Showing papers in "Information Fusion in 2001"

TL;DR: A relatively detailed study is presented indicating that the color distortion problem arises from the change of the saturation during the fusion process, and PCA, BT, and WT are evaluated and found to be IHS-like image merging techniques.

TL;DR: A principled distance between two basic probability assignments (BPAs) (or two bodies of evidence) based on a quantification of the similarity between sets is introduced based on the evidential theory of Dempster–Shafer.

TL;DR: An approach that fuses images with diverse focuses by first decomposing the source images into blocks and then combining them by the use of spatial frequency is presented.

TL;DR: The sensor-fusion results obtained from measurements within the European research project ground explosive ordinance detection (GEODE) system that strives for the realisation of a vehicle-mounted, multi-sensor, anti-personnel landmine-detection system for humanitarian de-mining are presented.

TL;DR: Test results show a significant improvement in the integrated vision and sound localization (IVSL) system's ability over that of the stand-alone microphone-array based sound localization system to accurately localize sound sources in low signal to noise situations.

TL;DR: The use of prior behavior of a classifier, as measured by the confusion matrix, can yield useful information for merging multiple classifiers, and response vectors can be estimated and a ranking of possible classes can be produced which can allow Borda type reconciliation methods to be applied.

TL;DR: The performance comparison between M3CS and Kuncheva's CS+DT method, as well as some simple aggregation methods such as maximum, minimum, average, and majority vote, confirms the validity of the proposed scheme.

TL;DR: An edge-based image registration approach is developed to guide the intensity-based registration which uses optical flow estimation, and it is demonstrated that the approach proves useful for registering some multi-spectral images.

TL;DR: This paper proposes a model formalism for the sensor reliability in a context that leads to two methods of integration when all the hypotheses, associated to the objects of the scene acquired by sensors, are previously learned: the first one models the integration of this further information in the fusion rule as degrees of trust and the second models the sensors reliability directly as probability mass.

TL;DR: Two binary labelling techniques for decision-level fusion are considered for reducing correlation in the context of multiple classifier systems and a method based on error correcting coding that uses binary code words to decompose a multi-class problem into a set of complementary two-class problems is described.

TL;DR: The proposed KBNNF bases its confidence and reliability on the analysis of variance (ANOVA)-based edge detector that can address two important issues of edge based image fusion well: the difference in edge position among the images because of the different characteristics of the images and the error in the image registration process.

TL;DR: This work develops a method for partitioning the M-ary object space and obtains the optimal decision rules that the fusion center and the sensors employ at the internal nodes of the binary decision tree.

TL;DR: An architecture for refining the fusion for building a more accurate picture of the unfolding battlespace uses a central processing center to fuse tracks from multiple tracking centers with a cognitive approach that associates local tracks with central tracks and refines estimates via the authors' new fuzzy clustering algorithm.

TL;DR: This article examines hierarchical (tree-like) models for composite systems and considers the following: traditional multi-criteria evaluation approaches; morphological clique problem with special discrete spaces for evaluation of system excellence; and generalization of morphologicalClique problem.

TL;DR: A hybrid learning scheme which combines a modified adaptive spline modelling algorithm and the expectation-maximisation algorithm is developed for FLL modelling, based on which Kalman filter type algorithms for state estimation and multi-sensor data fusion are investigated.

TL;DR: A framework for constructing flexible, robust and efficient software applications for multisensor fusion system (MFS) is described, which consists of four levels from bottom to top: sensor driver level, logical sensor level, fusion unit level and task unit level.

TL;DR: A new combination rule called the progressive rule, which is a refinement of the adaptive rule proposed by Dubois and Prade in order to tackle robustness with respect to shape modelling and handle consistently erroneous data, is put forward.

TL;DR: A general structure and a new algorithm are proposed to solve problems as making synergy decision between infrared search and track (IRST) and an intermittent-working radar to reduce the opportunity for the radar being locked-on by the adverse ESM, retrieving target range information after the radar is turned off, and detecting target maneuver.

TL;DR: This paper shows an example of developing a fusion system in a formal framework, i.e., through the use of formal operators in the development process, and compares the performance of a system developed according to the approach with a more traditional system.

TL;DR: The method lies in the feature description of the object and the solution uses an extended Kalman filter which fuses information from each sensor to identify the 3D structure and motion of an object.

TL;DR: Three different methods using machine vision techniques for automatically determining the speed of cell rotation from video image sequences are reported, however the use of Estimation Fusion methods to combine the techniques yields a system that performs robustly on all test cases.