Self-Organizing Hierarchical Knowledge Discovery by an Artmap Information Fusion System

Classifying terrain or objects may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from users with different goals and situations. Current fusion methods can help resolve such inconsistencies, as when evidence variously suggests that an object is a car, a truck, or an airplane. The methods described here define a complementary approach to the information fusion problem, considering the case where sensors and sources arc both nominally inconsistent and reliable, as when evidence suggests that an object is a car, a vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the automated system or the human user. The ARTMAP self-organizing rule discovery procedure is illustrated with an image example, but is not limited to the image domain.Air Force Office of Scientific Research (F49620-0 1-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-2016, NMA 501-03-1-2030); National Science Foundation (SBE-0354378, DGE-0221680); Office of Naval Research (N00014-01-1-0624

Mobile Robot Sensor Fusion with Fuzzy ARTMAP

The raw sensory input available to a mobile robot suffers from a variety of shortcomings. Sensor fusion can yield a percept more veridical than is available from any single sensor input. In this project, the fuzzy ARTMAP neural network is used to fuse sonar and visual sonar on a B14 mobile robot. The neural network learns to associate specific sensory inputs with a corresponding distance metric. Once trained, the network yields predictions of range to obstacles that are more accurate than those provided by either sensor type alone. This improvement in accuracy holds across all distances and angles of approach tested.Defense Advanced Research Projects Agency, Office of Naval Research, Navy Research Laboratory (ONR-00014-96-1-0772, ONR-00014-95-1-0409, ONR-00014-95-0657

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.Air Force Office of Scientific Research (F49620-01-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-2016, NMA 501-03-1-2030); National Science Foundation (SBE-0354378, DGE-0221680); Office of Naval Research (N00014-01-1-0624); Department of Homeland Securit

Self-Organizing Hierarchical Knowledge Discovery by an ARTMAP Image Fusion System

Classifying novel terrain or objects front sparse, complex data may require the resolution of conflicting information from sensors working at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods described here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among objects are assumed to be unknown to the automated system or the human user. The ARTMAP information fusion system used distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchical knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships.Air Force Office of Scientific Research (F49620-01-1-0397, AFOSR F49620-01-1-0423); Office of Naval Research (N00014-01-1-0624); National Imagery and Mapping Agency and the National Science Foundation for Siegfried Martens (NMA501-03-1-2030, DGE-0221680); Department of Homeland Securit

Neural Sensor Fusion for Spatial Visualization on a Mobile Robot

An ARTMAP neural network is used to integrate visual information and ultrasonic sensory information on a B 14 mobile robot. Training samples for the neural network are acquired without human intervention. Sensory snapshots are retrospectively associated with the distance to the wall, provided by on~ board odomctry as the robot travels in a straight line. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. The neural network effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.Office of Naval Research and Naval Research Laboratory (00014-96-1-0772, 00014-95-1-0409, 00014-95-0657

Information Fusion and Hierarchical Knowledge Discovery by ARTMAP Neural Networks

Mapping novel terrain from sparse, complex data often requires the resolution of conflicting information from sensors working at different times, locations, and scales, and from experts with different goals and situations. Information fusion methods help resolve inconsistencies in order to distinguish correct from incorrect answers, as when evidence variously suggests that an object's class is car, truck, or airplane. The methods developed here consider a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an objects class is car, vehicle, or man-made. Underlying relationships among objects are assumed to be unknown to the automated system of the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierarchial knowledge structures. The system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); Office of Naval Research (N00014-01-1-0624

A Neural Network Method for Mixture Estimation for Vegetation Mapping

While most forest maps identify only the dominant vegetation class in delineated stands, individual stands are often better characterized by a mix of vegetation types. Many land management applications, including wildlife habitat studies, can benefit from knowledge of mixes. This paper examines various algorithms that use data from the Landsat Thematic Mapper (TM) satellite to estimate mixtures of vegetation types within forest stands. Included in the study are maximum likelihood classification and linear mixture models as well as a new methodology based on the ARTMAP neural network. Two paradigms are considered: classification methods, which describe stand-level vegetation mixtures as mosaics of pixels, each identified with its primary vegetation class; and mixture methods, which treat samples as blends of vegetation, even at the pixel level. Comparative analysis of these mixture estimation methods, tested on data from the Plumas National Forest, yields the following conclusions: (1) accurate estimates of proportions of hardwood and conifer cover within stands can be obtained, particularly when brush is not present in the understory; (2) ARTMAP outperforms statistical methods and linear mixture models in both the classification and the mixture paradigms; (3) topographic correction fails to improve mapping accuracy; and (4) the new ARTMAP mixture system produces the most accurate overall results. The Plumas data set has been made available to other researchers for further development of new mapping methods and comparison with the quantitative studies presented here, which establish initial benchmark standards.National Science Foundation (IRI 94-0165, SBR 95-13889); Office of Naval Research (N00014-95-1-0409, N00014-95-0657); Region 5 Remote Sensing Laboratory of the U.S. Forest Servic

Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition

Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.Air Force Office of Scientific Research (F40620-01-1-0423); National Geographic-Intelligence Agency (NMA 201-001-1-2016); National Science Foundation (SBE-0354378; BCS-0235298); Office of Naval Research (N00014-01-1-0624); National Geospatial-Intelligence Agency and the National Society of Siegfried Martens (NMA 501-03-1-2030, DGE-0221680); Department of Homeland Security graduate fellowshi

Die Linke in der SPD

Eine Untersuchung über die Chancen der Linken in der SPD erfordert eigentlich auch eine Einschätzung der Gesamtpartei, die wir hier nicht leisten können. Wrr sprechen von der SPD als einer „Volkspartei" in Anführungszeichen, da mit diesem Begriff die Veränderungen in der SPD seit 1959 nur unzureichend beschrieben werden. Wir gehen davon aus, daß sich spätestens seit 1959 in der SPD eine sozialreformerische Mehrheit durchgesetzt hat, die die bestehende privatkapitalistischeGesellschaft nur noch reformerisch verbessern will. Die Linke iri der SPD sehen wir als Einheit von sicherlich verschiedenen reformistischen Ansätzen. Es geht uns, gerade auch bei den Jusos, nicht um eine Darstellung und Kritik der unterschiedlichen Positionen. Ohne Zweifel bleibt diese Kritik weiterhin notwendig. (Vgl. dazu als ein Beispiel in dieser Zeitschrift: Siegfried Heimann/Bodo Zeuner, Eine neue Integrationsideologie. Zu den Thesen zur Strategie und Taktik des demokratischen Sozialismus des Peter von Oertzen, in: Prokla 14/ 15 (1974), S.105-148).Wir unterscheiden allerdings, ohne das weiter auszuführen, im Spektrum der reformistischen Linken in der SPD sich anpassende Reformisten, die sich mit der Formulierung von Modellen bescheiden und kämpferische Reformisten, die diejenigen, in deren Namen ein „Demokratischer Sozialismus" auf reformistischem Wege erreicht werden soll, als handelnde Subjekte mit einbeziehen