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http://dx.doi.org/10.9728/dcs.2017.18.8.1635

Weather Classification and Fog Detection using Hierarchical Image Tree Model and k-mean Segmentation in Single Outdoor Image  

Park, Ki-Hong (Division of Convergence Computer & Media, Mokwon University)
Publication Information
Journal of Digital Contents Society / v.18, no.8, 2017 , pp. 1635-1640 More about this Journal
Abstract
In this paper, a hierarchical image tree model for weather classification is defined in a single outdoor image, and a weather classification algorithm using image intensity and k-mean segmentation image is proposed. In the first level of the hierarchical image tree model, the indoor and outdoor images are distinguished. Whether the outdoor image is daytime, night, or sunrise/sunset image is judged using the intensity and the k-means segmentation image at the second level. In the last level, if it is classified as daytime image at the second level, it is finally estimated whether it is sunny or foggy image based on edge map and fog rate. Some experiments are conducted so as to verify the weather classification, and as a result, the proposed method shows that weather features are effectively detected in a given image.
Keywords
Weather classification; Weather feature; k-mean segmentation; Hierarchical image tree model; Fog detection;
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Times Cited By KSCI : 1  (Citation Analysis)
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