TY - GEN
T1 - An approach of fog detecting magnitude using referenceless perceptual image defogging
AU - Gai, Keke
AU - Sun, Xiaotong
AU - Li, Yujun
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/27
Y1 - 2018/7/27
N2 - The increasing economy is resulting in heavy pollutions and foggy environments in my regions, which raises up a problem of detecting the magnitude of the fog or smog. Creating an alert system to classify the magnitude of fog is for numerous fields, such as the transportation security, manufacturing management, and disease prevention. However, current fog detection solutions require a long training process from feature extractions to classifications. This paper focuses on the issue of the fog detection and proposes a new approach for classifying the magnitude of fog using the technique of image defogging. The proposed method, called Image Defogging-based Detection for Magnitude of Fog (IDDMF), emphasizes the perceptual fog density considering image corpus from both foggy and fog-free images. The experiment evaluations have provided an assessment for demonstrating the performance of the proposed approach.
AB - The increasing economy is resulting in heavy pollutions and foggy environments in my regions, which raises up a problem of detecting the magnitude of the fog or smog. Creating an alert system to classify the magnitude of fog is for numerous fields, such as the transportation security, manufacturing management, and disease prevention. However, current fog detection solutions require a long training process from feature extractions to classifications. This paper focuses on the issue of the fog detection and proposes a new approach for classifying the magnitude of fog using the technique of image defogging. The proposed method, called Image Defogging-based Detection for Magnitude of Fog (IDDMF), emphasizes the perceptual fog density considering image corpus from both foggy and fog-free images. The experiment evaluations have provided an assessment for demonstrating the performance of the proposed approach.
KW - Image defogging
KW - Image processing
KW - Magnitude of fog
KW - Referenceless perceptual image processing
UR - http://www.scopus.com/inward/record.url?scp=85051473108&partnerID=8YFLogxK
U2 - 10.1109/CSCloud/EdgeCom.2018.00020
DO - 10.1109/CSCloud/EdgeCom.2018.00020
M3 - Conference contribution
AN - SCOPUS:85051473108
SN - 9781538658505
T3 - Proceedings - 5th IEEE International Conference on Cyber Security and Cloud Computing and 4th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud/EdgeCom 2018
SP - 58
EP - 63
BT - Proceedings - 5th IEEE International Conference on Cyber Security and Cloud Computing and 4th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud/EdgeCom 2018
A2 - Qiu, Meikang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Cyber Security and Cloud Computing and 4th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud/EdgeCom 2018
Y2 - 22 June 2018 through 24 June 2018
ER -