3D LayoutCRF for multi-view object class recognition and segmentation

Derek Hoiem, Carsten Rother, John Winn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce an approach to accurately detect and segment partially occluded objects in various viewpoints and scales. Our main contribution is a novel framework for combining object-level descriptions (such as position, shape, and color) with pixel-level appearance, boundary, and occlusion reasoning. In training, we exploit a rough 3D object model to learn physically localized part appearances. To find and segment objects in an image, we generate proposals based on the appearance and layout of local parts. The proposals are then refined after incorporating object-level information, and overlapping objects compete for pixels to produce a final description and segmentation of objects in the scene. A further contribution is a novel instance penalty, which is handled very efficiently during inference. We experimentally validate our approach on the challenging PASCAL'06 car database.

Original languageEnglish (US)
Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
PublisherIEEE Computer Society
ISBN (Print)1424411807, 9781424411801
DOIs
StatePublished - Jan 1 2007
Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
Duration: Jun 17 2007Jun 22 2007

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
CountryUnited States
CityMinneapolis, MN
Period6/17/076/22/07

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Hoiem, D., Rother, C., & Winn, J. (2007). 3D LayoutCRF for multi-view object class recognition and segmentation. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 [4270070] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). IEEE Computer Society. https://doi.org/10.1109/CVPR.2007.383045