Showing papers in "Information Fusion in 2003"

TL;DR: The aim is to reframe the multiresolution-based fusion methodology into a common formalism and to develop a new region-based approach which combines aspects of both object and pixel-level fusion.

TL;DR: A solution is provided that addresses the information deconfliction problem that arises when estimates from two or more different nodes are determined to be inconsistent with each other, e.g., when two high precision (small covariance) estimates place the position of a particular object at very different locations.

TL;DR: An algorithm for building ensembles of simple Bayesian classifiers in random subspaces, which includes a hill-climbing-based refinement cycle, which tries to improve the accuracy and diversity of the base classifiers built on random feature subsets.

TL;DR: The method requires a false colour RGB image that is produced by mapping three individual bands of a multiband nightvision system to the respective channels of an RGB image, and the inverse transformation to RGB space yields a nightvision image with a day-time colour appearance.

TL;DR: The binary (0,1) code matrix conditions necessary for reduction of error in the ECOC framework are considered, and it is shown that equidistant codes can be generated by using properties related to the number of 1s in each row and between any pair of rows.

TL;DR: A Bayesian solution to target tracking using delayed, out-of-sequence measurements is presented and approximate, implementable algorithms for both cluttered and non-cluttered scenarios involving single and multiple time-delayed measurements are provided.

TL;DR: System performance metrics for measuring both absolute and relative degradation in fused image quality are proposed when fusing noisy input modalities and are employed in a comparative study of the robustness of typical image fusion schemes whose inputs are corrupted with noise.

TL;DR: Research into analysis and data fusion for sensors measuring hydraulic parameters of the pipeline water flow in treated water distribution systems and an artificial neural network based system is used to construct an empirical model for the prediction and classification of leaks.

TL;DR: An integrated multi-phase approach to middle and high level data fusion with an application to situation and threat assessments and a fuzzy belief network that performs heuristic belief propagation for threat assessment.

TL;DR: This research presents the results of a three-year, multi-million-euro, high-resolution photographic survey of the Caspian Sea floor conducted in June and July of 1997 by a team of scientists and engineers from the University of Poitiers and the French Academy of Sciences.

TL;DR: This paper presents algorithms to calculate multiple decision templates per class, and demonstrates their behaviour on two data sets, a synthetic data set, and a data set derived from a real world pattern recognition problem, the classification of bioacoustic time series.

TL;DR: The developed system named Intelligent Driving Recognition with Expert System is built on a two-level rule-based system that takes into account the measurements from the sensors and the sequence of the states that describes the manoeuvres.

TL;DR: Results show that bagging improves classification accuracy and limits the influence of outliers and ambiguous training patterns, in the framework of the Dempster–Shafer theory of evidence.

TL;DR: Recognizing the essential need of intelligent agents to perceive the environment to take appropriate actions, the concept of perception management is proposed, which refers to controlling the process of data acquisition from the external world to enhance percepts obtained.

TL;DR: It is shown that TFHs are related to the traditional cross-correlation techniques except that are less lenient on large phase errors, and that a weighted or conditional TFH technique can achieve better results than a traditional histogram with unit incrementing functions.

TL;DR: This paper proposes a formal definition of tactical plan recognition, i.e. the recognition of enemy plans, based on classical abduction, and focuses on another important characteristic feature of this task, namely that the identity of the observed enemy objects, for which plans are to be recognized, may be unknown.

TL;DR: Results indicate that the dualband input images do improve the performance of the clutter rejecters and target detectors over their single band counterparts, suggesting that dimensionality reduction by the eigenspace transformation can be performed independently on the two bands.

TL;DR: This research proposes to use Bayes classifier, committee of experts, stacked-generalization, winner-takes-all, and ranking-based fusion techniques for designing the composite classifiers of forward-looking infrared (FLIR) automatic target recognition (ATR).

TL;DR: A generic design and implementation of a multi-modal operator interface (MOI) which has been dedicated for supervisory control systems and is investigated through incorporating an anticipatory system that protects against possible catastrophic errors of the operator.

TL;DR: SSTBoost is introduced, a predictive classification methodology designed to target the accuracy of a modified boosting algorithm towards required sensitivity and specificity constraints, which can be applied for the early diagnosis of melanoma cancer or in other problems in which an automated cost-sensitive classification is required.

TL;DR: The Bayesian data reduction algorithm was developed previously and is applied to the preceding decision-fusion problem, with favorable comparison to a number of other expert-mixing approaches.

TL;DR: An interactive fusion system that uses the fuzzy set theory to detect regions in three-dimensional seismic images using geophysicist interpreter knowledge by means of a fuzzy rule-based classifier is presented.

TL;DR: This paper attempts to quantify the effect on the probability of correctly correlating two independently developed track sets of various types of information that might be included in the handover data set and suggests modifications to current correlation algorithms to reduce information handover to the missile and increase computational speed.

TL;DR: The characteristics of the information theoretic decision rule show that the decisions are less sensitive to changes in the reorganization of hypothesis and decision sets to different levels of abstraction in comparison to Bayesian probability theory.