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Content Provider | IEEE Xplore Digital Library |
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Author | Yu Chunyu Zhang Yongming Fang Jun Wang Jinjun |
Copyright Year | 2009 |
Abstract | Since the texture is an important feature of smoke, a novel method of texture analysis is proposed for real-time fire smoke detection. The texture analysis is based on gray level co-occurrence matrices (GLCM) and can distinguish smoke features from other none fire disturbances. For the realization of real-time fire detection, block processing technique is adopted and the computation of texture features is done to every block of image. Neural network is used to classify smoke texture features from none-smoke features and the fire alarm trigger is set according to the total smoke blocks in one frame. The accuracy of the method is discussed as a function of frames in the end. |
Starting Page | 511 |
Ending Page | 515 |
File Size | 556312 |
Page Count | 5 |
File Format | |
ISBN | 9780769538815 |
DOI | 10.1109/WCSE.2009.864 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-10-28 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Video fire smoke detection Image edge detection Shape measurement Image processing Laboratories Real-time detection Image texture analysis Texture Smoke detectors Computer science Neural networks Fires Feature extraction Motion detection |
Content Type | Text |
Resource Type | Article |
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