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Navigation of uncertain terrain by fusion of information from real and synthetic imagery

Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Lyons, Damian M. Nirmal, Paramesh Paul Benjamin, D. Fordham University Robotics and Computer Vision Laboratory Bronx NY 10458 United States Pace University Department of Computer Science New York NY 10023 United States

ISBN: (纸本)9780819490858

We consider the scenario where an autonomous platform that is searching or traversing a building may observe unstable masonry or may need to travel over unstable rubble. A purely behaviour-based system may handle these challenges but produce behaviour that works against long-terms goals such as reaching a victim as quickly as possible. We extend our work on ADAPT, a cognitive robotics architecture that incorporates 3D simulation and image fusion, to allow the robot to predict the behaviour of physical phenomena, such as falling masonry, and take actions consonant with long-term goals. We experimentally evaluate a cognitive only and reactive only approach to traversing a building filled with various numbers of challenges and compare their performance. The reactive only approach succeeds only 38% of the time, while the cognitive only approach succeeds 100% of the time. While the cognitive only approach produces very impressive behaviour, our results indicate how much better the combination of cognitive and behaviour-based can be. © 2012 SPIE.

关键词： Robots

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Homeland situation awareness through mining and fusing heterogeneous information from intelligence databases and field sensors

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Digioia, Giusj Panzieri, Stefano Computer and Automation Department Roma Tre University Via della Vasca Navale 79 00146 Rome Italy Engineering Ingegneria Informatica SpA Defense and Security Department Via Riccardo Morandi 32 00148 Ponte Galeria Rome Italy

ISBN: (纸本)9780819490858

One of the most felt issues in the defence domain is that of having huge quantities of data stored in databases and acquired from field sensors, without being able to infer information from them. Usually databases are continuously updated with observations, and are related to heterogeneous data. Deep and continuous analysis on data could mine useful correlations, explain relations existing among data and cue searches for further evidences. The solution to the problem addressed before seems to deal both with the domain of Data Mining and with the domain of high level Data fusion, that is Situation Assessment, Threat Assessment and Process Refinement, also synthesised as Situation Awareness. The focus of this paper is the definition of an architecture for a system adopting data mining techniques to adaptively discover clusters of information and relation among them, to classify observations acquired and to use the model of knowledge and the classification derived in order to assess situations, threats and refine the search for evidences. Sources of information taken into account are those related to the intelligence domain, as IMINT, HUMINT, ELINT, COMINT and other non-conventional sources. The algorithms applied refer to not supervised and supervised classification for rule exploitation, and adaptively built Hidden Markov Model for situation and threat assessment. © 2012 SPIE.

关键词： Data mining

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Spectral and spatial algorithm architecture for classification of hyperspectral and LIDAR

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Rand, Robert S. Khuon, Timothy S. National Geospatial-Intelligence Agency Office of Innovision Basic and Applied Research 7500 GEOINT Drive Springfield VA 22153 United States

ISBN: (纸本)9780819490858

Architecture for neural net multi-sensor data fusion is introduced and analyzed. This architecture consists of a set of independent sensor neural nets, one for each sensor, coupled to a fusion net. The neural net of each sensor is trained (from a representative data set of the particular sensor) to map to a hypothesis space output. The decision outputs from the sensor nets are used to train the fusion net to an overall decision. To begin the processing, the 3D point cloud LIDAR data is classified based on a multi-dimensional mean-shift segmentation and classification into clustered objects. Similarly, the multi-band HSI data is spectrally classified by the Stochastic Expectation-Maximization (SEM) into a classification map containing pixel classes. For sensor fusion, spatial detections and spectral detections complement each other. They are fused into final detections by a cascaded neural network, which consists of two levels of neural nets. The first layer is the sensor level consisting of two neural nets: spatial neural net and spectral neural net. The second level consists of a single neural net, that is the fusion neural net. The success of the system in utilizing sensor synergism for an enhanced classification is clearly demonstrated by applying this architecture for classifying on November 2010 airborne data collection of LIDAR and HSI over the Gulfport, MS, area. © 2012 SPIE.

关键词： Sensor data fusion

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Colour-the-INSight: Combining a direct view rifle sight with fused intensified and thermal imagery

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Hogervorst, M.A. Jansen, C. Toet, A. Bijl, P. Bakker, P. Hiddema, A.C. van Vliet, S.F. TNO Human Factors P.O. Box 23 3769 GZ Soesterberg Netherlands Thales Nederland B.V. Bestevaer 46 Huizen Netherlands PHOTONIS Netherlands B.V. P.O. Box 60 9300 AB Roden Netherlands Ministry of Defence Joint Soldier and Equipment Centre of Expertise Wuytierslaan 198 Amersfoort Netherlands

ISBN: (纸本)9780819490858

We present the design and evaluation of a new demonstrator rifle sight viewing system containing direct view, red aim point and fusion of an (uncooled, LWIR) thermal sensor with a digital image intensifier. Our goal is to create a system that performs well under a wide variety of (weather) conditions during daytime and nighttime and combines the advantages of the various sensor systems. A real-time colour image with salient hot targets is obtained from the night vision sensors by implementing the Colour-the-Night fusion method (Hogervorst & Toet, 2010) on the on-board processor. The prototype system was evaluated in a series of field trials with military observers performing detection and identification tasks. The tests showed that during daytime the addition of a thermal image to direct vision is advantageous, e.g. for the detection of hot targets. At nighttime, the fusion of thermal and image intensified imagery results in increased situational awareness and improved detection of (hot) targets. For identification of small (handheld) objects, the technology needs to be further refined. © 2012 SPIE.

关键词： Image fusion

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Allothetic and idiothetic sensor fusion in rat-inspired robot localization

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Weitzenfeld, Alfredo Fellous, Jean-Marc Barrera, Alejandra Tejera, Gonzalo Div. of Information Technology University of South Florida Polytechnic 3433 Winter Lake Rd Lakeland FL 33803 United States Dept. of Psychology and Applied Mathematics University of Arizona 1503 E University Blvd. Tucson AZ 85721 United States Dept. of Computer Engineering Instituto Tecnológico Autónomo de México Rio Hondo #1 Mexico DF 01080 Mexico Inst. of Comp. Julio Herrera y Reissig 565 Universidad de la República 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. © 2012 SPIE.

关键词： Robots

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Human cognitive and perceptual factors in JDL level 4 hard / soft data fusion

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Rimland, Jeffrey C. Hall, David L. Graham, Jacob L. Pennsylvania State University College of Information Sciences and Technology 101 IST Building University Park PA 16802-6822 United States

ISBN: (纸本)9780819490858

Utilization of human participants as "soft sensors" is becoming increasingly important for gathering information related to a wide range of phenomena including natural and man-made disasters, environmental changes over time, crime prevention, and other roles of the "citizen scientist." The ubiquity of advanced mobile devices is facilitating the role of humans as "hybrid sensor platforms", allowing them to gather data (e.g. video, still images, GPS coordinates), annotate it based on their intuitive human understanding, and upload it using existing infrastructure and social networks. However, this new paradigm presents many challenges related to source characterization, effective tasking, and utilization of massive quantities of physical sensor, human-based, and hybrid hard/soft data in a manner that facilitates decision making instead of simply amplifying information overload. In the Joint Directors of Laboratories (JDL) data fusion process model, "level 4" fusion is a meta-process that attempts to improve performance of the entire fusion system through effective source utilization. While there are well-defined approaches for tasking and categorizing physical sensors, these methods fall short when attempting to effectively utilize a hybrid group of physical sensors and human observers. While physical sensor characterization can rely on statistical models of performance (e.g. accuracy, reliability, specificity, etc.) under given conditions, "soft" sensors add the additional challenges of characterizing human performance, tasking without inducing bias, and effectively balancing strengths and weaknesses of both human and physical sensors. This paper addresses the challenges of the evolving human-centric fusion paradigm and presents cognitive, perceptual, and other human factors that help to understand, categorize, and augment the roles and capabilities of humans as observers in hybrid systems. © 2012 SPIE.

关键词： Data fusion

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multisource causal data mining

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Woodley, Robert Gosnell, Michael Shallenberger, Kevin 21st Century Systems Inc. 6825 Pine Street Suite 141 Box 10 Omaha NE 68106-2857 United States

ISBN: (纸本)9780819490858

Analysts are faced with mountains of data, and finding that relevant piece of information is the proverbial needle in a haystack, only with dozens of haystacks. Analysis tools that facilitate identifying causal relationships across multiple data sets are sorely needed. 21st Century Systems, Inc. (21CSi) has initiated research called Causal-View, a causal datamining visualization tool, to address this challenge. Causal-View is built on an agent-enabled framework. Much of the processing that Causal-View will do is in the background. When a user requests information, Data Extraction Agents launch to gather information. This initial search is a raw, Monte Carlo type search designed to gather everything available that may have relevance to an individual, location, associations, and more. This data is then processed by Data-Mining Agents. The Data-Mining Agents are driven by user supplied feature parameters. If the analyst is looking to see if the individual frequents a known haven for insurgents he may request information on his last known locations. Or, if the analyst is trying to see if there is a pattern in the individual's contacts, the mining agent can be instructed with the type and relevance of the information fields to look at. The same data is extracted from the database, but the Data Mining Agents customize the feature set to determine causal relationships the user is interested in. At this point, a Hypothesis Generation and Data Reasoning Agents take over to form conditional hypotheses about the data and pare the data, respectively. The newly formed information is then published to the agent communication backbone of Causal-View to be displayed. Causal-View provides causal analysis tools to fill the gaps in the causal chain. We present here the Causal-View concept, the initial research into data mining tools that assist in forming the causal relationships, and our initial findings. © 2012 SPIE.

关键词： Data mining

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Advances in data representation for hard/soft information fusion

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Rimland, Jeffrey C. Coughlin, Dan Hall, David L. Graham, Jacob L. Pennsylvania State University College of IST 101IST Building University Park PA 16802-6822 United States Pennsylvania State University OE03 Paterno Library University Park PA 16802 United States

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. This

关键词： Service oriented architecture (SOA)

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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...

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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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