http://iet.metastore.ingenta.com
1887

Extremely efficient PM2.5 estimator based on analysis of saliency and statistics

Extremely efficient PM2.5 estimator based on analysis of saliency and statistics

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Air pollution is a crucial environmental problem, especially the fine particulate matter (PM2.5) which has become one of the focal points. PM2.5 is a complex pollutant which can intrude the lungs and threaten people's health during the whole lives. In order to enable people to know the PM2.5 index of their surroundings at any time, an image-based PM2.5 predictor with saliency detection (IPPS) is proposed. The proposed predictor first obtains the non-salient regions based on saliency detection technologies. Then, the authors extract two features of the entropy and intensity values of non-salient image saturation map. Finally, they multiply these two features into the approximation of PM2.5 concentration. Experiments show that the proposed IPPS is superior in accuracy and efficiency.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • 6. Gu, K., Qiao, J.-F., Li, X.: ‘Highly efficient picture-based prediction of PM2.5 concentration’, IEEE Trans. Ind. Electron., 2018, in press.
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2018.5613
Loading

Related content

content/journals/10.1049/el.2018.5613
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address