Comparison of Several Classifiers for the Detection of Polluting Smokes

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....

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Bibliographic Details
Main Authors: Gacquer, D., Delmotte, F., Delcroix, V., Piechowiak, S.
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Cameras
Clouds
Collaboration
Computational intelligence
Computer networks
Layout
Pollution
Smoke detectors
Visual databases
Online Access:Request full text
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Summary: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.
DOI:10.1109/CIMCA.2006.73