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检索条件"任意字段=Conference on Sensor Fusion - Architectures, Algorithms, and Applications III"

共 369 条记录，以下是341-350 订阅

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Optimal features in feature out (FEI-FEO) fusion for decisions in multisensor environments

Optimal features in feature out (FEI-FEO) fusion for decisio...

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conference on sensor fusion - architectures, algorithms, and applications II

作者： Belur, SV Dasarathy, BV Comp Sci Corp Dept Appl Res Syst Sci Div Lanham MD 20706 USA

ISBN: (纸本)0819428256

The study presents a formal methodology to the problem of feature level fusion, that had been previously addressed in the literature mostly in an ad hoc manner on a case by case basis only. The input set of features extracted from multiple sensors (data sources) are optimally fused to derive a synthetic feature so as to enhance the effective discrimination potential among the defined set of decision classes. This features in - feature out (FEI-FEO)' fusion process, unlike most other fusion schemes reported in the literature, is designed through a formal learning phase in which an optimal mapping from the multisensor derived feature space to a single unified feature is developed. This learning, accomplished through a new composite random and deterministic search based optimization teal, defines the transformation for the FEI-FEO process. This transformation is applied to the multi-sensor generated feature sets in the operational phase to derive the fused feature values corresponding to the objects under observation. The corresponding classification decisions are made on the basis of relative closeness of these feature values to the different class mean values in the transformed single dimensional feature space. The new methodology has been implemented in MATLAB which, being a vector/matrix oriented language, is an ideal candidate for solving problems in patter recognition and learning. The method is applied to well-known data sets available on the web for testing pattern recognition algorithms to assess its effectiveness relative to the traditional classification methods from both conceptual as well as computational view points.

关键词： feature level fusion feature optimization features in - feature out fusion feature synthesis

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Adaptive local fusion systems for novelty detection and diagnostics in condition monitoring

Adaptive local fusion systems for novelty detection and diag...

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conference on sensor fusion - architectures, algorithms, and applications II

作者： Taylor, O MacIntyre, J Univ Sunderland Sch Comp & Informat Sci Ctr Adapt Syst Sunderland SR6 0DD England

ISBN: (纸本)0819428256

This paper describes the application of Kohonen Self Organising Maps in a dynamic machine condition monitoring application which learns fault conditions over time. The authors describe the implementation of a novelty detection and adaptive diagnostic system which forms a modular component of a larger on-line condition monitoring system. NEURAL-MAINE, a project under the European Union's EUREKA programme, aims to advance on-line condition monitoring applications by the use of neural networks, data fusion and multiple sensor technology. NEURAL-MAINE aims to implement a system on two levels: at the first,, Local fusion Systems (LFS) are used to model individual machine components, such as high pressure turbines;at the second, a larger 'Overseer' system sits above the LFS and takes in their input, as well as plant operating parameters, in order to give a global view of the condition of the plant. This paper describes the implementation and testing of the two major components of the Local fusion System, namely the novelty detection and the adaptive diagnostic systems. Novelty detection works by using a Kohonen-based neural network(1) to learn the normal operating state of the component that is being modeled, with new values being passed into the network to see if it is similar to the learned normal states of operation. If the new fused sensor values are unlike the values which the Kohonen neural network represents, this is nagged as novel, and the values are passed to the adaptive diagnostic system. The adaptive diagnostic system takes as its input the values that the novelty detection system identified as novel and passes these into the Kohonen-based diagnostic networks. If the diagnostic networks recognize the values then a local diagnosis is given and passed to the Overseer system. If the diagnostic networks do not recognize the values, then these patterns are dynamically learned as the new fault condition occurs, and the diagnostic networks are updated. This system a

关键词： neural networks data fusion novelty detection adaptive diagnostic system

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Estimation of subpixel-resolution motion fields from segmented image sequences

Estimation of subpixel-resolution motion fields from segment...

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conference on sensor fusion - architectures, algorithms, and applications II

作者： Schultz, RR Stevenson, RL Univ N Dakota Dept Elect Engn Grand Forks ND 58202 USA

ISBN: (纸本)0819428256

Super-resolution enhancement algorithms are used to estimate a high-resolution video still (HRVS) from several low-resolution frames, provided that objects within the image sequence move with subpixel increments. However, estimating an accurate subpixel-resolution motion field between two low-resolution, noisy video frames has proven to be a formidable challenge. Up-sampling the image sequence frames followed by the application of block matching, optical flow estimation, or Bayesian motion estimation results in relatively poor subpixel-resolution motion fields, and consequently inaccurate regions within the super-resolution enhanced video still. This is particularly true for large interpolation factors (greater than or equal to 4). To improve the quality of the subpixel motion fields and the corresponding HRVS, motion can be estimated for each object within a segmented image sequence. First, a reference video frame is segmented into its constituent objects, and a mask is generated for each object which describes its spatial location. As described previously, subpixel-resolution motion estimation is then conducted by video frame up-sampling followed by the application of a motion estimation algorithm. Finally, the motion vectors are averaged over the region of each mask by applying an a-trimmed mean filter to the horizontal and vertical components separately. Since each object moves as a single entity, averaging eliminates many of the motion estimation errors and results in much more consistent subpixel motion fields. A substantial improvement is also visible within particular regions of the HRVS estimates. Subpixel-resolution motion fields and HRVS estimates are computed for interpolation factors of 2, 4, 8, and 16, to examine the benefits of object segmentation and motion field averaging.

关键词： interpolation motion estimation segmentation sensor fusion super-resolution enhancement

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ARTMAP-FTR: A neural network for fusion target recognition, with application to sonar classification

ARTMAP-FTR: A neural network for fusion target recognition, ...

引用

SPIE conference on Detection and Remediation Technologies for Mines and Minelike Targets iii

作者： Carpenter, GA Streilein, WW Boston Univ Dept Cognit & Neural Syst Boston MA 02215 USA

ISBN: (纸本)0819428418

ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.

关键词： sonar classification sensor fusion target recognition ARTMAP-FTR ART ARTMAP fuzzy ARTMAP adaptive resonance neural network

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Multiple sensor fusion for long range surveillance: Design and performance issues

Multiple sensor fusion for long range surveillance: Design a...

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conference on sensor fusion - architectures, algorithms, and applications

作者： Jones, G Raytheon Canada Ltd. (Canada)

ISBN: (纸本)081942482X

This paper provides a description and detailed review of a multiple hypothesis tracking system, which handles data from two radars, and a number of other sources. An efficient method for processing detections from two (time-offset) radars, and integrating them in the multiple hypothesis framework will be described. The implications of such a methodology on the tracking filter will also be discussed. The paper then explains the algorithm employed for fusion of Automatic Dependent Surveillance reports into the system, and concludes with a demonstration of some sample results.

关键词： multiple-hypothesis tracking sensor fusion

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Decision level data fusion using Bayesian inference

Decision level data fusion using Bayesian inference

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conference on sensor fusion - architectures, algorithms, and applications

作者： Parish, J TRW Inc. (United States)

ISBN: (纸本)081942482X

This paper describes a blackboard system for integrating observations from multiple sensors. Multiple sensors report observations to the blackboard system. The blackboard system correlates the observations to a set of active models, and the models are both temporally limited and also probabilistic. The design is object-oriented to allow for extensions that accommodate new models and sensors. An example application to a grid of sensors is presented

关键词： Bayesian inference blackboard systems data fusion

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Identity and attribute information fusion using evidential reasoning

Identity and attribute information fusion using evidential r...

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conference on sensor fusion - architectures, algorithms, and applications

作者： Bosse, E Simard, MA Defence Research Establishment Valcartier (Canada) Lockheed Martin Canada (Canada)

ISBN: (纸本)081942482X

The aim of this paper is to explore the problem of fusing identity and attribute information emanating from different sources, and to offer the decision maker a quantitative analysis based on statistical methodology that can enhance his/her decision making process regarding the identity of detected objects. Two identity information fusion architectures are discussed here. The first is concerned with the fusion of identity declarations where the sources are expected to provide only useful and complete results such as an identity declaration. The second is concerned with the fusion of attribute information using a modified version of the Dempster-Shafer evidential combination algorithm.

关键词： sensors Information fusion Data fusion Data processing Radar Electronic support measures Telecommunications Signal processing Databases Probability theory

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E-O and SAR image analysis and registration applications

E-O and SAR image analysis and registration applications

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conference on sensor fusion - architectures, algorithms, and applications

作者： Lampropoulos, GA Halet, A Rey, M Boulter, JF A.U.G. Signals Ltd. (Canada) Defence Research Establishment Ottawa (Canada) Defence Research Establishment Valcartier (Canada)

ISBN: (纸本)081942482X

in this paper we present an approach for association and registration of multi-sensor images. With no loss of generality, we consider herein the association and registration of SAR and Visible satellite images. Although the current approach as presented is a semi-automatic registration technique, there is sufficient promise for the development of a fully automatic multi-sensor multi-spectra registration approach. Some experimental results are presented.

关键词： Image segmentation Image registration Synthetic aperture radar Roads Image restoration Image analysis Image processing algorithms and systems sensors Satellite imaging Satellites

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fusion of data from spatially separated sensors using Riemannian manifolds

Fusion of data from spatially separated sensors using Rieman...

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conference on sensor fusion - architectures, algorithms, and applications

作者： Cain, MP Booz*Allen & Hamilton Inc. (United States)

ISBN: (纸本)081942482X

In this paper, an approach for representing target classes in feature space using Riemannian manifolds is explored. In a formal application of the approach, it is required that several basic assumptions used in the development of differential and Riemannian geometry are satisfied. These assumptions relate to the concepts of allowable parametric representations and allowable coordinate transformations. Developing target class representations which satisfy these assumptions has a direct consequence on the selection of a suitable feature set. Having found a suitable feature set, the approach results in a natural coordinate system in which to calculate distance metrics used in classification algorithms. In this paper, the approach is applied to a situation where an active sensor and a passive sensor are spatially separated and are simultaneously collecting data on a set of targets. It is shown that the use of the natural coordinate system offered by this approach leads to a straightforward and mathematically rigorous method for fusing the sensor data at the feature level.

关键词： Riemann data fusion feature level fusion classification

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Decision fusion using channels with communication constraints

Decision fusion using channels with communication constraint...

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conference on sensor fusion - architectures, algorithms, and applications

作者： Yu, CT Varshney, PK Nan-Tai Institute of Technology (Taiwan) Syracuse Univ. (United States)

ISBN: (纸本)081942482X

A decentralized detection system usually contains a number of remotely located local sensors that observe a common phenomenon and a data fusion center that makes a final decision. The local sensors are linked to the data fusion center by transmission channels. In this paper, some aspects of decision fusion problems with communication constraints are considered. Two interesting issues, namely, bandwidth allocation among the channels linking local sensors to the fusion center, and the trade-off between the number of sensors and the number of likelihood-ratio quantization levels at local sensors. are studied. Examples are presented for illustration.

关键词： bandwidth allocation sampling quantization data fusion distributed detection

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