DocumentCode :
1967207
Title :
Large Space Fire Image Processing of Improving Canny Edge Detector Based on Adaptive Smoothing
Author :
Jiang, Qin ; Wang, Qiang
Author_Institution :
Coll. of Metrol. & Meas. Instrum., China Jiliang Univ., Hangzhou, China
fYear :
2010
fDate :
30-31 Jan. 2010
Firstpage :
264
Lastpage :
267
Abstract :
To avoid the large-scale damage caused by fire occurring in large space building, there are many studies about image-processing technique for automatic real-time flame detection. Adaptive Canny edge algorithm and flame geometric features are combined to inspect flame area of video data generated by an ordinary camera monitoring. Firstly, candidate fire regions are detected using modified Canny operator method which combines adaptive smoothing to detect moving fire regions. Fire regions generally have a higher luminance contrast than neighboring regions, a luminance map is made and used to remove non-fire pixels. The proposed selection criterion is effective in improving the performances of traditional Canny operator. Experimental results showed that the proposed approach was more robust to noise, and help to separate consecutive frames flame area.
Keywords :
building; edge detection; fires; flames; video cameras; Canny edge detector; adaptive Canny edge algorithm; adaptive smoothing; automatic real-time flame detection; camera monitoring; flame geometric features; large space building; large space fire image processing; modified Canny operator method; video data; Cameras; Detectors; Extraterrestrial measurements; Fires; Frequency; Image edge detection; Image processing; Image segmentation; Smoothing methods; Space technology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovative Computing & Communication, 2010 Intl Conf on and Information Technology & Ocean Engineering, 2010 Asia-Pacific Conf on (CICC-ITOE)
Conference_Location :
Macao
Print_ISBN :
978-1-4244-5634-5
Electronic_ISBN :
978-1-4244-5635-2
Type :
conf
DOI :
10.1109/CICC-ITOE.2010.73
Filename :
5439215
Link To Document :
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