Transmission: a new feature for computer vision based smoke detection
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Fire surveillance method based on quaternionic wavelet features
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Adaptive estimation of visual smoke detection parameters based on spatial data and fire risk index
Computer Vision and Image Understanding
Early smoke detection in video using swaying and diffusion feature
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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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.