Presentation 2009-10-16
Smoke Detection Method Based on the Local Hurst Exponent
Takeshi YAMAMICHI, Hidenori MARUTA, Fujio KUROKAWA,
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Abstract(in English) Early fire detection is one of the important issue of safe operations in extensive facilities such as ports, power plants and large factories. For this purpose, smoke detection with image information is effective. However, smoke detection method has difficulties to treat smoke's information due to its characteristics such as a transmissive property, a non-stationary shape and so on. In this study, we propose a smoke detection method based on its characteristics. Smoke is considers as a fractal, and based on the fractal property, we detect smoke in the images. We use Hurst exponent, which is one of the index of fractals, to detect smoke regions in images. Moreover, as smoke is considered as a moving object, we use time-varying information of smoke in image sequences to obtain more accurate results of the extraction of smoke regions.
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Keyword(in English) Smoke Detection / Self-Affine Fractal / Hurst Exponent
Paper # SIP2009-67,IE2009-92
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Committee IE
Conference Date 2009/10/8(1days)
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Registration To Image Engineering (IE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Smoke Detection Method Based on the Local Hurst Exponent
Sub Title (in English)
Keyword(1) Smoke Detection
Keyword(2) Self-Affine Fractal
Keyword(3) Hurst Exponent
1st Author's Name Takeshi YAMAMICHI
1st Author's Affiliation Graduate School of Science and Technology, Nagasaki University()
2nd Author's Name Hidenori MARUTA
2nd Author's Affiliation Information Media Center, Nagasaki University
3rd Author's Name Fujio KUROKAWA
3rd Author's Affiliation Graduate School of Science and Technology, Nagasaki University:Information Media Center, Nagasaki University:Faculty of Engineering, Nagasaki University
Date 2009-10-16
Paper # SIP2009-67,IE2009-92
Volume (vol) vol.109
Number (no) 227
Page pp.pp.-
#Pages 6
Date of Issue