Object segmentation under varying illumination: Stochastic background model considering spatial locality

Tatsuya Tanaka, Atsushi Shimada, Daisaku Arita, Rin ichiro Taniguchi

研究成果: ジャーナルへの寄稿学術誌査読

1 被引用数 (Scopus)

抄録

We propose a new method for background modeling. Our method is based on the two complementary approaches. One uses the probability density function (PDF) to approximatebackground model. The PDF is estimated non-parametrically by using Parzen density estimation. Then, foreground object is detected based on the estimated PDF. The method is based on the evaluation of the local texture at pixel-level resolution which reduces the effects of variations in lighting. Fusing those approachs realizes robust object detection under varying illumination. Several experiments show the effectiveness of our approach.

本文言語英語
ページ(範囲)21-31
ページ数11
ジャーナルProgress in Informatics
7
DOI
出版ステータス出版済み - 3月 2010

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンス(全般)
  • 図書館情報学

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