By Topic

An Early Fire Detection Method Based on Smoke Texture Analysis and Discrimination

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)

Texture is an important property of fire smoke, which is a significant signal for early fire detection. This paper describes a method of analyzing the texture of fire smoke combining two innovative texture analysis tools, Wavelet Analysis and Gray Level Cooccurrence Matrices (GLCM). Tree-Structured Wavelet transform is used to represent the textural images and GLCM are used to compute the different scales of the wavelet transform and to extract the features of fire-smoke texture. The smoke texture and the non-smoke texture are classified by neural network classifier. The discrimination performance is related to the quantity of input vectors.

Published in:

Image and Signal Processing, 2008. CISP '08. Congress on  (Volume:3 )

Date of Conference:

27-30 May 2008