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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1351-1360 订阅

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MULTI-sensor DATA fusion USING GEOMETRIC TRANSFORMATIONS FOR GAS TRANSMISSION PIPELINE INSPECTION

MULTI-SENSOR DATA FUSION USING GEOMETRIC TRANSFORMATIONS FOR...

引用

2008 IEEE Instrumentation and Measurement Technology conference (I2MTC 2008), vol.4

作者： Joseph A. Oagaro Shreekanth Mandayam Department of Electrical & Computer Engineering Rowan University Glassboro NJ USA

This paper presents a technique that can be used to fuse data from multiple sensors that are employed in nondestructive evaluation (NDE) applications, specifically for the in-line inspection of gas transmission pipelines. A radial basis function artificial neural network is used to perform geometric transformations on data obtained from multiple sources. The technique allows the user to define the redundant and complementary information present in the data sets. The efficacy of the algorithm is demonstrated using experimental images obtained from the NDE of a test specimen suite using magnetic flux leakage (MFL), ultrasonic (UT) and thermal imaging methods. The results presented in this paper indicate that neural network based geometric transformation algorithms show considerable promise in multi-sensor data fusion applications.

关键词： Image processing pattern recognition industrial monitoring inverse problems

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Signal-to-noise ratio for cross-sensor fusion approach

Signal-to-noise ratio for cross-sensor fusion approach

引用

conference on Multisensor, Multisource Information fusion

作者： Kozaitis, S. P. Ouendeno, M. Florida Inst Technol Dept Elect & Comp Engn 150 W Univ Blvd Melbourne FL 32901 USA

We determined the signal-to-noise ratios for an image fusion approach that is suitable for application to systems with disparate sensors. We found there was reconstruction error present when the forward and reverse tr... 详细信息

ISBN: (纸本)9780819466938

关键词： image fusion image transform signal-to-noise ratio wavelet wavelet transform

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Real-time EO/IR sensor fusion on a portable computer and head mounted display

Real-time EO/IR sensor fusion on a portable computer and hea...

引用

conference on Multisensor, Multisource Information fusion

作者： Yue, Zhanfeng Topiwala, Pankaj LLC FastVDO 7150 Riverwood Dr Columbia MD 21046 USA

ISBN: (纸本)9780819466938

Multi-sensor platforms are widely used in surveillance video systems for both military and civilian applications. The complimentary nature of different types of sensors (e.g. EO and IR sensors) makes it possible to observe the scene under almost any condition (day/night/fog/smoke). In this paper, we propose an innovative EO/IR sensor registration and fusion algorithm which runs real-time on a portable computing unit with head-mounted display. The EO/IR sensor suite is mounted on a helmet for a dismounted soldier and the fused scene is shown in the goggle display upon the processing on a portable computing unit. The linear homography transformation between images from the two sensors is pre-computed for the mid-to-far scene, which reduces the computational cost for the online calibration of the sensors. The system is implemented in a highly optimized C++ code, with MMX/SSE, and performing a real-time registration. The experimental results on real captured video show the system works very well both in speed and in performance.

关键词： real-time sensor fusion homography

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Multi-sensor detection and fusion technique

Multi-sensor detection and fusion technique

引用

conference on Multisensor, Multisource Information fusion

作者： Bhargave, Ashish Arnbrose, Barry Lin, Freddie Kazantzidis, Manthos Broadata Commun Inc Torrance CA USA

ISBN: (纸本)9780819466938

A multi-sensor detection and fusion technology is described in this paper. The system consist of inputs from three sensors, Infra Red, Doppler Motion, and Stereo Video. This choice of sensors is designed to give high reliability, infra red and Doppler to provide detection ability at night, stereo video has the ability to analyze depth and range information. The combination of these sensors has the ability to provide high probability of detection and very low false alarm rate. The technique consists of three processing parts corresponding to each sensor data, and a fusion module, which makes the final decision based on the inputs from the three parts. The signal processing and detection algorithms process the inputs from each sensors and provides a specific information to the fusion module. fusion module is based on bayes belief propagation theory. It takes the processed inputs from all the sensor modules and provides a final decision on the presence and absense of objects, as well as their reliability based on the iterative belief propagation algorithm operating on decision graphs. A prototype system was built using the technique to study the feasibility of intrusion detection for NASA's launch danger zone protection. The system verified the potential of the proposed algorithms and proved the feasibility of high probability of detection and low false alarm rates compared to many existing techniques.

关键词： sensors Video Detection and tracking algorithms Image processing Doppler effect Prototyping Signal processing Infrared sensors Surveillance Video surveillance

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Multi sensor fusion of images for target identification

Multi sensor fusion of images for target identification

引用

conference on Multisensor, Multisource Information fusion

作者： Muthukumar, A. Anitha, G. Shanmugam, J. Madras Inst Technol Dept Elect Engn Div Avion Madras Tamil Nadu India

ISBN: (纸本)9780819466938

Multisensor data fusion is an emerging technology applied to defense and non-defense applications. In this paper, a image fusion algorithm using different texture parameters is proposed to identify long-range targets. The method uses a semi-supervised approach for detecting single target from the input images. The procedure consists of three steps: Feature extraction, Feature level fusion and sensor level fusion. In this study, two methods of texture feature extraction using co-occurrence matrix and run-length matrix are considered. Texture parameters are calculated at each pixel of the selected training image, and target non-target pixels identified manually. Some of the texture features calculated at the target position differ from those in the background. Discriminant analysis is used to perform feature level fusion on the training image, which classify target and non-target pixels. By applying the discriminant function to the feature space of textural parameters, a new image is created. The maxima of this image correspond to target point. The same discriminant function can be applied to the other images for detecting the trained targets regions. sensor level fusion combines images obtained from feature level fusion of visual and IR images. The method was first tested with synthetically generated images and then with real images. Results are obtained using both co-occurrence and run-length method of texture feature extraction for target identification.

关键词： image fusion texture features discriminant analysis

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Proceedings of SPIE - Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2007

Proceedings of SPIE - Multisensor, Multisource Information F...

引用

Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2007

ISBN: (纸本)081946693X

The proceedings contain 27 papers. The topics discussed include: a novel method to evaluate the performance of pan-sharpening algorithms;signal-to-noise ratio for cross-sensor fusion approach;fusion and kernel type selection in adaptive image retrieval;merging infrared and color visible images with a contrast enhanced fusion method;multi-sensor detection and fusion technique;novel classification fusion techniques for military target classification;a full-scale prototype multisensor system for fire detection and situational awareness;fusion of disparate information sources in a hybrid decision-support architecture;selection of fusion operations using rank-score diversity for robot mapping and localization;maximum likelihood ensemble filter applied to multisensory systems;assessing the value of information in a fuzzy cognitive map;and multisensor fusion of images for target identification.

关键词： Information fusion

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Maximum likelihood ensemble filter applied to multisensor systems

Maximum likelihood ensemble filter applied to multisensor sy...

引用

conference on Multisensor, Multisource Information fusion

作者： Albayrak, Arif R. Zupanski, Milija Zupanski, Dusanka Colorado State Univ CIRA 1375 Campus Delivery Ft Collins CO 80523 USA

ISBN: (纸本)9780819466938

Maximum Likelihood Ensemble Filter (MLEF) is an alternative deterministic ensemble based filter technique that optimizes a non-linear cost function along with a Maximum Likelihood approach. In addition to the common use of ensembles for calculating error covariance, the ensembles in MLEF are exploited to efficiently calculate Hessian preconditioning and the gradient of the cost function. This study is divided into two segments. The first part presents a one sensor approach, were MLEF is compared to different filters using Lorenz 63 system. These filters are: Extended Kalman Filter, Ensemble Kalman Filter. The second part develops a multi-sensor system. Here we study a moving particle on an orbit obtained from the same Lorenz system. We analyze the information content of MLEF's ensemble subspace for each sensor and consider the effects of different number of ensembles on the fusion process. In practice, when using ensemble based filtering techniques, a large ensemble size is required to obtain the best results. In this study we show that MLEF can obtain similar results using a smaller ensemble size by utilizing an information matrix, where essential characteristics are captured. This is a vital consideration when working with multi-sensor data fusion systems.

关键词： ensemble filter maximum likelihood information content data fusion

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Operating condition modeling for ATR fusion assessment

Operating condition modeling for ATR fusion assessment

引用

conference on Multisensor, Multisource Information fusion

作者： Kahler, Bart Blasch, Erik Goodwon, Lloyd Gen Dynam Corp Dayton OH 45431 USA AFRL SNA Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9780819466938

Real world Operating Conditions (OCs) influence sensor data that in turn affects the performance of target detection and identification systems utilizing the collected information. The impact of operating conditions on collected data is widely accepted, but not fully characterized. OCs that affect data depend on sensor wavelength and associated scenario phenomenology, and can vary significantly between electro-optical (EO), infrared (IR), and radar sensors. This paper will discuss what operating conditions might be modeled for each sensor type and how they could affect automatic target recognition (ATR) systems designed to exploit their respective sensory data. The OCs are broken out into four categories;sensor, environment, target, and ATR algorithm training. These main categories will further contain subcategories with varying levels of influence. The purpose of this work is to develop an OC distribution model for the "real world" that can be used to realistically represent the performance of multiple ATR systems, and ultimately the decision made from the fused ATR results. An accurate OC model will greatly enhance the performance assessment of ATR and fusion systems by affording Bayesian conditioning in fusion performance analysis and aiding in the sensitivity analysis of fusion performance over different operational conditions. Accurate OC models will also be useful in the fusion algorithm operation.

关键词： EO IR SAR radar ATR operating conditions OCs fusion detection identification

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A full-scale prototype multisensor system for fire detection and situational awareness

A full-scale prototype multisensor system for fire detection...

引用

conference on Multisensor, Multisource Information fusion

作者： Minor, Christian P. Johnson, Kevin J. Rose-Pehrsson, Susan L. Owrutsky, Jeffrey C. Wales, Stephen C. Steinhurst, Daniel A. Gottuk, Daniel T. Novo Res Inc 1900 Eikin StSuite 230 Alexandria VA 22308 USA US Naval Res Lab Washington DC 20375 USA Hughes Associates Inc Baltimore MD 21227 USA

ISBN: (纸本)9780819466938

A data fusion-based, multisensory detection system, called "Volume sensor", was developed under the Advanced Damage Countermeasures (ADC) portion of the US Navy's Future Naval Capabilities program (FNC) to meet reduced manning goals. A diverse group of sensing modalities was chosen to provide an automated damage control monitoring capability that could be constructed at a relatively low cost and also easily integrated into existing ship infrastructure. Volume sensor employs an efficient, scalable, and adaptable design framework that can serve as a template for heterogeneous sensor network integration for situational awareness. In the development of Volume sensor, a number of challenges were addressed and met with solutions that are applicable to heterogeneous sensor networks of any type. These solutions include: 1) a uniform, but general format for encapsulating sensor data, 2) a communications protocol for the transfer of sensor data and command and control of networked sensor systems, 3) the development of event specific data fusion algorithms, and 4) the design and implementation of modular and scalable system architecture. In full-scale testing on a shipboard environment, two prototype Volume sensor systems demonstrated the capability to provide highly accurate and timely situational awareness regarding damage control events while simultaneously imparting a negligible footprint on the ship's 100 Mbps Ethernet network and maintaining smooth and reliable operation in a real-time fashion.

关键词： fire detection flood detection multisensor multicriteria data fusion multivariate analysis video sensor audio sensor spectral sensor sensor network

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Prospects for dynamic ISR tasking and interpretation based on standing orders to sensor networks

Prospects for dynamic ISR tasking and interpretation based o...

引用

conference on Multisensor, Multisource Information fusion

作者： Pantaleev, Aleksandar Josephson, John R. Ohio State Univ Artificial Intelligence Res Lab Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9780819466938

This research is intended to contribute to the development of automated and human-in-the-loop systems for higher level fusion to respond to the information requirements of command decision making. In tactical situations with short time constraints, the analysis of information requirements may take place in advance for certain classes of problems, and provided to commanders and their staff as part of the control and communications systems that come with sensor networks. In particular, it may be possible that certain standing orders can assume the role of Priority Intelligence Requirements. Standing orders to a sensor network are analogous to standing orders to Soldiers. Trained Soldiers presumably don't need to be told to report contact with hostiles, for example, or to report any sighting of civilians with weapons. Such standing orders define design goals and engineering requirements for sensor networks and their control and inference systems. Since such standing orders can be defined in advance for a class of situations, they minimize the need for situation-specific human analysis. Thus, standing orders should be able to drive automatic control of some network functions, automated fusion of sensor reports, and automated dissemination of fused information. We define example standing orders, and outline an algorithm for responding to one of them based on our experience in the field of multisensor fusion.

关键词： standing orders PIRs automated fusion sensor networks multisensor fusion

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