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記事

1 引用 (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.

元の言語英語
ページ(範囲)21-31
ページ数11
ジャーナルProgress in Informatics
発行部数7
DOI
出版物ステータス出版済み - 1 1 2010

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Stochastic models
Probability density function
Lighting
work environment
Textures
Pixels
experiment
evaluation
segmentation
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Library and Information Sciences

これを引用

Object segmentation under varying illumination : Stochastic background model considering spatial locality. / Tanaka, Tatsuya; Shimada, Atsushi; Arita, Daisaku; Taniguchi, Rin-Ichiro.

:: Progress in Informatics, 番号 7, 01.01.2010, p. 21-31.

研究成果: ジャーナルへの寄稿記事

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