Showing papers in "Information Fusion in 2015"

TL;DR: A general image fusion framework by combining MST and SR to simultaneously overcome the inherent defects of both the MST- and SR-based fusion methods is presented and experimental results demonstrate that the proposed fusion framework can obtain state-of-the-art performance.

TL;DR: A novel image fusion method for multi-focus images with dense scale invariant feature transform (SIFT) that shows the great potential of image local features such as the dense SIFT used for image fusion.

TL;DR: This paper presents a novel multi-focus image fusion method in spatial domain that utilizes a dictionary which is learned from local patches of source images and outperforms existing state-of-the-art methods, in terms of visual and quantitative evaluations.

TL;DR: C-SPINE, a framework for Collaborative BSNs (CBSNs), is proposed and natively supports multi-sensor data fusion among CBSNs to enable joint data analysis such as filtering, time-dependent data integration and classification.

TL;DR: This survey focuses on the multi-source domain adaptation problem where there is more than one source domain available together with only one target domain, and examines how to select good sources and samples for the adaptation.

TL;DR: Three types of fusion approaches are presented: the indirect approach, the optimization-based approach and the direct approach to solve GDM problems with heterogeneous preference structures.

TL;DR: An effective quadtree decomposition strategy is presented and the new weighted focus-measure performs better than the commonly used focus-measures on the detection of the focused regions, since it is sensitive to the homogeneous regions.

TL;DR: Simulation results are presented to show that ROL/NDC gives a higher network lifetime than other similar schemes, such Mires++.

TL;DR: A path forward is proposed to advance the research on ocular recognition by improving the sensing technology, heterogeneous recognition for addressing interoperability, utilizing advanced machine learning algorithms for better representation and classification, and developing algorithms for ocular Recognition at a distance.

TL;DR: This survey aims to provide a comprehensive status of recent and current research on context-based Information Fusion (IF) systems, tracing back the roots of the original thinking behind the development of the concept of "context" and discussing the current strategies and techniques.

TL;DR: A Minimized Variance Model and an Entropy Weight Model are proposed to determine the expert weights in the cluster and the cluster weights, respectively, and synthesize these two types of weights into the final objective weights of the CMALGDM experts.

TL;DR: A novel interval-valued intuitionistic fuzzy (IVIF) mathematical programming method for hybrid MCGDM considering alternative comparisons with hesitancy degrees, which is solved by the technically developed linear goal programming approach.

TL;DR: The concept of a human-centric wireless sensor network is introduced, as an infrastructure that supports the capture and delivery of shared information in the field, and helps increase the information availability, and therefore, the efficiency and effectiveness of the emergency response process.

TL;DR: The proposed approach is applied to solve the practical decision making problem concerned with the selection of Strategic Freight Forwarder of China Southern Airlines, and a comparison analysis with a similar approach is conducted to demonstrate the advantages of the proposed method.

TL;DR: This paper proposes a novel technique which is a joint of pixel-level and feature-level fusion at the top-level’s wavelet sub-bands for face recognition, and proposes two alternating direction methods to solve the corresponding optimization problems for finding transformation matrices of dimension reduction and optimal fusion coefficients of the high frequency waveletSub-bands.

TL;DR: A personalized travel planning system that simultaneously considers all categories of user requirements and provides users with a travel schedule planning service that approximates automation and has better performance on the schedule adjustment, personalization, and feedback giving.

TL;DR: A new MAS specially designed to manage data from WSNs, which was tested in a residential home for the elderly, and is based on virtual organizations, and incorporates social behaviors to improve the information fusion processes.

TL;DR: In this article, Markov Logic Networks (MLNs) are used for encoding uncertain knowledge and compute inferences according to observed evidence, and a mechanism to evaluate the level of completion of complex events is presented.

TL;DR: The proposed method uses the approach of likelihood-based outranking comparisons to address multiple criteria decision analysis (MCDA) problems based on interval type-2 trapezoidal fuzzy numbers and introduces the concepts of lower and upper likelihoods for acquiring the likelihood of an IT2TrF binary relationship.

TL;DR: This paper proposes a robust solution based on the use of multimodal sensor fusion that considerably improves the effectiveness of the algorithm and reduces computation time when compared with the classical inverse perspective mapping.

TL;DR: Trajectory-level analysis indicates that the proposed adaptive MCS for partially-supervised learning of facial models over time allows for robust spatio-temporal video-to-video FR, and may therefore enhance security and situation analysis in video surveillance.

TL;DR: An energy-efficient image prioritization framework is presented to cope with the fragility of traditional WVSNs and demonstrates the usefulness of the proposed method in terms of salient event coverage and reduced computational and transmission costs, as well as in helping analysts find semantically relevant visual information.

TL;DR: This paper introduces various Nystrom methods, reviews different sampling methods for the Nystrom method and summarize them from the perspectives of both theoretical analysis and practical performance, and discusses some open machine learning problems related to Nystrom Methods.

TL;DR: A new hierarchical routing algorithm with high energy efficiency named EESSC is proposed which is based on the improved HAC clustering approach, and a re-cluster mechanism is designed to dynamic adjust the result of clustering to make sensor nodes organization more reasonable.

TL;DR: Seven knowledge fusion patterns have been discovered: simple fusion, extension, instantiated fusion, configured fusion, adaptation, flat fusion, and historical fusion.

TL;DR: This paper introduces the semi-uninorm based ordered weighted averaging (SUOWA) operators, a new class of aggregation functions that, as WOWA operators, simultaneously generalize weighted means and OWA operators.

TL;DR: This paper proposes a low-complexity distributed data replication mechanism to increase the resilience of WSN-based distributed storage at large scale and proposes a simple, yet accurate, analytical modeling framework and an extensive simulation campaign, which complement experimental results on the SensLab testbed.

TL;DR: The results demonstrate that the generative model is able to model both spatial and temporal datasets and may be used for the purpose of developing and evaluating counter-piracy methods and algorithms.

TL;DR: A novel approach for crowd density measure, in which local information at pixel level substitutes a global crowd level or a number of people per-frame, is proposed, which demonstrates good performances for detection, tracking, behavior analysis, and privacy preservation.

TL;DR: A fusion system for context-based situation and threat assessment with application to harbor surveillance and Belief-based Argumentation to evaluate the threat posed by suspicious vessels is proposed.