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

作者： Pinsky, Ephi Siman-tov, Avihay Peles, David RAFAEL Ltd IL-31021 Haifa Israel

ISBN: (纸本)9780819490858

A novel multispectral video system that continuously optimizes both its spectral range channels and the exposure time of each channel autonomously, under dynamic scenes, varying from short range-clear scene to long range-poor visibility, is currently being developed. Transparency and contrast of high scattering medium of channels with spectral ranges in the near infrared is superior to the visible channels, particularly to the blue range. Longer wavelength spectral ranges that induce higher contrast are therefore favored. Images of 3 spectral channels are fused and displayed for (pseudo) color visualization, as an integrated high contrast video stream. In addition to the dynamic optimization of the spectral channels, optimal real-time exposure time is adjusted simultaneously and autonomously for each channel. A criterion of maximum average signal, derived dynamically from previous frames of the video stream is used (Patent Application International Publication Number: WO2009/093110 A2, 30.07.2009). This configuration enables dynamic compatibility with the optimal exposure time of a dynamically changing scene. It also maximizes the signal to noise ratio and compensates each channel for the specified value of daylight reflections and sensors response for each spectral range. A possible implementation is a color video camera based on 4 synchronized, highly responsive, CCD imaging detectors, attached to a 4CCD dichroic prism and combined with a common, color corrected, lens. Principal Components Analysis (PCA) technique is then applied for real time "dimensional collapse" in color space, in order to select and fuse, for clear color visualization, the 3 most significant principal channels out of at least 4 characterized by high contrast and rich details in the image data.

关键词： Multispectral adaptive spectral range PCA dimensional reduction visualization contrast fusion poor-visibility fog aerosols colors enhancement adaptive exposure time

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multisource taxonomy-based classification using the transferable belief model

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Farrell, William J. Knapp, Andrew M. Lakota Technical Solutions Inc. 14502 Greenview Drive Ste 340 Laurel MD United States

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. © 2012 SPIE.

关键词： Taxonomies

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Adaptive IR and VIS image fusion

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Stoklasa, B. Rehacek, J. Hradil, Z. Department of Optics Palacky University 17. listopadu 12 77146 Olomouc 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. © 2012 SPIE.

关键词： Image fusion

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Leveraging provenance to improve data fusion in sensor networks

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Dogan, Gulustan Seo, Eunsoo Brown, Theodore Abdelzaher, Tarek F. City University of New York United States University of Illinois at Urbana Champaign United States

ISBN: (纸本)9780819490858

Provenance is the information about the origin of the data inputs and the data manipulations to a obtain a final result. With the huge amount of information input and potential processing available in sensor networks, provenance is crucial for understanding the creation, manipulation and quality of data and processes. Thus maintaining provenance in a sensor network has substantial advantages. In our paper, we will concentrate on showing how provenance improves the outcome of a multi-modal sensor network with fusion. To make the ideas more concrete and to show what maintaining provenance provides, we will use a sensor network composed of binary proximity sensors and cameras to monitor intrusions as an example. Provenance provides improvements in many aspects such as sensing energy consumption, network lifetime, result accuracy, node failure rate. We will illustrate the improvements in accuracy of the position of the intruder in a target localization network by simulations. © 2012 SPIE.

关键词： Sensor data fusion

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Practical considerations in Bayesian fusion of point sensors

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Johnson, Kevin Minor, Christian Naval Research Laboratory Washington DC 20375 United States Nova Research Inc. Alexandria VA 22308 United States

ISBN: (纸本)9780819490858

Sensor data fusion is and has been a topic of considerable research, but rigorous and quantitative understanding of the benefits of fusing specific types of sensor data remains elusive. Often, sensor fusion is performed on an ad hoc basis with the assumption that overall detection capabilities will improve, only to discover later, after expensive and time consuming laboratory and/or field testing that little advantage was gained. The work presented here will discuss these issues with theoretical and practical considerations in the context of fusing chemical sensors with binary outputs. Results are given for the potential performance gains one could expect with such systems, as well as the practical difficulties involved in implementing an optimal Bayesian fusion strategy with realistic scenarios. Finally, a discussion of the biases that inaccurate statistical estimates introduce into the results and their consequences is presented. © 2012 SPIE.

关键词： Sensor data fusion

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Multirobot autonomous landmine detection using distributed multisensor.information aggregation

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Jumadinova, Janyl Dasgupta, Prithviraj University of Nebraska at Omaha Omaha NE United States

ISBN: (纸本)9780819490858

We consider the problem of distributed sensor information fusion by multiple autonomous robots within the context of landmine detection. We assume that different landmines can be composed of different types of material and robots are equipped with different types of sensors, while each robot has only one type of landmine detection sensor on it. We introduce a novel technique that uses a market-based information aggregation mechanism called a prediction market. Each robot is provided with a software agent that uses sensory input of the robot and performs calculations of the prediction market technique. The result of the agent's calculations is a 'belief' representing the confidence of the agent in identifying the object as a landmine. The beliefs from different robots are aggregated by the market mechanism and passed on to a decision maker agent. The decision maker agent uses this aggregate belief information about a potential landmine and makes decisions about which other robots should be deployed to its location, so that the landmine can be confirmed rapidly and accurately. Our experimental results show that, for identical data distributions and settings, using our prediction market-based information aggregation technique increases the accuracy of object classification favorably as compared to two other commonly used techniques. © 2012 SPIE.

关键词： Landmine detection

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Analysis of decision fusion algorithms in handling uncertainties for integrated health monitoring systems

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Zein-Sabatto, Saleh Mikhail, Maged Bodruzzaman, Mohammad DeSimio, Martin Derriso, Mark Behbahani, Alireza Tennessee State University College of Engineering Nashville TN 37209 United States Dayton OH United States Air Force Research Laboratory Wright-Patterson AFB OH 45433 United States

ISBN: (纸本)9780819490858

It has been widely accepted that data fusion and information fusion methods can improve the accuracy and robustness of decision-making in structural health monitoring systems. It is arguably true nonetheless, that decision-level is equally beneficial when applied to integrated health monitoring systems. Several decisions at low-levels of abstraction may be produced by different decision-makers; however, decision-level fusion is required at the final stage of the process to provide accurate assessment about the health of the monitored system as a whole. An example of such integrated systems with complex decision-making scenarios is the integrated health monitoring of aircraft. Thorough understanding of the characteristics of the decision-fusion methodologies is a crucial step for successful implementation of such decision-fusion systems. In this paper, we have presented the major information fusion methodologies reported in the literature, i.e., probabilistic, evidential, and artificial intelligent based methods. The theoretical basis and characteristics of these methodologies are explained and their performances are analyzed. Second, candidate methods from the above fusion methodologies, i.e., Bayesian, Dempster-Shafer, and fuzzy logic algorithms are selected and their applications are extended to decisions fusion. Finally, fusion algorithms are developed based on the selected fusion methods and their performance are tested on decisions generated from synthetic data and from experimental data. Also in this paper, a modeling methodology, i.e. cloud model, for generating synthetic decisions is presented and used. Using the cloud model, both types of uncertainties; randomness and fuzziness, involved in real decision-making are modeled. Synthetic decisions are generated with an unbiased process and varying interaction complexities among decisions to provide for fair performance comparison of the selected decision-fusion algorithms. For verification purposes, implementat

关键词： Monitoring

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Use of sonification in the detection of anomalous events

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Ballora, Mark Cole, Robert J. Kruesi, Heidi Greene, Herbert Mohanan, Ganesh Hall, David L. College of Information Sciences and Technology Penn State University University Park PA 16802 United States Raytheon Corp. State College PA 16803 United States

ISBN: (纸本)9780819490858

In this paper, we describe the construction of a soundtrack that fuses stock market data with information taken from tweets. This soundtrack, or auditory display, presents the numerical and text data in such a way that anomalous events may be readily detected, even by untrained listeners. The soundtrack generation is flexible, allowing an individual listener to create a unique audio mix from the available information sources. Properly constructed, the display exploits the auditory system's sensitivities to periodicities, to dynamic changes, and to patterns. This type of display could be valuable in environments that demand high levels of situational awareness based on multiple sources of incoming information. © 2012 SPIE.

关键词： Sound recording

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Cross layers decision making and fusion model in layered sensing systems

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Khoshnaw, Abdulqadir Zein-Sabatto, Saleh Malkani, Mohan College of Engineering Tennessee State University 3500 John A. Merritt Blvd Nashville TN 37209 United States

ISBN: (纸本)9780819490858

Layered sensing systems involve operation of several layers of sensing with different capabilities integrated into one whole system. The integrated layers of sensing must share information and local decisions across layers for better situation awareness. This research focused on the development of a model for decision making and fusion at the information level in layered sensing systems using the cloud model for uncertainty processing. In this research, the addition of a new processing level to the Joint Directors of Laboratories (JDL) processing model is proposed. The new processing level is called "information Assessment, fusion, and Control (IAFC)". Through this level, the different layers of a layered sensing system evaluate information about a given situation in terms of threat level and make a decision. The information assessment and control processing module were able to assess the threat level of a situation accurately and exchange assessments in order to determine the overall situation's threat level among all layers. The uncertain decisions were fused together to a unified decision using the cloud model of uncertainty processing methodology. Using this methodology, a cognitive element was added to the process of information assessment module leading to more accurate situation awareness. © 2012 SPIE.

关键词： Decision making

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Navigation of uncertain terrain by fusion of information from real and synthetic imagery

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Lyons, Damian M. Nirmal, Paramesh Paul Benjamin, D. Fordham University Robotics and Computer Vision Laboratory Bronx NY 10458 United States Pace University Department of Computer Science New York NY 10023 United States

ISBN: (纸本)9780819490858

We consider the scenario where an autonomous platform that is searching or traversing a building may observe unstable masonry or may need to travel over unstable rubble. A purely behaviour-based system may handle these challenges but produce behaviour that works against long-terms goals such as reaching a victim as quickly as possible. We extend our work on ADAPT, a cognitive robotics architecture that incorporates 3D simulation and image fusion, to allow the robot to predict the behaviour of physical phenomena, such as falling masonry, and take actions consonant with long-term goals. We experimentally evaluate a cognitive only and reactive only approach to traversing a building filled with various numbers of challenges and compare their performance. The reactive only approach succeeds only 38% of the time, while the cognitive only approach succeeds 100% of the time. While the cognitive only approach produces very impressive behaviour, our results indicate how much better the combination of cognitive and behaviour-based can be. © 2012 SPIE.

关键词： Robots

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