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检索条件"任意字段=Multisensor. Multisource Information Fusion: Architectures, Algorithms, and Applications 2003"

共 284 条记录，以下是31-40 订阅

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A neurophysiological paradigm for data fusion in a multisource environment

A neurophysiological paradigm for data fusion in a multisour...

引用

Conference on multisensor. multisource information fusion - architectures, algorithms and applications 2003

作者： Fisher, PS Minton, DH Fisher, HP Winston Salem State Univ Dept Comp Sci Winston Salem NC 27110 USA

ISBN: (纸本)0819449598

A technique for representing data obtained from sensors, video streams. imagery, sound, text, etc. is presented. The technique is called Finite Inductive Sequences (FI) and is proposed as a means for eliminating data requiring storage where conventional mathematical models don't eliminate enough and statistical models eliminate too much. FI is a simple idea and is based upon a symbol push-out technique that allows the order (inductive base) of the model to be set to an a priori value for all derived rules. The rules are obtained from an exemplar data set, and are derived by a technique called factoring, and this results in a table of rules called a ruling. These rules can then be used in pattern recognition applications. These techniques are shown be example as well as a more formal setting, and lastly these rules and ruling are likened to the structure both present and absent in the cerebellum.

关键词： pattern recognition cerebellum data models finite induction data storage finite induction

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Discrimination of Chemical/Biological versus high explosive artillery rounds using acoustic and seismic data fusion

Discrimination of Chemical/Biological versus high explosive ...

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Conference on multisensor. multisource information fusion - architectures, algorithms and applications 2003

作者： Reiff, C Fargues, M Gonski, D US Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)0819449598

The threat of chemical and biological weapons is a serious problem and the ability to determine. if an incoming artillery round contains high explosives or a chemical/biological agent is valuable information to anyone on the battlefield. Early detection of a chemical or biological agent provides the soldier with more time to respond to the threat. information about the round type and location can be obtained from acoustic and seismic sensors and fused with information from radars, infrared and video cameras, and meteorological sensors to identify the round type quickly after detonation. This paper will describe the work with ground based acoustic and seismic sensors to discriminate between round types in a program sponsored-by the Soldier Biological and Chemical Command.

关键词： chemical biological artillery discrimination data fusion acoustic seismic

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architectures, algorithms, and applications Using Bayesian Networks

Architectures, Algorithms, and Applications Using Bayesian N...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Kingsbury, Todd Gen Dynam Adv Informat Syst San Diego CA 92108 USA

ISBN: (纸本)9780819486387

A Bayesian network is a tree structure where each branch represents a classification candidate. The leaves of the tree represent observable target features such as frequency or length. An optimized tree groups similar features together, e.g. frequency and pulse width, while collecting dissimilar or disparate information, e. g. spectral and kinematics, all within the same unifying structure. A vehicular track then is a subset of the a priori candidate library and contains only feasible branches. The algorithm for updating the confidence of each feasible candidate according to Bayes' rule is embedded in each track, as is the ability of a track to learn, apply a priori probability distributions, switch modes, switch among kinematics models, apply tracking history to classification and apply classification history to tracking, and support multisensor.correlation and sensor fusion.

关键词： Bayesian tracking classification sensor fusion

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multisensor.Data fusion with Disparate Data Sources

Multisensor Data Fusion with Disparate Data Sources

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Conference on multisensor. multisource information fusion - architectures, algorithms and applications 2010

作者： Minor, Christian P. Hammond, Mark H. Johnson, Kevin J. Rose-Pehrsson, Susan L. Nova Res Inc 1900 Elkin St 320 Alexandria VA 22308 USA US Navy Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)9780819481740

In many instances, sensing tasks are best addressed with multiple sensing modalities. However, fusion of the outputs of disparate sensor systems presents a significant challenge to forming a cohesive sensing system. A discussion of strategies for fusion of disparate sensor data is presented and illustrated with examples of real time and retrospective data fusion for multisensor.systems. The first example discussed is a real-time system for situational awareness and the detection of damage control events in ship compartments. The second example is a retrospective data fusion framework for a multisensor.system for the detection of buried unexploded ordnance at former bomb and target ranges.

关键词： data fusion multisensor.system fire detection UXO remediation

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Sensitivity of fusion performance to classifier model variations

Sensitivity of fusion performance to classifier model variat...

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Conference on multisensor. multisource information fusion - architectures, algorithms and applications 2003

作者： Goebel, K GE Global Res Niskayuna NY 12309 USA

ISBN: (纸本)0819449598

During design of classifier fusion tools, it is important to evaluate the performance of the fuser. In many cases, the output of the classifiers needs to be simulated to provide the range of fusion input that allows an evaluation throughout the design space. One fundamental question is how the output should be distributed, in particular for multi-class continuous output classifiers. Using the wrong distribution may lead to fusion tools that are either overly optimistic or otherwise distort the outcome. Either case may lead to a fuser that performs sub-optimal in practice. It is therefore imperative to establish the bounds of different classifier output distributions. In addition, one must take into account the design space that may be of considerable complexity. Exhaustively simulating the entire design space may be a lengthy undertaking. Therefore, the simulation has to be guided to populate the relevant areas of the design space. Finally, it is crucial to quantify the performance throughout the design of the fuser. This paper addresses these issues by introducing a simulator that allows the evaluation of different classifier distributions in combination with a design of experiment setup, and a built-in performance evaluation. We show results from an application of diagnostic decision fusion on aircraft engines.

关键词： classification diagnostics information fusion decision fusion simulation

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Multi-level image fusion

Multi-level image fusion

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Conference on multisensor. multisource information fusion - architectures, algorithms and applications 2003

作者： Petrovic, V Orasys MV YU-11077 Novi Beograd Serbia Yugoslavia

ISBN: (纸本)0819449598

Signal-level image fusion has in recent years established itself as a useful tool for dealing with vast amounts of image data obtained by disparate sensors. In many modem multisensor.systems, fusion algorithms significantly reduce the amount of raw data that needs to be presented or processed without loss of information content as well as provide an effective way of information integration. One of the most useful and widespread applications of signal-level image fusion is for display purposes. Fused images provide the observer with a more reliable and more complete representation of the scene than would be obtained through single sensor display configurations. In recent years, a plethora of algorithms that deal with the problem of fusion for display has been proposed. However, almost all are based on relatively basic processing techniques and do not consider information from higher levels of abstraction. As some recent studies have shown this does not always satisfy the complex demands of a human observer and a more subjectively meaningful approach is required. This paper presents a fusion framework based on the idea that subjectively relevant fusion could be achieved if information at higher levels of abstraction such as image edges and image segment boundaries are used to guide the basic signal-level fusion process. fusion of processed, higher level information to form a blueprint for fusion at signal level and fusion of information from multiple levels of extraction into a single fusion engine are both considered. When tested on two conventional signal-level fusion methodologies, such multi-level fusion structures eliminated undesirable effects such as fusion artefacts and loss of visually vital information that compromise their usefulness. Images produced by inclusion of multi-level information in the fusion process are clearer and of generally better quality than those obtained through conventional low-level fusion. This is verified through subjective evaluatio

关键词： image fusion signal-level fusion feature-level fusion multi-level fusion

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Adaptive IR and VIS image fusion

Adaptive IR and VIS image fusion

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Stoklasa, B. Rehacek, J. Hradil, Z. Palacky Univ Dept Opt Olomouc 77146 Czech Republic

ISBN: (纸本)9780819490858

Adaptive image fusion system based on neural network principle was realized. It works with digitalized video sequences of visible and infrared band sensors, and is able to produce the optimal fused image for a wide range of lighting conditions through an adaptive change of a fusion algorithm. The change is driven by a change in the measured statistic of the input images. The best algorithm for a particular input is found with the help of an objective measurement of the fusion process quality.

关键词： Image fusion Adaptive fusion Neural network

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multisource taxonomy-based classification using the transferable belief model

Multisource taxonomy-based classification using the transfer...

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Conference on multisensor. multisource information fusion - architectures, algorithms, and applications

作者： Farrell, William J., III Knapp, Andrew M. Lakota Tech Solut Inc Laurel MD USA

ISBN: (纸本)9780819490858

This paper addresses the problem of multi-source object classification in a context where objects of interest are part of a known taxonomy and the classification sources report at varying levels of specificity. This problem must consider several technical challenges: a) support fusion of heterogeneous classification inputs, b) provide a computationally scalable approach that accommodates taxonomy's with thousands of leaf nodes, and c) provide outputs that support tactical decision aides and are suitable inputs for subsequent fusion processes. This paper presents an approach that employs the Transferable Belief Model, Pignistic Transforms, and Bayesian fusion to address these challenges.

关键词： Evidence Theory Transferable Belief Model Pignistic Transform Bayesian fusion information Theory Classification Data Association

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Non-algorithmic information fusion

Non-algorithmic information fusion

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Conference on multisensor. multisource information fusion

作者： Brander, Jim Interact Engn Sydney NSW Australia

ISBN: (纸本)9780819471659

This paper considers the applicability of algorithms, constraint solving and active structure across the spectrum of complexity of information fusion applications. information fusion is recast as a cognitive application using dynamic structure building and constraint reasoning. The similarity between situation awareness and an undirected structure responding to change is highlighted. The efficiency and speed of operation of cognitive information fusion are touched on. A tsunami warning system provides an example which involves multiple threat and demonstrates the difference between segmented algorithms making decisions without context, and the active use of knowledge.

关键词： non-algorithmic undirected constraint reasoning active structure self-modifiable dynamically extensible cognitive core

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Adaptive multimodal biometric fusion algorithm using particle swarm

Adaptive multimodal biometric fusion algorithm using particl...

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Conference on multisensor. multisource information fusion - architectures, algorithms and applications 2003

作者： Veeramachaneni, K Osadciw, LA Varshney, PK Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

ISBN: (纸本)0819449598

This paper introduces a new algorithm called Adaptive Multimodal Biometric fusion Algorithm" (AMEBF), which is a combination of Bayesian decision fusion and particle swarm optimization. A Bayesian framework is implemented to fuse decisions received from multiple biometric sensors. The system's accuracy improves for a subset of decision fusion rules. The optimal rule is-a function of the error cost and a priori probability of an intruder This Bayesian framework formalizes the design of a system that can adoptively increase or reduce the security level. Particle swarm optimization searches the decision and sensor operating points (i.e. thresholds) space to achieve the desired security level: The optimization function aims to minimize the cost in a Bayesian decision fusion. The particle swarm optimization algorithm results in the fusion rule and the operating points of sensors at which the system can work This algorithm is important to systems designed with varying security needs and user access requirements. The adaptive algorithm is found to achieve desired security level and switch between different rules and sensor operating points for varying needs.

关键词： Bayesian decision fusion particle swarm optimization multimodal biometrics

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