Showing papers in "Information Fusion in 2010"

TL;DR: The proposed algorithm significantly improves the fusion quality in terms of: entropy, mutual information, discrepancy, and average gradient; compared to fusion methods including, IHS, Brovey, discrete wavelet transform (DWT), a-trous wavelet and RIM.

TL;DR: A new contourlet packet is constructed based on a complete wavelet quadtree followed by a nonsubsampled directional filter bank, which has more accurate reconstruction of images than WP and shows the superiorities of the method to its counterparts in image clarity and some numerical guidelines.

TL;DR: The formulas of computing the cross-covariances among local estimation errors are proposed and two Monte Carlo simulation examples for an infrared target tracking system and a Bernoulli-Gaussian white noise deconvolution system show their effectiveness.

TL;DR: A dendritic cell algorithm is derived based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system.

TL;DR: A new method to render multi-band night-time imagery (images from sensors whose sensitive range does not necessarily coincide with the visual part of the electromagnetic spectrum, e.g. image intensifiers, thermal camera's) in natural daytime colors is presented.

TL;DR: The extent to which audiovisual speech perception should be considered to represent a 'special' case of audiovISual multisensory integration is discussed, and the available evidence on this question is reviewed.

TL;DR: Design principles for modular Bayesian fusion systems which can (i) cope with large quantities of heterogeneous information and (ii) can adapt to changing constellations of information sources on the fly are introduced.

TL;DR: In this paper, the cumulative rule and the averaging rule of belief fusion were proposed for subjective logic consensus operator for independent and dependent opinions respectively, which are applicable to the combination of general basic belief assignments.

TL;DR: The results show that this evaluation method can provide valuable insight into the way fusion schemes combine perceptually important details from the individual input image modalities, and can be used to design image fusion schemes that are optimally tuned to human visual perception for different applications and scenarios.

TL;DR: A novel method is proposed that uses fuzzy linguistic assessment variables instead of crisp values of incomplete fuzzy preference relations to ensure comparison consistency and reflects an environment in which some uncertainty or vagueness exists.

TL;DR: It is shown how the inclusion of this new dynamic local fusion agent guarantees that, in a video-surveillance system, objects of interest are successfully tracked across the whole area, assuring continuity and seamless transitions.

TL;DR: A non-parametric and region-based image fusion based on the Bootstrap sampling (BS) principle, which reduces the dependence effect of pixels in real images and minimizes the fusion time.

TL;DR: In this article, the authors present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs.

TL;DR: A novel decision level fusion technique which incorporates the proposed feature selection strategy is developed for improved face recognition which outperforms all the individual image modalities as well as the data level fused images obtained using the Discrete Wavelet Transform (DWT) based fusion technique.

TL;DR: In this paper, efficient neural networks (ENNs) for face detection are presented and it is proved that the equations given in previous work for conventional and fast neural networks are not valid.

TL;DR: The intent of this paper is to show enhancements in Levels 2 and 3 fusion capabilities through a new class of models and algorithms in graph matching through a complete implementation of a heuristic approach using an inexact isomorphism technique.

TL;DR: The purpose of this paper is to rigorously prove that the correlation-based quality measures have some undesired behavior under certain conditions and to demonstrate the utility of the theoretical analysis, by illustrating its predictive capabilities.

TL;DR: This paper addresses the uncertainty problem in the fusion of the information provided by complementary modalities like visible spectrum and thermal infrared video in a generic framework using evidence theory and develops a novel algorithm for dynamic assessment of individual sensor reliability within the belief model.

TL;DR: A temporal fusion mechanism that allows a set of agents to fuse their plans and generate a global coordinated plan is proposed and applied to a Combat Search and Rescue application.

TL;DR: A regression approach based on the statistical learning theory of Vapnik is proposed, which gives two kinds of classifiers: a fuzzy SVM and a belief SVM.

TL;DR: The proposition is the proposition of a hybrid fusion system that performs as well as the centralized fusion detector respectively optimal for simultaneous and not simultaneous change.

TL;DR: This paper relaxs the policy of always fusing delayed measurements and explores thresholding techniques that result in delayed measurements being selectively fused, and shows that selective fusion can reduce computational costs while maintaining near optimal performance.

TL;DR: To help in the construction of multi-criteria aggregation functions the concept of criteria organization is introduced to indicate the body of knowledge that guides how an alternative's satisfactions to the individual criteria to obtain its overall satisfaction.

TL;DR: The aim of this study was to investigate the extent to which the simultaneous presentation of auditory and visual signals enhances the estimation of motion speed and instantaneous position in this temporal localization task.

TL;DR: The current paper presents capacity analysis of a behavioral data set previously analyzed using the race model, and it is hoped that this exploration of capacity and race model analyses will encourage the use of this promising new technique both in multisensory research and other applicable fields.

TL;DR: Some of the most important properties of software agents, namely autonomy, monitoring and communication skills, are desirable features for building an ideal information fusion system.

TL;DR: Fusion methods of multiple classifiers for improving the classification of individual components in terms of positioning accuracy through computer vision inspection are presented.

TL;DR: Results showed the SBS display to lead to much poorer accuracy than the other displays, while reaction times in carrying out the secondary task favoured AVE in the HL sequence and DWT in the LL sequence, and the potential for comparative experiments evaluating human performance when viewing fused sequences against naturally occurring fusion processes such as the rattlesnake is highlighted.

TL;DR: An improved artificial immune algorithm is presented and applied to the problems associated with image registration and configurations of multiple sensor systems and results show that the immune algorithm can successfully obtain the global optimum with less computational cost compared to other traditional algorithms.