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An Autoadaptive Edge-Detection Algorithm for Flame and Fire Image Processing

Qiu, Tian, Yan, Yong, Lu, Gang (2012) An Autoadaptive Edge-Detection Algorithm for Flame and Fire Image Processing. IEEE Transactions on Instrumentation and Measurement, 61 (5). pp. 1486-1493. ISSN 0018-9456. (doi:10.1109/TIM.2011.2175833) (KAR id:29339)

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http://dx.doi.org/10.1109/TIM.2011.2175833

Abstract

The determination of flame or fire edges is the process of identifying a boundary between the area where there is thermochemical reaction and those without. It is a precursor to image-based flame monitoring, early fire detection, fire evaluation, and the determination of flame and fire parameters. Several traditional edge-detection methods have been tested to identify flame edges, but the results achieved have been disappointing. Some research works related to flame and fire edge detection were reported for different applications; however, the methods do not emphasize the continuity and clarity of the flame and fire edges.

A computing algorithm is thus proposed to define flame and fire edges clearly and continuously. The algorithm detects the coarse and superfluous edges in a flame/fire image first and then identifies the edges of the flame/fire and removes the irrelevant artifacts. The autoadaptive feature of the algorithm ensures that the primary symbolic flame/fire edges are identified for different scenarios. Experimental results for different flame images and video frames

proved the effectiveness and robustness of the algorithm.

Item Type: Article
DOI/Identification number: 10.1109/TIM.2011.2175833
Uncontrolled keywords: Edge detection, feature extraction, fire, flame, image edge analysis, image processing, monitoring, shape measurement.
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: J. Harries
Date Deposited: 25 Apr 2012 14:31 UTC
Last Modified: 20 Dec 2021 08:02 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/29339 (The current URI for this page, for reference purposes)

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