The Intersection Probability and Its Properties

Cuzzolin, Fabio

doi:10.1007/978-3-642-02906-6_26

Cited by 11 publications

(13 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2) CuzzP: An intersection probability denoted as CuzzP [9] was proposed using the proportional repartition of the total non-specific mass (total non-specific mass ( ) = ∑ ∈2 Θ ,| | ( )) for each contribution of the non-specific masses involved. CuzzP is defined by (∅) = 0, and for any singleton ∈ Θ by…”

Section: Classical Probabilistic Transformationsmentioning

confidence: 99%

“…In them, a classical transformation, denoted as BetP [4], which offers a good compromise between the maximum of credibility (Bel) and the maximum of plausibility (Pl) for decision making. Unfortunately, BetP does not provide the highest probabilistic information content (PIC) [7]; Sudano [8] also proposed series of alternatives and principles of these similar to BetP, which were called PrPl, PrBel and PrHyb; CuzzP [9], which was proposed by Cuzzolin in the framework of DST in 2009, showed its ability of probabilistic transformation; Another novel transformation was proposed by Dezert and Smarandache in the framework of DSmT (free DSm model, hybrid DSm model or Shafer's model), which was called DSmP [7] and comprehensive comparisons have been made in [7] to prove the capabilities of DSmP in probabilistic transformation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new probabilistic transformation based on evolutionary algorithm for decision making

Dong

Dezert³

2017

2017 20th International Conference on Information Fusion (Fusion)

View full text Add to dashboard Cite

The study of alternative probabilistic transformation (PT) in DS theory has emerged recently as an interesting topic, especially in decision making applications. These recent studies have mainly focused on investigating various schemes for assigning both the mass of compound focal elements to each singleton in order to obtain Bayesian belief function for realworld decision making problems. In this paper, work by us also takes inspiration from both Bayesian transformation camps, with a novel evolutionary-based probabilistic transformation (EPT) to select the qualified Bayesian belief function with the maximum value of probabilistic information content (PIC) benefiting from the global optimizing capabilities of evolutionary algorithms. Verification of EPT is carried out by testing it on a set of numerical examples on 4D frames. On each problem instance, comparisons are made between the novel method and those existing approaches, which illustrate the superiority of the proposed method in this paper. Moreover, a simple constraint-handling strategy with EPT is proposed to tackle target type tracking (TTT) problem, simulation results of the constrained EPT on TTT problem prove the rationality of this modification.

show abstract

Section: Classical Probabilistic Transformationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new probabilistic transformation based on evolutionary algorithm for decision making

Dong

Dezert³

2017

2017 20th International Conference on Information Fusion (Fusion)

View full text Add to dashboard Cite

show abstract

“…Other approaches for that end are the intersection probability [46], the orthogonal projection and the relative plausibility [47].…”

Section: Dempster-shafer Theory Of Evidencementioning

confidence: 99%

Keypoint descriptor fusion with Dempster–Shafer theory

Mondéjar-Guerra

Muñoz-Salinas

Marín-Jiménez

et al. 2015

International Journal of Approximate Reasoning

View full text Add to dashboard Cite

Keypoint matching is the task of accurately finding the location of a scene point in two images. Many keypoint descriptors have been proposed in the literature aiming at providing robustness against scale, translation and rotation transformations, each having advantages and disadvantages. This paper proposes a novel approach to fuse the information from multiple keypoint descriptors using Dempster-Shafer Theory of evidence [1], which has proven particularly efficient in combining sources of information providing incomplete, imprecise, biased, and conflictive knowledge. The matching results of each descriptor are transformed into an evidence distribution on which a confidence factor is computed making use of its entropy. Then, the evidence distributions are fused using Dempster-Shafer Theory (DST), considering its confidence. As result of the fusion, a new evidence distribution that improves the result of the best descriptor is obtained. Our method has been tested with SIFT, SURF, ORB, BRISK and FREAK descriptors using all possible combinations of them. Results on the Oxford keypoint dataset [2] shows that the proposed approach obtains an improvement of up to 10% compared to the best one (FREAK).

show abstract

“…Besides the commonly employed decision measurements, several others are proposed in the literature, such as Sudano's [48], Cuzzolin's [49] or the Dezert Smarandache probabilities [50]. All of them consist in the transformation of bbms into probabilities to make a decision.…”

Section: Decision Makingmentioning

confidence: 99%

Fusing navigation and vision information with the Transferable Belief Model: Application to an intelligent speed limit assistant

Daniel

Lauffenburger

2014

Information Fusion

View full text Add to dashboard Cite

The present paper focuses on the fusion, based on imprecise and uncertain information, between a Geographic Information System (GIS) and a Speed Limit Sign Recognition System (SLSRS), performed on camera images. This study is dedicated to the development of a Speed Limit Assistant (SLA) in the context of vehicle driving aid. The proposed SLA is developed within the Evidence Theory framework. The information from the sources is interpreted as belief functions using a non antagonistic bba in the Transferable Belief Model (TBM) semantics. This bba ensures that the conflict which could appear after the global fusion is exclusively due to source discordances. The present paper proposes a way to manage these discordances by formalizing a conflictrelated constraint decision rule. As far as the application is concerned, a two-level (decentralized) fusion architecture is developed. The sensor relevancy is estimated in a first step, followed by the GIS intra-sensor fusion with a maximum of Credibility decision which determines the context-compliant speed candidate considering the road information given by the digital map. This allows the detection of possible errors of the GIS. The multi-sensor fusion then combines the GIS and SLSRS information considering that the sensors are independent and specialized in one speed, each. For the decision, two strategies are adopted. The first one considers the conflict as a threshold for the final speed selection, and so allows the SLA to stay undecided in case of highly conflicting situations. The second strategy employs the 5 th version of the Proportional Conflict Redistribution operator. The SLA has been tested in simulation and in real-time experiments by qualitative and quantitative performance evaluations.

show abstract

The Intersection Probability and Its Properties

Cited by 11 publications

References 12 publications

A new probabilistic transformation based on evolutionary algorithm for decision making

A new probabilistic transformation based on evolutionary algorithm for decision making

Keypoint descriptor fusion with Dempster–Shafer theory

Fusing navigation and vision information with the Transferable Belief Model: Application to an intelligent speed limit assistant

Contact Info

Product

Resources

About