Showing papers in "Information Fusion in 2014"

TL;DR: An up-to-date survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems is presented, providing a vision of the spectrum of applications that are currently being developed.

TL;DR: This paper presents an overview of consensus models based on soft consensus measures, showing the pioneering and prominent papers, the main existing approaches and the new trends and challenges.

TL;DR: In this paper, a review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion, concluding that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

TL;DR: The concept of distribution assessments in a linguistic term set is proposed, and the operational laws of linguistic distribution assessments are studied, to provide a theoretic basis for the application of linguistic distributions assessments in group decision making.

TL;DR: Experimental results clearly demonstrate that the proposed method outperforms the conventional fusion methods in the presence of anisotropic blur and mis-registration.

TL;DR: Comparison results prove that performance of the proposed fusion method is better than any of the above existing fusion methods.

TL;DR: A taxonomy that provides an overview and categorization of some existing consensus models for group decision making problems defined in a fuzzy context, taking into account the main features of each model is proposed.

TL;DR: A VDTN routing protocol, called GeoSpray, which takes routing decisions based on geographical location data, and combines a hybrid approach between multiple-copy and single-copy schemes, and improves significantly the delivery probability and reduces the delivery delay.

TL;DR: A novel condition-based monitoring system consisting of six modules: sensing, signal processing, feature extraction, classification, high-level fusion and decision making module, and a relationship between bearing condition and sensor performance has been found.

TL;DR: Visual and statistical analyses demonstrate that the fusion quality can be significantly improved over that of five typical methods in terms of entropy and mutual information, edge information, standard deviation, peak signal to noise and structural similarity.

TL;DR: A novel hybrid financial distress model based on combining the clustering technique and classifier ensembles is developed, which provides higher predication accuracy and lower Type I & II errors.

TL;DR: This paper proposes the idea of combining ''interest groups'' with the practical decision information to classify the decision makers (DMs) in complex multi-attribute large-group decision-making problems in interval-valued intuitionistic fuzzy (IVIF) environment and constructs a partial binary tree DEA-DA cyclic classification model to achieve multiple groups' classification of DMs.

TL;DR: This paper proposes a new fusion algorithm for multi-modal medical images based on MLE that enables the decomposition of input images into coarse and detailed layers in the MLE schema, and utilizes local energy and contrast fusion rules for coefficient selection in the different layers.

TL;DR: Experimental results show that, RWO-Sampling statistically does much better than alternative methods on imbalanced data sets when implementing common baseline algorithms.

TL;DR: The experimental and comparison results show that luminance and contrast is of great importance for image processing and prove that the proposed method is better than all other methods.

TL;DR: According to the visual perceptions and objective evaluations on the fused results, the proposed multi-component fusion method for multi-source images can produce better fused images in the authors' experiments, compared with other single- component fusion methods.

TL;DR: The experimental results show that the proposed multi-biometric template protection scheme demonstrates better verification results as compared to their unibiometric counterparts while preserving template security.

TL;DR: The usage of multivariate orthogonal space transformations and vectorized time-series models in combination with data-driven system identification models to achieve an enhanced performance of residual-based fault detection in condition monitoring systems equipped with multi-sensor networks is introduced.

TL;DR: A novel dependency model, named Explicit Generalized Gaussian Density Dependency (EGGDD) model, is developed by the shift-invariant shearlet transform (SIST), which can produce better fusion results than those of some existing methods by the comparison of visual sense and quantitative measurements.

TL;DR: This study is concerned with information granulation present in the problems of Analytic Hierarchy Process (AHP), which is available in the characterization of a pairwise assessment of alternatives studied in the decision-making problem.

TL;DR: This work proposes a biased, adaptive sink mobility scheme, that adjusts to local network conditions, such as the surrounding density, remaining energy and the number of past visits in each network region, that achieves significantly reduced latency, especially in networks with non-uniform sensor distribution.

TL;DR: A retinal image gradability assessment algorithm based on the fusion of generic image quality indicators is introduced demonstrating very good performance and confirming the usability of the solution in an ambulatory application environment.

TL;DR: The novelty of the proposed method is that, it combines the best features from different texture domains along with their weights and 'weighted z-score' values used to compute a discriminative index for liver classification.

TL;DR: A novel Nearest Neighbor (NN) imputation method that estimates missing data in WSNs by learning spatial and temporal correlations between sensor nodes by utilizing a kd-tree data structure, which is a non-parametric, data-driven binary search tree.

TL;DR: The main contribution of the paper is the definition of a dissimilarity matrix representing the distance of each node to every other node in the network.

TL;DR: This paper proposes a mathematical framework and methodology for group decision-making under multi-granular and multi-attribute linguistic assessments based on distances between linguistic assessments and a degree of consensus.

TL;DR: This paper introduces an algorithm to aggregate the preference relations provided by experts in multi-expert decision making problems and presents a generalization of the concepts of restricted dissimilarity function and distance between sets for the case where the experts are working with a Cartesian product of lattices.

TL;DR: A novel model-based pan-sharpening method via sparse representation and local autoregressive (AR) model is proposed, which demonstrates its good performance in terms of visual analysis and quantitative evaluation.

TL;DR: Empirical results show that the proposed T2-ELM hybrid system outperformed each of type-2 FLS and ELM, as the two constituent models, in all cases, with the improvement made to the ELM performance far higher against that oftype- 2 FLS that had a closer performance to the hybrid since it is already noted for being able to model uncertainties.

TL;DR: An approach is proposed that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks and enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm.