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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81-90 订阅

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Distributed active-camera control architecture based on multi-agent systems

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Highlights on Practical applications of Agents and Multi-Agent Systems - 10th International conference on Practical applications of Agents and Multi-Agent Systems

作者： Bustamante, Alvaro Luis Molina, José M. Patricio, Miguel A. Universidad Carlos III de Madrid Avda. de la Universidad Carlos III 22 28270 Colmenarejo Madrid Spain

ISBN: (纸本)9783642287619

In this contribution a Multi-Agent System architecture is proposed to deal with the management of spatially distributed heterogeneous nets of sensors, specially is described the problem of Pan-Tilt-Zoom or active cameras. The design of surveillance multi-sensor systems implies undertaking to solve two related problems: data fusion and coordinated sensor-task management. Generally, proposed architectures for the coordinated operation of multiple sensors are based on centralization of management decisions at the fusion center. However, the existence of intelligent sensors capable of taking decisions brings the possibility of conceiving alternative decentralized architectures. This problem could be approached by means of a Multi-Agent System (MAS). In specific, this paper proposes a MAS architecture for automatically control sensors in video surveillance environments. © 2012 Springer-Verlag.

关键词： Multi agent systems

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Allothetic and Idiothetic sensor fusion in Rat-inspired Robot Localization

Allothetic and Idiothetic Sensor Fusion in Rat-inspired Robo...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Weitzenfeld, Alfredo Fellous, Jean-Marc Barrera, Alejandra Tejera, Gonzalo Univ South Florida Polytech Div Informat Technol 3433 Winter Lake Rd Lakeland FL 33803 USA Univ Arizona Dept Psychol & Applied Mat Tucson AZ 85721 USA Dept Comp Engn Inst Technol Autonomo Mexico Mexico City 01080 DF Mexico Univ Republica Inst Comp Montevideo 11300 Uruguay

ISBN: (纸本)9780819490858

We describe a spatial cognition model based on the rat's brain neurophysiology as a basis for new robotic navigation architectures. The model integrates allothetic (external visual landmarks) and idiothetic (internal kinesthetic information) cues to train either rat or robot to learn a path enabling it to reach a goal from multiple starting positions. It stands in contrast to most robotic architectures based on SLAM, where a map of the environment is built to provide probabilistic localization information computed from robot odometry and landmark perception. Allothetic cues suffer in general from perceptual ambiguity when trying to distinguish between places with equivalent visual patterns, while idiothetic cues suffer from imprecise motions and limited memory recalls. We experiment with both types of cues in different maze configurations by training rats and robots to find the goal starting from a fixed location, and then testing them to reach the same target from new starting locations. We show that the robot, after having pre-explored a maze, can find a goal with improved efficiency, and is able to (1) learn the correct route to reach the goal, (2) recognize places already visited, and (3) exploit allothetic and idiothetic cues to improve on its performance. We finally contrast our biologically-inspired approach to more traditional robotic approaches and discuss current work in progress.

关键词： robotics biologically-inspired spatial cognition navigation localization SLAM

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Advances in data representation for hard/soft information fusion

Advances in data representation for hard/soft information fu...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Rimland, Jeffrey C. Coughlin, Dan Hall, David L. Graham, Jacob L. Penn State Univ Coll IST 101IST Bldg University Pk PA 16802 USA Penn State Univ Paterno Lib University Pk PA 16802 USA

ISBN: (纸本)9780819490858

Information fusion is becoming increasingly human-centric. While past systems typically relegated humans to the role of analyzing a finished fusion product, current systems are exploring the role of humans as integral elements in a modular and extensible distributed framework where many tasks can be accomplished by either human or machine performers. For example, "participatory sensing" campaigns give humans the role of "soft sensors" by uploading their direct observations or as "soft sensor platforms" by using mobile devices to record human-annotated, GPS-encoded high quality photographs, video, or audio. Additionally, the role of "human-in-the-loop", in which individuals or teams using advanced human computer interface (HCI) tools such as stereoscopic 3D visualization, haptic interfaces, or aural "sonification" interfaces can help to effectively engage the innate human capability to perform pattern matching, anomaly identification, and semantic-based contextual reasoning to interpret an evolving situation. The Pennsylvania State University is participating in a Multi-disciplinary University Research Initiative (MURI) program funded by the U. S. Army Research Office to investigate fusion of hard and soft data in counterinsurgency (COIN) situations. In addition to the importance of this research for Intelligence Preparation of the Battlefield (IPB), many of the same challenges and techniques apply to health and medical informatics, crisis management, crowd-sourced "citizen science", and monitoring environmental concerns. One of the key challenges that we have encountered is the development of data formats, protocols, and methodologies to establish an information architecture and framework for the effective capture, representation, transmission, and storage of the vastly heterogeneous data and accompanying metadata -- including capabilities and characteristics of human observers, uncertainty of human observations, "soft" contextual data, and information pedigree. Thi

关键词： hard and soft information fusion hybrid cognition service oriented architecture SOA ESB

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A novel approach to guarantee causal message ordering in pre-planned wireless sensor networks

A novel approach to guarantee causal message ordering in pre...

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12th International conference on algorithms and architectures for Parallel Processing, ICA3PP 2012

作者： Kim, Chayoung Ahn, Jinho Dept. of Computer Science College of Natural Science Kyonggi University Suwon Gyeonggi-do 443-760 Korea Republic of

ISBN: (纸本)9783642330643

Data fusion often depends on the time of occurrence of fused sensor readings, called as temporal ordering, which is defined with respect to a single message, or as causal ordering to guarantee dependency relationships between messages in many-to-many communication patterns. There are some temporal ordering protocols based on physical time synchronization in publish/subscribe (P/S) paradigm of wireless sensor networks, but there exist little research works on development of causal ordering protocols based on logical time in P/S of wireless sensor networks (WSNs). Causal message ordering is more useful for most distributed applications in which a large number of sensor nodes request cooperating to fuse their data in WSNs. Temporal ordering is not sufficient for these distributed applications because it is not defined for dependency relationships between these messages. Also, many-to-many communication patterns attempt to address the problem of providing scalability of data propagation, guaranteeing message delivery order and supporting overlapping multicast groups in WSNs. In this paper, we present a novel approach based on gossiping and firefly synchronization instead of physical time synchronization, guaranteeing the causal message ordering property in P/S of WSNs. In the proposed protocol, every sensor broker disseminates the multicast message including the latest time-stamped information that represents the gossip round in which the message is generated to subscribers for causal message ordering. The latest time-stamped information is represented using colors. Its scalability feature might be highly suitable for the area of the applications requiring only the minimum causal information of message delivery with flexible consistency by cooperating to fuse their data. © 2012 Springer-Verlag.

关键词： Scalability

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Plenary talk IV: Robust 3-D vision techniques: algorithms and applications

Plenary talk IV: Robust 3-D vision techniques: Algorithms an...

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SICE Annual conference

作者： In So Kweon KAIST South Korea

Summary form only given: For the 3D reconstruction of static and dynamic scenes, we have developed several robust computer vision methods ranging from the image enhancement to the design of novel camera systems. In this talk, we first present a sparse-model based approach to recover the camera response function, which is crucial for many color image processing problems such as high dynamic range imaging and photo-consistent mosaicking. We also present a sensor-fusion based approach to obtain super-resolution (SR) depth images from low-resolution depth images and high-resolution color images. A learning-based optimization framework successfully recovers the SR depth images. The second part of the talk introduces new camera systems to capture the 3D information accurately and efficiently. The camera systems include (i) “camera + depth” fusion camera systems, (ii) a fast bundle adjustment based approach for large-scale datasets, (iii) a novel coded-light photometric stereo for modeling 3-D dynamic scenes. Specifically, we present a hand-held fusion sensor system, consisting of four cameras and two 2D laser scanners, to capture 3D information of large-scale scenes. This new approach allows boosting the advantages of two sensor systems and complements the weakness of the two. As an important application of 3D vision techniques, we demonstrate the robustness of the methods by automatically reconstructing a large-scale environment, such as KAIST campus.

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architectures, algorithms, and applications Using Bayesian Networks

Architectures, Algorithms, and Applications Using Bayesian N...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Kingsbury, Todd Gen Dynam Adv Informat Syst San Diego CA 92108 USA

ISBN: (纸本)9780819486387

A Bayesian network is a tree structure where each branch represents a classification candidate. The leaves of the tree represent observable target features such as frequency or length. An optimized tree groups similar features together, e.g. frequency and pulse width, while collecting dissimilar or disparate information, e. g. spectral and kinematics, all within the same unifying structure. A vehicular track then is a subset of the a priori candidate library and contains only feasible branches. The algorithm for updating the confidence of each feasible candidate according to Bayes' rule is embedded in each track, as is the ability of a track to learn, apply a priori probability distributions, switch modes, switch among kinematics models, apply tracking history to classification and apply classification history to tracking, and support multisensor correlation and sensor fusion.

关键词： Bayesian tracking classification sensor fusion

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The Continuum fusion theory of signal detection applied to a bi-modal fusion problem

The Continuum Fusion theory of signal detection applied to a...

引用

conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Schaum, A. USN Res Lab Washington DC 20375 USA

ISBN: (纸本)9780819486387

A new formalism has been developed that produces detection algorithms for model-based problems, in which one or more parameter values is unknown. Continuum fusion can be used to generate different flavors of algorithm for any composite hypothesis testing problem. The methodology is defined by a fusion logic that can be translated into max/min conditions. Here it is applied to a simple sensor fusion model, but one for which the generalized likelihood ratio test is intractable. By contrast, a fusion-based response to the same problem can be devised that is solvable in closed form and represents a good approximation to the GLR test.

关键词： bi-modal continuum fusion detector algorithm

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Multi-sensor remote sensing information fusion for urban area classification and change detection

Multi-sensor remote sensing information fusion for urban are...

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conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Palubinskas, Gintautas Makarau, Aliaksei Reinartz, Peter German Aerosp Ctr DLR Remote Sensing Technol Inst Oberpfaffenhofen Germany

ISBN: (纸本)9780819486387

Information extraction from multi-sensor remote sensing imagery is an important and challenging task for many applications such as urban area mapping and change detection. A special acquisition (orthogonal) geometry is of great importance for optical and radar data fusion. This acquisition geometry allows to minimize displacement effects due inaccuracy of Digital Elevation Model (DEM) used for data ortho-rectification and existence of unknown 3D structures in a scene. Final data spatial alignment is performed by recently proposed co-registration method based on a Mutual Information measure. For a combination of features originating from different sources, which are quite often non-commensurable, we propose an information fusion framework called INFOFUSE consisting of three main processing steps: feature fission (feature extraction aiming at complete description of a scene), unsupervised clustering (complexity reduction and feature representation in a common dictionary) and supervised classification realized by Bayesian or Neural networks. An example of urban area classification is presented for the orthogonal acquisition of space borne very high resolution WorldView-2 and TerraSAR-X Spotlight imagery over Munich city, South Germany. Experimental results confirm our approach and show a great potential also for other applications such as change detection.

关键词： multi-sensor remote sensing optical image SAR information fusion classification change detection

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fusion of Disparate Spectra for Chemical Identification

Fusion of Disparate Spectra for Chemical Identification

引用

conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Minor, Christian P. Brooke, Heather Johnson, Kevin J. Nova Res Inc Alexandria VA 22308 USA US Navy Res Lab Chem Div Washington DC 20375 USA

ISBN: (纸本)9780819486387

Currently there is no systematic framework for characterizing fused, multisensory systems, and therefore the comparison of multiple independent systems is difficult without extensive field-testing. Development of a framework would allow for theoretical comparisons and enable more rapid prototyping of fused sensor systems, guidance for design from existing sensor components, and more effective engineering of new sensors optimized for use in fused sensor systems. Recent research at NRL has focused on characterizing Fourier transform infrared spectroscopy (FTIR) and mass spectrometry data for fused, multisensor applications to enhance chemical detection and discrimination in the presence of complex interfering backgrounds. An information theoretic approach has been used to elucidate the information content available from spectral data, quantify the ability of these sensing techniques to distinguish chemicals, and determine their susceptibility to noise and resolution limitations. The approach has also been applied to feature extraction and data fusion techniques on these data. Results characterizing the effectiveness of a fused multisensor system combining FTIR and mass spectrometry are presented.

关键词： multimodal sensing data fusion mass spectrometry infrared spectroscopy

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Implementation and testing of a sensor-netting algorithm for early warning and high confidence C/B threat detection

Implementation and testing of a sensor-netting algorithm for...

引用

conference on Multisensor, Multisource Information fusion - architectures, algorithms, and applications

作者： Gruber, Thomas Grim, Larry Fauth, Ryan Tercha, Brian Powell, Chris Steinhardt, Kristin MESH Inc Oxford PA 19363 USA

ISBN: (纸本)9780819486387

Large networks of disparate chemical/biological (C/B) sensors, MET sensors, and intelligence, surveillance, and reconnaissance (ISR) sensors reporting to various command/display locations can lead to conflicting threat information, questions of alarm confidence, and a confused situational awareness. sensor netting algorithms (SNA) are being developed to resolve these conflicts and to report high confidence consensus threat map data products on a common operating picture (COP) display. A data fusion algorithm design was completed in a Phase I SBIR effort and development continues in the Phase II SBIR effort. The initial implementation and testing of the algorithm has produced some performance results. The algorithm accepts point and/or standoff sensor data, and event detection data (e. g., the location of an explosion) from various ISR sensors (e.g., acoustic, infrared cameras, etc.). These input data are preprocessed to assign estimated uncertainty to each incoming piece of data. The data are then sent to a weighted tomography process to obtain a consensus threat map, including estimated threat concentration level uncertainty. The threat map is then tested for consistency and the overall confidence for the map result is estimated. The map and confidence results are displayed on a COP. The benefits of a modular implementation of the algorithm and comparisons of fused /un-fused data results will be presented. The metrics for judging the sensor-netting algorithm performance are warning time, threat map accuracy (as compared to ground truth), false alarm rate, and false alarm rate v. reported threat confidence level.

关键词： sensor netting algorithm fusion tomography map uncertainty consistency

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