Co‐occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition-Reference-Cited by-同舟云学术

Co‐occurrence matching of local binary patterns for improving visual adaption and its application to smoke recognition

Published:2018-08-24 Issue:2 Volume:13 Page:178-187
ISSN:1751-9640
Container-title:IET Computer Vision
language:en
Short-container-title:IET Computer Vision

Author:

Yuan Feiniu¹²,Shi Jinting³,Xia Xue²^ORCID,Huang Qinghua⁴^ORCID,Li Xuelong⁵

Affiliation:

1. College of Information, Mechanical and Electrical Engineering, Shanghai Normal UniversityShanghai201418People's Republic of China

2. School of Information Technology, Jiangxi University of Finance and EconomicsNanchang330032People's Republic of China

3. Vocational School of Teachers and Technology, Jiangxi Agricultural UniversityNanchang330045People's Republic of China

4. School of Mechanical Engineering, and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical UniversityXi'an710072ShanxiPeople's Republic of China

5. Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of SciencesXi'an710119People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Computer Vision and Pattern Recognition,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2018.5164

Reference72 articles.

1. Video-based smoke detection with histogram sequence of LBP and LBPV pyramids

2. An effective algorithm to detect both smoke and flame using color and wavelet analysis

3. QuickBlaze: Early Fire Detection Using a Combined Video Processing Approach

4. Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space

5. A Neural Based Experimental Fire-Outbreak Detection System for Urban Centres

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