Title :
Comparison of Several Classifiers for the Detection of Polluting Smokes
Author :
Gacquer, D. ; Delmotte, F. ; Delcroix, V. ; Piechowiak, Sylvain
Author_Institution :
LAMIH, Univ. de Valenciennes et du Hainaut-Cambresis, Valenciennes
fDate :
Nov. 28 2006-Dec. 1 2006
Abstract :
This paper addresses the pollution detection problem by using a camera and analyzing the pictures. A camera is used to record visual scenes around complex plants. Then several signals are computed to describe the pictures. Our aim is to detect among the various clouds if there are polluting smokes. We assume in this paper that the signals are useful to classify the clouds and that we do not need other data. In this paper two types of classifiers are studied: Bayesian networks and a k-nearest neighbour classifier.
Keywords :
air pollution measurement; belief networks; environmental science computing; image classification; smoke; video signal processing; Bayesian network; cloud classification; complex plant; k-nearest neighbour classifier; smoke pollution detection problem; video camera; visual scene classification; Bayesian methods; Cameras; Clouds; Collaboration; Computational intelligence; Computer networks; Layout; Pollution; Smoke detectors; Visual databases;
Conference_Titel :
Computational Intelligence for Modelling, Control and Automation, 2006 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7695-2731-0
DOI :
10.1109/CIMCA.2006.73
