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Pavement scene interpretation and obstacle detection by large margin image labeling

Conference Paper


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Abstract


  • This paper presents a novel discriminative approach for pave-ment scene understanding and obstacle detection in real-world images. It overcomes the heavy constraints in previous systems such as a simple background, a specic obstacle, etc. The approach we exploited extends the bundle method to incorporate pairwise correlations among neighboring pixels, and adopts graph-cuts as the inference engine to attain the approximation efficiently. A set of robust features on both local and multi-scale level is also introduced that captures the general statistical properties of pavements and obstacles. The proposed approach is validated on real-world image database, and outperforms the current state-of-the-art visioned-based methods

Authors


  •   Jia, Ke (external author)
  •   Liu, Nianjun (external author)
  •   Wang, Lei
  •   Cheng, Li (external author)

Publication Date


  • 2009

Citation


  • Jia, K., Liu, N., Wang, L. & Cheng, L. (2009). Pavement scene interpretation and obstacle detection by large margin image labeling. International Workshop on Vision and Control for Access Space (VCAS), in conjunction with the 9th Asian Conference on Computer Vision (pp. 1-10).

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1612&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/606

Start Page


  • 1

End Page


  • 10

Abstract


  • This paper presents a novel discriminative approach for pave-ment scene understanding and obstacle detection in real-world images. It overcomes the heavy constraints in previous systems such as a simple background, a specic obstacle, etc. The approach we exploited extends the bundle method to incorporate pairwise correlations among neighboring pixels, and adopts graph-cuts as the inference engine to attain the approximation efficiently. A set of robust features on both local and multi-scale level is also introduced that captures the general statistical properties of pavements and obstacles. The proposed approach is validated on real-world image database, and outperforms the current state-of-the-art visioned-based methods

Authors


  •   Jia, Ke (external author)
  •   Liu, Nianjun (external author)
  •   Wang, Lei
  •   Cheng, Li (external author)

Publication Date


  • 2009

Citation


  • Jia, K., Liu, N., Wang, L. & Cheng, L. (2009). Pavement scene interpretation and obstacle detection by large margin image labeling. International Workshop on Vision and Control for Access Space (VCAS), in conjunction with the 9th Asian Conference on Computer Vision (pp. 1-10).

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1612&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/606

Start Page


  • 1

End Page


  • 10