An adherent raindrop detection method using MSER

Koichi Ito, Kazumasa Noro, Takafumi Aoki

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Image processing algorithms used in surveillance systems are designed to work under good weather conditions. For example, in a rainy day, raindrops are adhered to camera lenses and windshields, resulting in partial occlusions in acquired images, and making performance of image processing algorithms significantly degraded. To improve performance of surveillance systems in a rainy day, raindrops have to be automatically detected and removed from images. Addressing this problem, this paper proposes an adherent raindrop detection method from a single image which does not need training data and special devices. The proposed method employs image segmentation using Maximally Stable Extremal Regions (MSER) and qualitative metrics to detect adherent raindrops from the result of MSER-based image segmentation. Through a set of experiments, we demonstrate that the proposed method exhibits efficient performance of adherent raindrop detection compared with conventional methods.

本文言語English
ホスト出版物のタイトル2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
出版社Institute of Electrical and Electronics Engineers Inc.
ページ105-109
ページ数5
ISBN(電子版)9789881476807
DOI
出版ステータスPublished - 2016 2 19
イベント2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 - Hong Kong, Hong Kong
継続期間: 2015 12 162015 12 19

出版物シリーズ

名前2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015

Other

Other2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
国/地域Hong Kong
CityHong Kong
Period15/12/1615/12/19

ASJC Scopus subject areas

  • 人工知能
  • モデリングとシミュレーション
  • 信号処理

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