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Advanced Object Tracking and Data Fusion: Methods and Applications


Session organizer: Wolfgang Koch 

Tutorial Abstract:

In many engineering applications, including surveillance, guidance, or navigation, single stand-alone sensors or sensor networks are used for collecting information on time varying quantities of interest, such as kinematical characteristics of moving or stationary objects of interest (e.g. manoeuvring air targets or ground moving vehicles). More strictly speaking, the state vectors of stochastically moving objects are to be estimated from time series of sensor data sets, also called scans or data frames. The individual measurements are produced at discrete instants of time, being referred to as scan or frame time, target revisit time, or data innovation time. In case of moving point-source objects or small extended objects often relatively simple statistical models can be derived from basic physical laws describing their temporal behaviour and thus defining the underlying dynamical system. In addition, appropriate sensor models are available or can be constructed, which characterize the statistical properties of the produced sensor data sufficiently correct.

Illustrated by examples from real applications, methods for dealing with selected aspects of sensor data fusion will be discussed: At first, `fusion of data produced at different times', i.e. the tracking problem, will be considered. The use of multiple sensors is illustrated for improving detection and track accuracy (`fusion of data produced by multiple sensors'). This is followed by a discussion of methods for integrating non-sensor context information (`fusion of data with refined sensor models', `fusion of data with road maps'. The overview is concluded by a sensor management example. A written manuscript will be made available in electronic form.


Fusion of Quantitative and Qualitative Information with Neuro-Fuzzy Systems and Bayesian Networks


Session organizer: Rudolf Kruse 

Tutorial Abstract:

In many engineering applications, empirical data and expert knowledge are used simultaneously. Often there is a need to fuse these different types of information. In many cases a common framework for the representation of quantitative and qualitative knowledge is needed, because methods for learning, adaptation, updating, revision, etc. have to be integrated. In this tutorial we consider two fusion frameworks in more details: Probabilistic graphical models and neuro-fuzzy systems. In the last decade graphical models have become one of the most popular tools to structure uncertain knowledge about high-dimensional domains in order to make reasoning in such domains feasible. Their most prominent representatives are Bayesian networks and Markov networks, but also relational and possibilistic networks turned out to be useful in practical applications. For all types of networks several clear, correct, and efficient propagation methods have been developed, with join tree propagation and bucket elimination being among the most widely known. In practice, however, the need also arises to support a variety of additional knowledge-based operations on graphical models. In this turorial the fusion of networks with relational rule systems, and operations such as revision, updating, network approximation, and learning from data samples are studied.

As a second framework for the fusion of qualitative and quantitative information, neuro-fuzzy systems are very popular. Nowadays fuzzy systems are frequently applied in data analysis problems, because they can provide a simple, inexpensive and interpretable modelling of a priori knowledge in terms of IF-THEN-rules. In order to take advantage of the knowledge hidden in data sets, it must be possible to improve the priori knowledge in the light of this additional data material. The so called learning and adaptation in fuzzy systems is most often implemented by learning techniques derived from neural networks. In this tutorial we study the methods used in our neuro-fuzzy system NEFCLASS in some more detail. The research to be reported about in this tutorial was mainly triggered by consulting of the automobile manufacturer Daimler-Chrysler and Volkswagen Group, where graphical models are now established for several tasks, and by projects with British Telecom, BMW, Siemens and Deutsche Giro- und Sparkassenverband, where fuzzy systems and neural networkshave been used in the context of exploratory data analysis.


A New Class of Computation for Distributed Sensor Networks


Session organizer: S. S. Iyengar 

Tutorial Abstract:

“The Next Generation Distributed Sensor Network Simulator.” This talk provides an overview of the development of theory and implementation of real time sensor simulators for unstructured applications. The talk also discusses a comparison of this simulator with the other existing simulator currently available in the literature.


Multisource Multisensor Information Fusion: Architectures, Algorithms, and Applications


Session organizer: Belur Dasarathy 

Tutorial Abstract:

This tutorial will offer an overview of multi-sensor, and multi-source information fusion field from the author's perspectives along three fronts: architectures, algorithms, and applications. This is accomplished through the process of addressing the questions of what, why, when, and how as it relates to information fusion. The presentation starts with an introduction to the terminology, motivation for the study of information fusion, followed by a delineation of the various taxonomies discernible in the field. This will be followed by a brief discussion of some basic architecture design options. A sampling of algorithms conceived for fusion in different modes will be presented next. The tutorial will emphasize fusion at feature and decision levels since this is less directly driven by sensor and application specific considerations and hence amenable to a discussion that is independent of any specific application. The presentation will close with a panoramic view of the application domains.


An Introduction to Dirichlet Processes


Tutorial Organizer: Volker Tresp 

Tutorial Abstract:

Bayesian modeling is a principled approach to updating the degree of belief in a hypothesis given prior knowledge and given available evidence. Both prior knowledge and evidence are combined using Bayes' rule to obtain the a posteriori hypothesis. In most cases of interest to machine learning, the prior knowledge is formulated as a prior distribution over parameters and the evidence corresponds to the observed data. By applying Bayes' formula we can perform inference about new data. In hierarchical Bayesian modeling, one is not primarily interested in learning the true parameters, but in learning the true distribution of parameters. Consider the situation of learning a model for predicting the outcome for patients with a particular disease based on patient information. Due to differences in patient mix and hospital characteristics such as staff experiences the models are different for different hospitals but also will share some common effects. This can be modeled by assuming that the model parameters originate from a particular distribution of parameters that can be learned from data from a sufficiently large number of hospitals. If applied to a new hospital, this learned distribution assumes the role of a learned prior. Now a technical problem arises: the parameterization of this learned prior distribution needs to be sufficiently flexible to truthfully represent the actual distribution. Thus instead of selecting a prior distribution out off a class of standard distributions, it is more appropriate to assume a nonparametric representation. The most common way of generating a nonparametric distribution is by means of a Dirichlet process. The Dirichlet process can be thought of as a generalization of a Dirichlet distribution if we let the number of states go to infinity.

In my tutorial I will give an introduction to Dirichlet processes and present various applications. I hope that this tutorial might inspire the participants to think of novel applications of nonparametric Bayesian modeling to multi-sensor fusion and to the integration for intelligent systems.

Image Fusion


Session organizer: Michael Heizmann 

Tutorial Abstract:

For many tasks of automated visual inspection and environmental perception, acquisition and evaluation of a single image is not sufficient. The reason is that the information of interest cannot be acquired from a single image at all or with the necessary quality. This insufficiency can result from sensor characteristics such as sensor noise. In addition, the information of interest may be unavailable by direct observation. In such cases, the desired information can often be obtained by recording multiple images (so-called image series), which are then combined in an image fusion in order to achieve a new or improved representation of the image content or the scene properties.

This tutorial gives an introduction to common fusion approaches for image data and illustrates main aspects of image fusion. Image sensors are characterized with respect to the fusability of the acquired data. Abstraction levels on which an image fusion can be performed are explained. To describe processing stages for image fusion in general, a universal fusion scheme is introduced. The mentioned aspects of image fusion are illustrated by means of practical examples.

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