplastic small round cell tumor (DSRCT) are characterized by EWSR1 and ETS fusion partners including EWS-FLI1, EWS-ERG, EWS-WT1, and others. Since 2019 our Next Generation Sequencing (NGS) sarcoma panel (N = 338) identified both fusion partners and exons containing the EWS breakpoint in ES and DSRCT. Hence, it should be possible to learn more about the exact breakpoints of EWS fusion genes. The molecular diversity and functionality of these fusion transcripts, especially the exact sequence, which could be identical, similar, or unique, may have significant biological implications for diagnostics and treatment options. We have used in-frame analysis of EWS gene fusion breakpoints to identify corresponding polypeptides spanning the breakpoints of the most common EWS gene fusions to provide useful insights about ES and DSRCT. Methods: Pathology reports and EWS gene fusions were analyzed using the Cleveland Clinic NGS panel which is based on anchored multiplex polymerase chain reaction (PCR) enriched for 34 gene targets. The amplicons were subjected to massively parallel sequencing with 151x2 cycle pair-end reads. An informatics pipeline was used for read alignment (GRCh37 as reference genome), fusion identification, and annotation. The DNA sequence across fusion junction and translational amino acid sequences were extracted for comparison in a preliminary teaching set (37/338 EWS fusions). Results: EWS-FLI1 fusions at exon 7-7 (chr22:29683123-chr11:128675261) and exon 7-6 (chr22:29683123-chr11:128651853) were the most common fusion genes in ES (N = 24/32;75%). Other ES gene fusions included exons 10-6, 7-10, 9-8, 7-2 EWS-FEV, 7-8 EWS-ERG, 7-9 EWS-ERG, and 10-9 EWS-ERG. EWS-WT1 gene fusions (chr22:29683123-chr11:32414301) in DSRCT exon 7-7 occurred in 4 of 5 cases (80%). In frame analysis of the common gene fusions in 75% of ES and DSRCT (total n = 37) yielded identical corresponding polypeptides that span the breakpoint (table). Conclusions: Analysis of ES and DSRCT fusion genes provides evidence that cancer-specific fusion genes are associated with common identical breakpoints and corresponding in-frame polypeptides. Our results show that the identification of in-frame polypeptides from the fusion gene sequencing can identify potential nested sets of cancer-specific mRNAs and polypeptides. This information may become relevant for diagnostic and therapeutic targets future vaccines and diagnostics against ES and DSRCT as well as other cancers characterized by fusion genes or frameshift mutations. [Table: see text]
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Popovich, 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.

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Chiu, 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.

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Abstract Summary Presence or absence of gene fusions is one of the most important diagnostic markers in many cancer types. Consequently, fusion detection methods using various genomics data types, such as RNA sequencing (RNA-seq) are valuable tools for research and clinical applications. While information-rich RNA-seq data have proven to be instrumental in discovery of a number of hallmark fusion events, bioinformatics tools to detect fusions still have room for improvement. Here, we present Fusion-Bloom, a fusion detection method that leverages recent developments in de novo transcriptome assembly and assembly-based structural variant calling technologies (RNA-Bloom and PAVFinder, respectively). We benchmarked Fusion-Bloom against the performance of five other state-of-the-art fusion detection tools using multiple datasets. Overall, we observed Fusion-Bloom to display a good balance between detection sensitivity and specificity. We expect the tool to find applications in translational research and clinical genomics pipelines. Availability and implementation Fusion-Bloom is implemented as a UNIX Make utility, available at https://github.com/bcgsc/pavfinder and released under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/. Supplementary information Supplementary data are available at Bioinformatics online.
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Ross, 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.

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Das, 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.

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Snidaro, 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.

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Willett, 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.

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Xue, 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.

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A distributed information fusion structure based on data fusion tree is built to realize precise localization and efficient navigation for the mobile robot. The multi-class, multi-level information from robot and environment is fused using different algorithms in different levels, and make the robot have a deeper understanding to the whole environment. Experiments demonstrate that the new model proposed in the paper can improve the positioning precision of robot greatly, and the search efficiency and success rate are also better than traditional mode.
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Dissertations / Theses on the topic "Information fusion"

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Xu, Philippe. "Information fusion for scene understanding." Thesis, Compiègne, 2014. http://www.theses.fr/2014COMP2153/document.

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La compréhension d'image est un problème majeur de la robotique moderne, la vision par ordinateur et l'apprentissage automatique. En particulier, dans le cas des systèmes avancés d'aide à la conduite, la compréhension de scènes routières est très importante. Afin de pouvoir reconnaître le grand nombre d’objets pouvant être présents dans la scène, plusieurs capteurs et algorithmes de classification doivent être utilisés. Afin de pouvoir profiter au mieux des méthodes existantes, nous traitons le problème de la compréhension de scènes comme un problème de fusion d'informations. La combinaison d'une grande variété de modules de détection, qui peuvent traiter des classes d'objets différentes et utiliser des représentations distinctes, est faites au niveau d'une image. Nous considérons la compréhension d'image à deux niveaux : la détection d'objets et la segmentation sémantique. La théorie des fonctions de croyance est utilisée afin de modéliser et combiner les sorties de ces modules de détection. Nous mettons l'accent sur la nécessité d'avoir un cadre de fusion suffisamment flexible afin de pouvoir inclure facilement de nouvelles classes d'objets, de nouveaux capteurs et de nouveaux algorithmes de détection d'objets. Dans cette thèse, nous proposons une méthode générale permettant de transformer les sorties d’algorithmes d'apprentissage automatique en fonctions de croyance. Nous étudions, ensuite, la combinaison de détecteurs de piétons en utilisant les données Caltech Pedestrian Detection Benchmark. Enfin, les données du KITTI Vision Benchmark Suite sont utilisées pour valider notre approche dans le cadre d'une fusion multimodale d'informations pour de la segmentation sémantique
Image 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
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Kaupp, Tobias. "Probabilistic Human-Robot Information Fusion." Thesis, The University of Sydney, 2008. http://hdl.handle.net/2123/2554.

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This 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.
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Kaupp, Tobias. "Probabilistic Human-Robot Information Fusion." University of Sydney, 2008. http://hdl.handle.net/2123/2554.

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PhD
This 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.
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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.

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Johansson, 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.

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By 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

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Nouranian, Saman. "Information fusion for prostate brachytherapy planning." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58305.

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Low-dose-rate prostate brachytherapy is a minimally invasive treatment approach for localized prostate cancer. It takes place in one session by permanent implantation of several small radio-active seeds inside and adjacent to the prostate. The current procedure at the majority of institutions requires planning of seed locations prior to implantation from transrectal ultrasound (TRUS) images acquired weeks in advance. The planning is based on a set of contours representing the clinical target volume (CTV). Seeds are manually placed with respect to a planning target volume (PTV), which is an anisotropic dilation of the CTV, followed by dosimetry analysis. The main objective of the plan is to meet clinical guidelines in terms of recommended dosimetry by covering the entire PTV with the placement of seeds. The current planning process is manual, hence highly subjective, and can potentially contribute to the rate and type of treatment related morbidity. The goal of this thesis is to reduce subjectivity in prostate brachytherapy planning. To this end, we developed and evaluated several frameworks to automate various components of the current prostate brachytherapy planning process. This involved development of techniques with which target volume labels can be automatically delineated from TRUS images. A seed arrangement planning approach was developed by distributing seeds with respect to priors and optimizing the arrangement according to the clinical guidelines. The design of the proposed frameworks involved the introduction and assessment of data fusion techniques that aim to extract joint information in retrospective clinical plans, containing the TRUS volume, the CTV, the PTV and the seed arrangement. We evaluated the proposed techniques using data obtained in a cohort of 590 brachytherapy treatment cases from the Vancouver Cancer Centre, and compare the automation results with the clinical gold-standards and previously delivered plans. Our results demonstrate that data fusion techniques have the potential to enable automatic planning of prostate brachytherapy.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Dalmas, Tiphaine. "Information fusion for automated question answering." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/27860.

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Until recently, research efforts in automated Question Answering (QA) have mainly focused on getting a good understanding of questions to retrieve correct answers. I focus on the analysis of the relationships between answer candidates as provided in open domain QA on multiple documents. I argue that such candidates have intrinsic properties, partly regardless of the question, and those properties can be exploited to provide better quality and more user-oriented answers in QA. Information fusion refers to the technique of merging pieces of information from different sources. While frequency has proved to be a significant characteristic of a correct answer, I evaluate the value of other relationships characterizing answer variability and redundancy. Partially inspired by recent developments in multi-document summarization, I redefine the concept of “answer” within an engineering approach to QA based on the Model-View-Controller (MVC) pattern of user interface design. An “answer model” is a directed graph in which nodes correspond to entities projected from extractions and edges convey relationships between such nodes. I describe shallow techniques to compare entities and enrich the model by discovering four broad categories of relationships between entities in the model: equivalence, inclusion, aggregation and alternative. Quantitatively, answer candidate modelling improves answer extraction accuracy. It also proves to be more robust to incorrect answer candidates than traditional techniques. Qualitatively, models provide meta-information encoded by relationships that allow shallow reasoning to help organize and generate the final output. Coupling this fusion-based reasoning with the MVC approach, I report experiments on mixed-media answering involving generation of illustrated summaries, and discuss the application of web-based answer modelling to improve non web QA tasks. Finally, I discuss issues related to the computation of answer models (candidate selection for fusion, relationship transitivity), and address the difficulty of assessing fusion-based answers with the current evolution methods in QA.
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Oreshkin, 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.

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This thesis addresses the problem of design and analysis of distributed in-network signal processing algorithms for effcient aggregation and fusion of information in wireless sensor networks. The distributed in-network signal processing algorithms alleviate a number of drawbacks of the centralized fusion approach. The single point of failure, complex routing protocols, uneven power consumption in sensor nodes, ineffcient wireless channel utilization, and poor scalability are among these drawbacks. These drawbacks of the centralized approach lead to reduced network lifetime, poor robustness to node failures, and reduced network capacity. The distributed algorithms alleviate these issues by using simple pairwise message exchange protocols and localized in-network processing. However, for such algorithms accuracy losses and/or time required to complete a particular fusion task may be significant. The design and analysis of fast and accurate distributed algorithms with guaranteed performance characteristics is thus important. In this thesis two specific problems associated with the analysis and design of such distributed algorithms are addressed.
For 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.
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Peacock, Andrew M. "Information fusion for improved motion estimation." Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/428.

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Motion Estimation is an important research field with many commercial applications including surveillance, navigation, robotics, and image compression. As a result, the field has received a great deal of attention and there exist a wide variety of Motion Estimation techniques which are often specialised for particular problems. The relative performance of these techniques, in terms of both accuracy and of computational requirements, is often found to be data dependent, and no single technique is known to outperform all others for all applications under all conditions. Information Fusion strategies seek to combine the results of different classifiers or sensors to give results of a better quality for a given problem than can be achieved by any single technique alone. Information Fusion has been shown to be of benefit to a number of applications including remote sensing, personal identity recognition, target detection, forecasting, and medical diagnosis. This thesis proposes and demonstrates that Information Fusion strategies may also be applied to combine the results of different Motion Estimation techniques in order to give more robust, more accurate and more timely motion estimates than are provided by any of the individual techniques alone. Information Fusion strategies for combining motion estimates are investigated and developed. Their usefulness is first demonstrated by combining scalar motion estimates of the frequency of rotation of spinning biological cells. Then the strategies are used to combine the results from three popular 2D Motion Estimation techniques, chosen to be representative of the main approaches in the field. Results are presented, from both real and synthetic test image sequences, which illustrate the potential benefits of Information Fusion to Motion Estimation applications. There is often a trade-off between accuracy of Motion Estimation techniques and their computational requirements. An architecture for Information Fusion that allows faster, less accurate techniques to be effectively combined with slower, more accurate techniques is described. This thesis describes a number of novel techniques for both Information Fusion and Motion Estimation which have potential scope beyond that examined here. The investigations presented in this thesis have also been reported in a number of workshop, conference and journal papers, which are listed at the end of the document.
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Cavanaugh, Andrew F. "Bayesian Information Fusion for Precision Indoor Location." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/157.

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This thesis documents work which is part of the ongoing effort by the Worcester Polytechnic Institute (WPI) Precision Personnel Locator (PPL) project, to track and locate first responders in urban/indoor settings. Specifically, the project intends to produce a system which can accurately determine the floor that a person is on, as well as where on the floor that person is, with sub-meter accuracy. The system must be portable, rugged, fast to set up, and require no pre-installed infrastructure. Several recent advances have enabled us to get closer to meeting these goals: The development of Transactional Array Reconciliation Tomography(TART) algorithm, and corresponding locator hardware, as well as the integration of barometric sensors, and a new antenna deployment scheme. To fully utilize these new capabilities, a Bayesian Fusion algorithm has been designed. The goal of this thesis is to present the necessary methods for incorporating diverse sources of information, in a constructive manner, to improve the performance of the PPL system. While the conceptual methods presented within are meant to be general, the experimental results will focus on the fusion of barometric height estimates and RF data. These information sources will be processed with our existing Singular Value Array Reconciliation Tomography (σART), and the new TART algorithm, using a Bayesian Fusion algorithm to more accurately estimate indoor locations.
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Books on the topic "Information fusion"

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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.

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Popovich, 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.

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Popovich, 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.

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Popovich, 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.

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M, Jordan John, ed. Human-centered information fusion. Boston: Artech House, 2010.

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Bouchon-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.

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Snidaro, 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.

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1948-, Bouchon-Meunier B., Yager Ronald R. 1941-, and Zadeh Lotfi Asker, eds. Information, uncertainty, and fusion. Boston: Kluwer Academic Publishers, 2000.

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Bouchon-Meunier, Bernadette. Information, Uncertainty and Fusion. Boston, MA: Springer US, 2000.

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Popovich, 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.

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Book chapters on the topic "Information fusion"

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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.

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Shekhar, 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.

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Steinhauer, 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.

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Nguyen, 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.

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Zhang, 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.

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Li, 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.

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Li, 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.

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Li, 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.

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Li, 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.

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Li, 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.

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Conference papers on the topic "Information fusion"

1

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.

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Tamke, 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.

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Aristov, 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.

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"Session Information." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190359.

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Barbu, 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.

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Hintz, 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.

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Krenc, 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.

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Hintz, 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.

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Blasch, 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.

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De 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.

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Reports on the topic "Information fusion"

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Bray, O. H. Information integration for data fusion. Office of Scientific and Technical Information (OSTI), January 1997. http://dx.doi.org/10.2172/444047.

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Corkill, Daniel D. Collaborative Software for Information Fusion. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada437538.

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Veeravalli, 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.

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Knoblock, 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.

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Kokar, 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.

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Yager, 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.

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Yager, 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.

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Thuraisingham, Bhavani. Secure Sensor Semantic Web and Information Fusion. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada610634.

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BERG, 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.

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Zhang, 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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