Name
Abstract | ||
|---|---|---|
Smoke is an important sign for early fire detection. Image based detection methods are more useful than other methods which use some special sensor devices. When treating image information of smoke, it is important to consider characteristics of smoke. In this study, we consider that the image information of smoke is a self-affine fractal. We focus on the nature of smoke and present a new smoke detection method based on the fractal property of smoke. We use the Hurst exponent H, which is one of the widely known exponent of fractals. We calculate H of smoke from a relation between H and the wavelet transform of the image. So we detect smoke areas in images with H through the wavelet transform. Moreover, to obtain the accurate detection result, we use the time-accumulation technique to smoke detection results of each image. In experiments, we show the effectiveness of our method with the fractal property of smoke. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5650254 | Image Processing |
Keywords | Field | DocType |
fires,fractals,image motion analysis,smoke,wavelet transforms,Hurst exponent,early fire detection,image based smoke detection,image information,self-affine fractal,wavelet transform,Hurst exponent,fractal,smoke detection | Computer vision,Signal processing,Exponent,Pattern recognition,Computer science,Hurst exponent,Fractal,Smoke,Image based,Artificial intelligence,Fire detection,Wavelet transform | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-7993-1 | 978-1-4244-7993-1 | 7 |
PageRank | References | Authors |
0.59 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hidenori Maruta | 1 | 16 | 7.11 |
| Akihiro Nakamura | 2 | 8 | 2.42 |
| Takeshi Yamamichi | 3 | 7 | 0.59 |
| fujio kurokawa | 4 | 14 | 9.80 |