Adaptive fusion architecture for context aware detection and classification

J. Bell; Y. Petillot; K. Lebart; P. Y. Mignotte; E. Coiras; H. Rohou

Adaptive fusion architecture for context aware detection and classification

J. Bell, Y. Petillot, K. Lebart, P. Y. Mignotte, E. Coiras, H. Rohou

School of Engineering & Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This paper presents a framework for the fusion of detection and classification algorithms for side-scan imagery. The framework is based on Dempster-Shafer Theory of Evidence, which permits the fusion of heterogeneous outputs of targets detectors and classifiers. The paper will illustrate how the technique permits the incorporation of contextual information into the decision process, giving more importance to the outputs of those algorithms that perform better in particular mission conditions. © 2007 IEEE.

Original language	English
Title of host publication	OCEANS 2007 - Europe
Publication status	Published - 2007
Event	OCEANS 2007 - Europe - Aberdeen, Scotland, United Kingdom Duration: 18 Jun 2007 → 21 Jun 2007

Conference

Conference	OCEANS 2007 - Europe
Country/Territory	United Kingdom
City	Aberdeen, Scotland
Period	18/06/07 → 21/06/07

Keywords

Classification
Detection
Fusion
Sonar

Cite this

@inproceedings{525434717cd04d458314714eabf107e5,

title = "Adaptive fusion architecture for context aware detection and classification",

abstract = "This paper presents a framework for the fusion of detection and classification algorithms for side-scan imagery. The framework is based on Dempster-Shafer Theory of Evidence, which permits the fusion of heterogeneous outputs of targets detectors and classifiers. The paper will illustrate how the technique permits the incorporation of contextual information into the decision process, giving more importance to the outputs of those algorithms that perform better in particular mission conditions. {\textcopyright} 2007 IEEE.",

keywords = "Classification, Detection, Fusion, Sonar",

author = "J. Bell and Y. Petillot and K. Lebart and Mignotte, {P. Y.} and E. Coiras and H. Rohou",

year = "2007",

language = "English",

isbn = "1424406358",

booktitle = "OCEANS 2007 - Europe",

note = "OCEANS 2007 - Europe ; Conference date: 18-06-2007 Through 21-06-2007",

}

TY - GEN

T1 - Adaptive fusion architecture for context aware detection and classification

AU - Bell, J.

AU - Petillot, Y.

AU - Lebart, K.

AU - Mignotte, P. Y.

AU - Coiras, E.

AU - Rohou, H.

PY - 2007

Y1 - 2007

N2 - This paper presents a framework for the fusion of detection and classification algorithms for side-scan imagery. The framework is based on Dempster-Shafer Theory of Evidence, which permits the fusion of heterogeneous outputs of targets detectors and classifiers. The paper will illustrate how the technique permits the incorporation of contextual information into the decision process, giving more importance to the outputs of those algorithms that perform better in particular mission conditions. © 2007 IEEE.

AB - This paper presents a framework for the fusion of detection and classification algorithms for side-scan imagery. The framework is based on Dempster-Shafer Theory of Evidence, which permits the fusion of heterogeneous outputs of targets detectors and classifiers. The paper will illustrate how the technique permits the incorporation of contextual information into the decision process, giving more importance to the outputs of those algorithms that perform better in particular mission conditions. © 2007 IEEE.

KW - Classification

KW - Detection

KW - Fusion

KW - Sonar

UR - http://www.scopus.com/inward/record.url?scp=36348965302&partnerID=8YFLogxK

M3 - Conference contribution

SN - 1424406358

SN - 9781424406357

BT - OCEANS 2007 - Europe

T2 - OCEANS 2007 - Europe

Y2 - 18 June 2007 through 21 June 2007

ER -

Adaptive fusion architecture for context aware detection and classification

Abstract

Conference

Keywords

Other files and links

Fingerprint

Cite this