Classification of Fog Situations Based on Gaussian Mixture Model

被引：0

作者：

Wan, Jinjin ^{[1
,2
]}

Qiu, Zhenan ^{[1
]}

Gao, Haifeng ^{[1
]}

Jie, Feiran ^{[1
]}

Peng, Qunnie ^{[1
]}

机构：

[1] Sci & Technol Electroopt Control Lab, Luoyang 471000, Peoples R China

[2] AVIC, Luoyang Inst Electroopt Equipment, Luoyang 471000, Peoples R China

来源：

PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017) | 2017年

关键词：

fog image; non fog image; unsupervised learning; feature extraction;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a learning approach is proposed to classify the fog situations into no fog, fog and dense fog three types. Feature vectors designed according to the contrast and details of foggy images are extracted to form the training set. By using the Gaussian Mixture Model to model the probability density of three situations and learning the parameters of the model with the expectation maximization algorithm, the cluster center as well as the model parameters can be well estimated. Experimental results show that the proposed approach performs well in all situations and is feasible and effective in fog situations recognition.

引用

页码：10902 / 10906

页数：5

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