40 years of Dempster-Shafer theory | International Journal of Approximate Reasoning skip to
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This paper addresses classification problems in which the class membership of training data
are only partially known. Each learning sample is assumed to consist of a feature vector xi∈ …

A new method is proposed for building a predictive belief function from statistical data in the
transferable belief model framework. The starting point of this method is the assumption that …

This work proposes a novel filtering algorithm that constitutes an extension of Bayesian
particle filters to the Dempster–Shafer theory. Our proposal solves the multi-target problem …

This work proposes a novel shape from silhouette (SfS) algorithm using the Dempster–
Shafer (DS) theory for dealing with inconsistent silhouettes. Standard SfS methods makes …

In this Book we argue that the fruitful interaction of computer vision and belief calculus is
capable of stimulating significant advances in both fields. From a methodological point of …

The combination rule is critical in an evidence based fusion process. The conjunctive rule is
most common eventhough when the cognitive independence–distinctness–assumption is …

This article proposes a method to classify multiple maneuvering targets at the same time.
This task is a much harder problem than classifying a single target, as sensors do not know …

This paper derives a particle filter algorithm within the Dempster–Shafer framework. Particle
filtering is a well-established Bayesian Monte Carlo technique for estimating the current …

Firstly, a multiple model extension of the random finite set (RFS)-based single-target
Bayesian filtering (STBF), referred as MM-STBF, is presented to accommodate the possible …