Flame image segmentation using multiscale color and wavelet-based texture features

Bai, Xiaojing and Lu, Gang and Yan, Yong (2017) Flame image segmentation using multiscale color and wavelet-based texture features. Computer Engineering and Applications (Chinese), 53 (9). pp. 213-219. ISSN 1002-8331. (doi:https://doi.org/10.3778/j.issn.1002-8331.1610-0083) (Full text available)

Abstract

Accurate and reliable segmentation of flame images are crucial in vision based monitoring and characterization of flames. It is, however, difficult to maintain the segmentation accuracy while achieving fast processing time due to the impact of the background noise in the images and the variation of operation conditions. To improve the quality of the image segmentation, a flame image segmentation method is proposed based on Multiscale Color and Wavelet-based Textures(MCWT) of the images. By combining the color and texture features, a characteristic matrix is built and then compressed using a local mean method. The outer contour of the flame image under the compressed scale is detected by a cluster technique. Subsequently, the flame edge region under the original scale is determined, following that, the characteristic matrix of the edge region is constructed and classified, and finally, the flame image segmentation is achieved. Flame images captured from an industrial-scale coal-firedtest rig under different operation conditions are segmented to evaluate the proposed method. The test results demonstrate that the performance of segmenting flame images of the proposed method is superior to other traditional methods. It also has been found that the proposed method performs more effectively in segmenting the flame images with Gaussian and pepper and salt noise.

Item Type: Article
Uncontrolled keywords: flame image, image segmentation, color feature, texture feature, wavelet transform
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA166 Instrumentation
T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image Analysis, Image Processing
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering, cybernetics and intelligent systems
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Gang Lu
Date Deposited: 19 Sep 2017 10:35 UTC
Last Modified: 20 Sep 2017 12:44 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/63481 (The current URI for this page, for reference purposes)
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