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检索条件"任意字段=Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006"

共 284 条记录，以下是21-30 订阅

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Enhancing thermal video using a public database of images

Enhancing thermal video using a public database of images

引用

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

作者： Qadir, H. Kozaitis, S. P. Ali, E. A. Florida Inst Technol Dept Elect & Comp Engn Melbourne FL 32901 USA

ISBN: (纸本)9781628410587

We presented a system to display nightime imagery with natural colors using a public database of images. We initially combined two spectral bands of images, thermal and visible, to enhance night vision imagery, however the fused image gave an unnatural color appearance. Therefore, a color transfer based on look-up table (LUT) was used to replace the false color appearance with a colormap derived from a daytime reference image obtained from a public database using the GPS coordinates of the vehicle. Because of the computational demand in deriving the colormap from the reference image, we created an additional local database of colormaps. Reference images from the public database were compared to a compact local database to retrieve one of a limited number of colormaps that represented several driving environments. Each colormap in the local database was stored with an image from which it was derived. To retrieve a colormap, we compared the histogram of the fused image with histograms of images in the local database. The colormaps of the best match was then used for the fused image. Continuously selecting and applying colormaps using this approach offered a convenient way to color night vision imagery.

关键词： color night vision histogram matching image database image fusion thermal video

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New results in semi-supervised learning using adaptive classifier fusion

New results in semi-supervised learning using adaptive class...

引用

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

作者： Lynch, Robert Willett, Peter Analyt Informat Fus Syst LLC Old Mystic CT 06372 USA Univ Connecticut BECE Dept Storrs CT 02841 USA

ISBN: (纸本)9781628410587

In typical classification problems the data used to train a model for each class is often correctly labeled, and so that fully supervised learning can be utilized. For example, many illustrative labeled data sets can be found at sources such as the UCI Repository for Machine Learning (http://***/ml/), or at the Keel Data Set Repository (http://***). However, increasingly many real world classification problems involve data that contain both labeled and unlabeled samples. In the latter case, the data samples are assumed to be missing all class label information, and when used as training data these samples are considered to be of unknown origin (i.e., to the learning system, actual class membership is completely unknown). Typically, when presented with a classification problem containing both labeled and unlabeled training samples, a technique that is often used is to throw out the unlabeled data. In other words, the unlabeled data are not included with existing labeled data for learning, and which can result in a poorly trained classifier that does not reach its full performance potential. In most cases, the primary reason that unlabeled data are not often used for training is that, and depending on the classifier, the correct optimal model for semi-supervised classification (i.e., a classifier that learns class membership using both labeled and unlabeled samples) can be far too complicated to develop.

关键词： Semi-supervised learning Dimensionality reduction Classifier fusion KEEL and UCI Repositories

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MTS in False Positive Reduction for Multi-Sensor fusion

MTS in False Positive Reduction for Multi-Sensor Fusion

引用

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

作者： Woodley, Robert Gosnell, Michael Cudney, Elizabeth 21st Century Syst Inc Omaha NE 68106 USA

ISBN: (纸本)9781628410587

The Mahalanobis Taguchi System (MTS) is a relatively new tool in the vehicle health maintenance domain, but has some distinct advantages in current multi-sensor implementations. The use of Mahalanobis Spaces (MS) allows the algorithm to identify characteristics of sensor signals to identify behaviors in machines. MTS is extremely powerful with the caveat that the correct variables are selected to form the MS. In this research work, 56 sensors monitor various aspects of the vehicles. Typically, using the MTS process, identification of useful variables is preceded by validation of the measurements scale. However, the MTS approach doesn't directly include any mitigating steps should the measurement scale not be validated. Existing work has performed outlier removal in construction of the MS, which can lead to better validation. In our approach, we modify the outlier removal process with more liberal definitions of outliers to better identify variables' impact prior to identification of useful variables. This subtle change substantially lowered the false positive rate due to the fact that additional variables were retained. Traditional MTS approaches identify useful variables only to the extent they provide usefulness in identifying the positive (abnormal) condition. The impact of removing false negatives is not included. Initial results show our approach can reduce false positive values while still maintaining complete fault identification for this vehicle data set.

关键词： Mahalanobis-Taguchi System Multi-Sensor fusion Variable Selection Threshold Selection Vehicle Health Maintenance Fault Detection False Positive Reduction Comparison of Methods

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multisensor, multisource information fusion: architectures, algorithms, and applications 2012

Multisensor, Multisource Information Fusion: Architectures, ...

引用

multisensor, multisource information fusion: architectures, algorithms, and applications 2012

ISBN: (纸本)9780819490858

This proceedings contains 27 papers. These conferences provide a general discussion and dissemination for researchers and designers of recent advances in field of multisensor, multisource information fusion: architectures, algorithms, and applications. Papers published were categorized under seven sessions, i.e. Session 1: information fusion Approaches and algorithms with 4 papers;Session 2: information fusion Approaches and algorithms II with 3 papers;Session 3: information fusion in Robotics with 6 papers;Session 4: information fusion Approaches and algorithms III (biometrics-related) with 3 papers;Session 5: Image fusion with 3 papers;Session 6: information fusion Approaches and algorithms IV (human-in-the-loop) with 5 papers;Session 7: information fusion Systems and Evaluation Measures with 2 papers. Besides, a paper was published as poster session. The key terms of the proceeding papers include Sensor fusion;Intensity interferometry;Data Association;Intrusion protection;Network trace generators;Bayesian fusion;Layered sensing systems.

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Eliminating Mutual Views in fusion of Ranging and RGB-D Data From Robot Teams Operating in Confined Areas

Eliminating Mutual Views in Fusion of Ranging and RGB-D Data...

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

作者： Lyons, Damian M. Shrestha, Karma Fordham Univ Robot & Comp Vis Lab Bronx NY 10458 USA

ISBN: (纸本)9781628410587

We address the problem of fusing laser and RGB-Data from multiple robots operating in close proximity to one another. By having a team of robots working together, a large area can be scanned quickly, or a smaller area scanned in greater detail. However, a key aspect of this problem is the elimination of the spurious readings due to the robots operating in close proximity. While there is an extensive literature on the mapping and localization aspect of this problem, our problem differs from the dynamic map problem in that it involves at one kind of transient map feature, robots viewing other robots, and we know that we wish to completely eliminate all such mutual views. In prior work, we investigated the problem of fusing laser data from multiple robots in such a manner as to reject this spurious data from other robots. This work showed that a combination of local robot-based direction filtering and global map-based visibility filtering at a central map server removed 91% of the spurious data and resulted in a 98% quality improvement. In this paper we additionally consider the problem of fusing RGB-D data generated by a stereo-camera sensor. An approach based on a model of human visual attention is presented and compared with our prior work and with other related work. This approach is an order of magnitude faster than the prior work and yet rejects 73% of the spurious data producing a 55% quality improvement. Results are shown for this approach for two experiments with a two robot team operating in a confined indoor environment (4m x 4m).

关键词： cognitive robotics navigation sensory fusion

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Generalized information fusion and Visualization Using Spatial Voting and Data Modeling

Generalized Information Fusion and Visualization Using Spati...

引用

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

作者： Jaenisch, Holger M. Handley, James W. SPIE Bellingham WA 98227 USA Licht Strahl Engn INC Toney AL 35773 USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9780819495471

We present a novel and innovative information fusion and visualization framework for multi-source intelligence (multiINT) data using Spatial Voting (SV) and Data Modeling. We describe how different sources of information can be converted into numerical form for further processing downstream, followed by a short description of how this information can be fused using the SV grid. As an illustrative example, we show the modeling of cyberspace as cyber layers for the purpose of tracking cyber personas. Finally we describe a path ahead for creating interactive agile networks through defender customized Cyber-cubes for network configuration and attack visualization.

关键词： Spatial Voting Data Modeling information fusion Agile networks Cyber defense Cyber Persona Tracking

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Real-time tracking and fast retrieval of persons in multiple surveillance cameras of a shopping mall

Real-time tracking and fast retrieval of persons in multiple...

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

作者： Bouma, Henri Baan, Jan Landsmeer, Sander Kruszynski, Chris van Antwerpen, Gert Dijk, Judith TNO POB 96864 NL-2509 JG The Hague Netherlands

ISBN: (纸本)9780819495471

The capability to track individuals in CCTV cameras is important for e.g. surveillance applications at large areas such as train stations, airports and shopping centers. However, it is laborious to track and trace people over multiple cameras. In this paper, we present a system for real-time tracking and fast interactive retrieval of persons in video streams from multiple static surveillance cameras. This system is demonstrated in a shopping mall, where the cameras are positioned without overlapping fields-of-view and have different lighting conditions. The results show that the system allows an operator to find the origin or destination of a person more efficiently. The misses are reduced with 37%, which is a significant improvement.

关键词： Surveillance tracking CCTV person re-identification multi-sensor fusion

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Multi-brain fusion and applications to intelligence analysis

Multi-brain fusion and applications to intelligence analysis

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

作者： Stoica, A. Matran-Fernandez, A. Andreou, D. Poli, R. Cinel, C. Iwashita, Y. Padgett, C. NASA Jet Prop Lab Pasadena CA USA

ISBN: (纸本)9780819495471

In a rapid serial visual presentation (RSVP) images are shown at an extremely rapid pace. Yet, the images can still be parsed by the visual system to some extent. In fact, the detection of specific targets in a stream of pictures triggers a characteristic electroencephalography (EEG) response that can be recognized by a brain-computer interface (BCI) and exploited for automatic target detection. Research funded by DARPA's Neurotechnology for Intelligence Analysts program has achieved speed-ups in sifting through satellite images when adopting this approach. This paper extends the use of BCI technology from individual analysts to collaborative BCIs. We show that the integration of information in EEGs collected from multiple operators results in performance improvements compared to the single-operator case.

关键词： Bain-Computer Interfaces collaborative BCI Rapid Serial Visual Presentation visual search

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Spatial Voting For Automatic Feature Selection, fusion, and Visualization

Spatial Voting For Automatic Feature Selection, Fusion, and ...

引用

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

作者： Jaenisch, Holger Handley, James Licht Strahl Engineering Inc. (United States) Johns Hopkins Univ. (United States) Alabama A&M Univ. (United States)

ISBN: (纸本)9780819495471

We present a novel feature selection, fusion, and visualization utility using Spatial Voting (SV). This SV feature optimization utility is designed to be an off-line stand-alone utility to help an investigator find useful feature pairs for cluster analysis and lineage identification. The analysis can be used to enable the analyst to vary parameters manually and explore the best combination that yields visually appealing or significant groups or spreading of data points depending on the planned use of the analysis downstream. Several different criteria are available to the user in order to determine the best SV grid size and feature pair including minimizing zeros, minimizing covariance, balanced minimum covariance, or the maximization of one of eight different scoring metrics: Containment, Rand Index, Purity, Precision, Recall, F-Score, Normalized Mutual information (NMI), and Adjusted Rand Index (ARI). The tool that is described in this work facilitates this analysis and makes it simple, efficient, and interactive if the analyst so desires.

关键词： Spatial Voting Automatic Feature Selection fusion Visualization NMI Rand Index ARI

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fusion of Data from Multiple Sensors with Model-Based Data Analysis

Fusion of Data from Multiple Sensors with Model-Based Data A...

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

作者： Straub, Jeremy Univ North Dakota Dept Comp Sci 3950 Campus RdStop 9015 Grand Forks ND 58202 USA

ISBN: (纸本)9780819495471

For many applications, the combination of multiple data sources is required to reach a conclusion. Limited communications and processing capabilities dictate transmitting, processing and reviewing information more likely to affect a decision before less relevant data. Autonomous craft must, thus, prioritize data for transmission, onboard. Previous work demonstrated the ability to identify and prioritize discrepancies between collected and pre-existing image, topographic and gravitational data. This paper focuses on combining single-source analysis from these datasets to produce a fusion-analysis for predicting the decision-impact value of the component data. This approach is evaluated for persistent surveillance and asteroid assessment applications.

关键词： model-based transmission reduction multiple data source fusion prioritization of data for transmission link budget maximization link budget reduction

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