Efficient object segmentation using digital matting for MPEG video sequences

Yao Tsung Jason Tsai, James Jenn-Jier Lien

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

We developed an automatic object segmentation system to separate the foreground objects from the background scene in the MPEG video sequence. The system consists of two modules: the background modeling and updating module and the foreground object extraction module. For the first module and comparing to existing methods, the background model can be constructed no matter whether there exist moving foreground objects or not. In addition, the background model is capable of handling the illumination changes and intrusive but motionless targets by using the short-term approach and long-term approach, respectively, to keep updating the background model. For the second module, the noises and shadows are eliminated and the holes are filled in order to reduce the false and the missing foreground detection components, respectively. Furthermore, one particular function in this module is the automatic digital matting, which can be applied to have visually accurate segmentation result for the foreground objects.

Original languageEnglish
Pages (from-to)591-601
Number of pages11
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3852 LNCS
Publication statusPublished - 2006

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Segmentation
Module
Updating
Lighting
Background Modeling
Moving Objects
Object
Illumination
Model
Target
Background

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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AB - We developed an automatic object segmentation system to separate the foreground objects from the background scene in the MPEG video sequence. The system consists of two modules: the background modeling and updating module and the foreground object extraction module. For the first module and comparing to existing methods, the background model can be constructed no matter whether there exist moving foreground objects or not. In addition, the background model is capable of handling the illumination changes and intrusive but motionless targets by using the short-term approach and long-term approach, respectively, to keep updating the background model. For the second module, the noises and shadows are eliminated and the holes are filled in order to reduce the false and the missing foreground detection components, respectively. Furthermore, one particular function in this module is the automatic digital matting, which can be applied to have visually accurate segmentation result for the foreground objects.

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