Object segmentation under varying illumination

Stochastic background model considering spatial locality

Tatsuya Tanaka, Atsushi Shimada, Daisaku Arita, Rin-Ichiro Taniguchi

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)21-31
Number of pages11
JournalProgress in Informatics
Issue number7
DOIs
Publication statusPublished - Jan 1 2010

Fingerprint

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

Cite this

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

In: Progress in Informatics, No. 7, 01.01.2010, p. 21-31.

Research output: Contribution to journalArticle

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