디지털콘텐츠학회 논문지 (Journal of Digital Contents Society)

  • 제18권8호
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  • Pages.1635-1640
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  • 2017
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  • 1598-2009(pISSN)
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  • 2287-738X(eISSN)
한국디지털콘텐츠학회 (Digital Contents Society)
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DOI QR코드

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싱글 야외 영상에서 계층적 이미지 트리 모델과 k-평균 세분화를 이용한 날씨 분류와 안개 검출

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

  • 박기홍 (목원대학교 융합컴퓨터미디어학부)
  • Park, Ki-Hong (Division of Convergence Computer & Media, Mokwon University)
  • 투고 : 2017.12.13
  • 심사 : 2017.12.25
  • 발행 : 2017.12.31
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초록

본 논문에서는 싱글 야외 영상에서 날씨 분류를 위한 계층적 이미지 트리 모델을 정의하고, 영상의 밝기와 k-평균 세분화 영상을 이용한 날씨 분류 알고리즘을 제안하였다. 계층적 이미지 트리 모델의 첫 번째 레벨에서 실내와 야외 영상을 구분하고, 두 번째 레벨에서는 야외 영상이 주간, 야간 또는 일출/일몰 영상인지를 밝기 영상과 k-평균 세분화 영상을 이용하여 판단하였다. 마지막 레벨에서는 두 번째 레벨에서 주간 영상으로 분류된 경우 에지 맵과 안개 율을 기반으로 맑은 영상 또는 안개 영상인지를 최종 추정하였다. 실험 결과, 날씨 분류가 설계 규격대로 수행됨을 확인할 수 있었으며, 제안하는 방법이 주어진 영상에서 효과적으로 날씨 특징이 검출됨을 보였다.

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.

키워드

참고문헌

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