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

共 2100 条记录，以下是1541-1550 订阅

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Reduced data projection slice image fusion using principal component analysis

Reduced data projection slice image fusion using principal c...

引用

conference on Multisensor, Multisource Information fusion

作者： Riasati, VR Zhou, H Sci Applicat Int Corp San Diego CA 92121 USA

ISBN: (纸本)0819457981

In this paper we discuss the utilization of Principal Component Analysis, PCA, with projection slice synthetic discriminant function (PSDF) filters to reduce a data set that represents images from different sensor systems in order to extract relative information and features from the image set. The PCA helps to emphasize the differences in each of the training images in a given class. These differences are encoded into the PSDF filters. The PSDF filters provide a premise for data fusion by utilization of the projection-slice theorem. The PSDF is implemented with a few training images generated from the PCA, containing relevant information from all of the training images. The data in the principle components that are used to represent the entire data set can be emphasized by conditioning the eigen-values of the basis vectors used to corroborate important data packets in the entire data set. ne method of data fusion, and preferred data emphasis in conjunction with the PST is discussed and the fused images are presented.

关键词： sensor fusion projection slice theorem principal component analysis synthetic discriminant functions

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Information fusion and uncertainty management for biological multisensor systems

Information fusion and uncertainty management for biological...

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conference on Multisensor, Multisource Information fusion

作者： Braun, JJ Glina, Y Stein, DW Skomoroch, PN Fox, EB MIT Lincoln Lab Lexington MA 02420 USA

ISBN: (纸本)0819457981

This paper investigates methods of decision-making from uncertain and disparate data. The need for such methods arises in those sensing application areas in which multiple and diverse sensing modalities are available, but the information provided can be imprecise or only indirectly related to the effects to be discerned. Biological sensing for biodefense is an important instance of such applications. Information fusion in that context is the focus of a research program now underway at MIT Lincoln Laboratory. The paper outlines a multi-level, multi-classifier recognition architecture developed within this program, and discusses its components. Information source uncertainty is quantified and exploited for improving the quality of data that constitute the input to the classification processes. Several methods of sensor uncertainty exploitation at the feature-level are proposed and their efficacy is investigated. Other aspects of the program are discussed as well. While the primary focus of the paper is on biodefense, the applicability of concepts and techniques presented here extends to other multisensor fusion application domains.

关键词： information fusion multisensor data fusion machine learning biological sensors biodefense

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A performance metric for belief fusion techniques

A performance metric for belief fusion techniques

引用

conference on Multisensor, Multisource Information fusion

作者： Friesel, MA Lockheed Martin Maritime Syst & Sensors Command & Control Syst Engn Moorestown NJ 08057 USA

ISBN: (纸本)0819457981

In this paper it is illustrated how Bayes equations and frequency data may use as a measure of performance for belief fusion algorithms. A review of Bayes equations for single and multiple sources is provided. A simple performance measure is then calculated and applied to some belief fusion examples from the literature. Their performance measures are qualitatively similar, but the quantitative differences among these techniques appear to be arbitrary.

关键词： belief theory datafusion performance characterization Bayes Dempster-Shafer combat identification

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Image fusion with the hybrid evolutionary algorithm and response analysis

Image fusion with the hybrid evolutionary algorithm and resp...

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conference on Multisensor, Multisource Information fusion

作者： Maslov, IV CUNY Dept Comp Sci Grad Ctr New York NY 10016 USA

ISBN: (纸本)0819457981

Information fusion is a rapidly developing research area aimed at creating methods and tools capable of augmenting security and defense systems with the state-of-the-art computational power and intelligence. An important part of information fusion, image fusion serves as the basis for a fully automatic object and target recognition. Image fusion maps images of the same scene received from different sensors into a common reference system. Using sensors of different types gives rise to a problem of finding a set of invariant features that help overrun the imagery difference caused by the different types of sensors. The paper describes an image fusion method based on the combination of the hybrid evolutionary algorithm and image local response. The latter is defined as an image transform R(V) that maps an image into itself after a geometric transformation A(V) defined by a parameter vector V is applied to the image. The transform R(V) identifies the dynamic content of the image, i.e. the salient features that are most responsive to the geometric transformation A(V). Moreover, since R(V) maps the image into itself, the result of the mapping is largely invariant to the type of the sensor used to obtain the image. Image fusion is stated as the global optimization problem of finding a proper transformation A(V) that minimizes the difference between the images subject to fusion. Hybrid evolutionary algorithm can be applied to solving the problem. Since the search for the optimal parameter vector V is conducted in the response space rather than in the actual image space, the differences in the sensor types can be significantly alleviated.

关键词： information fusion image fusion target recognition optimization hybrid evolutionary algorithm response analysis

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Distributed target tracking for nonlinear systems: Application to bearings-only tracking

Distributed target tracking for nonlinear systems: Applicati...

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2005 8th International conference on Information fusion, fusion

作者： Bréhard, Thomas Le Cadre, Jean-Pierre IRISA/CNRS IRISA Campus de Beaulieu 35042 Rennes Cedex France

ISBN: (纸本)0780392868

Nonlinear distributed target tracking for a single target is addressed in this paper. The problem consists in deriving fusion rules for local full/partial target state estimates processed by a number of sensors. We investigate the general ways for the nonlinear fusion rules with/without feedback implementation via particle filtering algorithms. In particular, we focuse on practical application of these ideas for specific multi-sensor architectures including low/high bandwidth. Then, these new approaches are applied to the distributed bearings-only tracking problem. © 2005 IEEE.

关键词： sensor data fusion

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A Bayesian approach to extended object tracking and tracking of loosely structured target groups

A Bayesian approach to extended object tracking and tracking...

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7th International conference on Information fusion (fusion)

作者： Koch, W Saul, R FGAN FKIE D-53343 Wachtberg Germany

ISBN: (纸本)0780392868

In algorithms for tracking and sensor datafusion the targets to be tracked are usually considered as point source objects;i.e. compared to the sensor resolution their extension is neglected Due to the increasing resolution capabilities of modem sensors, however this assumption is often not valid: Different scattering centers of an object can cause distinct detections when passing the signal processing chain. Examples of extended targets are found in short-range applications (littoral surveillance, autonomous weapons, or robotics). As an extended target also a collectively moving, loosely structured group can be considered. This point of view is all the more appropriate, the smaller the mutual distances between the individual targets are. Due to the resulting data association and resolution conflicts any attempt of tracking the individual objects is no longer reasonable. With simulated sensor data produced by a partly resolvable aircraft formation the addressed phenomena are illustrated and a Bayesian solution to the resulting tracking problem is proposed. Ellipsoidal object extensions are modeled by random matrices and treated as additional state variables to be estimated or 'tracked'. We expect that the resulting tracking algorithms are relevant also for tracking large, collectively moving target swarms.

关键词： target tracking extended targets group targets sensor resolution SPD random matrices matrix-variate analysis

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Real time implementation of image alignment and fusion

Real time implementation of image alignment and fusion

引用

conference on Multisensor, Multisource Information fusion

作者： Dwyer, D Smith, M Dale, J Heather, J Octec Ltd Western Ctr Bracknell RG12 1RW Berks England

ISBN: (纸本)0819457981

Many modern imaging and surveillance systems contain more than one sensor. For example, most modern airborne imaging pods contain at least visible and infrared sensors. Often these systems have a single display that is only capable of showing data from either camera, and thereby fail to exploit the benefit of having simultaneous multi-spectral data available to the user. It can be advantageous to capture all spectral features within each image and to display a fused result rather than single band imagery. This paper discusses the key processes necessary for an image fusion system and then describes how they were implemented in a real-time, rugged hardware system. The problems of temporal and spatial misalignment of the sensors and the process of electronic image warping must be solved before the image data is fused. The techniques used to align the two inputs to the fusion system are described and a summary is given of our research into automatic alignment techniques. The benefits of different image fusion schemes are discussed and those that were implemented are described. The paper concludes with a summary of the real-time implementation of image alignment and image fusion by Octec and Waterfall Solutions and the problems that have been encountered and overcome.

关键词： image alignment image fusion real time image processing

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Reduced memory multiscale fusion for combined topographic and bathymetric data

Reduced memory multiscale fusion for combined topographic an...

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conference on Multisensor, Multisource Information fusion

作者： Cheung, S Jhee, H Slatton, KC Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

ISBN: (纸本)0819457981

The multiscale Kalman smoother (MKS) is a globally optimal estimator for fusing remotely sensed data. The MKS algorithm can be readily parallelized because it operates on a Markov tree data structure. However, such an implementation requires a large amount of memory to store the parameters and estimates at each scale in the tree. This becomes particularly problematic in applications where the observations have very different resolutions and the finest scale data are sparse or aggregated. Such cases commonly arise when fusing data to capture both regional and local structure. In this work, we develop an efficient MKS algorithm and apply it to the fusion of topographic and bathymetric elevation data.

关键词： data fusion multiscale estimation topography and bathymetry

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Effect of algorithm fusion and aggregation methodologies on the performance of a corrosion detection algorithm

Effect of algorithm fusion and aggregation methodologies on ...

引用

conference on Multisensor, Multisource Information fusion

作者： Nelson, BN Slebodnick, P Groeninger, W Lemieux, EJ GEO CENTERS INC Newton Ctr MA 02459 USA

ISBN: (纸本)0819457981

Over the last several years, the Naval Research Laboratory has been developing corrosion detection algorithms for assessing coatings conditions in tank and voids on US Navy Ships. The corrosion detection algorithm is based on four independent algorithms;two edge detection algorithms, a color algorithm and a grayscale algorithm. Of these four algorithms, the color algorithm is the key algorithm and to some extent drives overall algorithm performance. The four independent algorithm results are fused with other features to first generate an image level assessment of coatings damage. The image level results are next aggregated across a tank or void image set to generate a single coatings damage value for the tank or void being inspected. The color algorithm, algorithm fusion methodology and aggregation algorithm components are key to the overall performance of the corrosion detection algorithm. This paper will describe modifications that have been made in these three algorithm components to increase the corrosion detection algorithmis overall operating range, to improve the algorithmis ability to assess low coatings damage and to improve the accuracy of coatings damage classification at both the individual image as well as at the whole tank level.

关键词： information fusion image processing algorithm fusion corrosion detection algorithms

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Mitigation of image impairments for multichannel remote sensing data fusion

Mitigation of image impairments for multichannel remote sens...

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conference on Visual Information Processing XIV

作者： Kurekin, A Dolia, AN Marshall, D Lukin, V Lever, K Univ Cardiff Wales Sch Comp Sci Sch Engn Cardiff CF24 0YF S Glam Wales

ISBN: (纸本)0819458023

Whilst for the majority of applications image quality depends on sensor accuracy and principles of image formation, in remote sensing systems information is also degraded by communication errors. To improve image fusion results in the presence of communication and sensor impairments we propose a two-stage approach. Preliminary nonlinear locally-adaptive image processing is applied at the first stage for mitigating impairments produced in image sensors and communication systems, and fusion algorithms are used at the second stage. The efficiency of the proposed algorithms is demonstrated for satellite remote sensing images and simulated data with similar characteristics and distortions. The influence of image distortions and the effectiveness of mitigation are estimated for an image fusion architecture for low-level image classification based on artificial neural networks. Experimental results are presented providing quantitative assessment of the proposed algorithms.

关键词： impairment mitigation remote sensing data fusion

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