Journal article
Geometry driven semantic labeling of indoor scenes
Computer Vision – ECCV 2014, Vol.8689, pp.679-694
2014
Abstract
We present a discriminative graphical model which integrates geometrical information from RGBD images in its unary, pairwise and higher order components. We propose an improved geometry estimation scheme which is robust to erroneous sensor inputs. At the unary level, we combine appearance based beliefs defined on pixels and planes using a hybrid decision fusion scheme. Our proposed location potential gives an improved representation of the planar classes. At the pairwise level, we learn a balanced combination of various boundaries to consider the spatial discontinuity. Finally, we treat planar regions as higher order cliques and use graphcuts to make efficient inference. In our model based formulation, we use structured learning to fine tune the model parameters. We test our approach on two RGBD datasets and demonstrate significant improvements over the state-of-the-art scene labeling techniques.
Details
- Title
- Geometry driven semantic labeling of indoor scenes
- Authors/Creators
- S.H. Khan (Author/Creator)M. Bennamoun (Author/Creator)F. Sohel (Author/Creator)R. Togneri (Author/Creator)
- Publication Details
- Computer Vision – ECCV 2014, Vol.8689, pp.679-694
- Publisher
- Springer Verlag
- Identifiers
- 991005543298607891
- Copyright
- 2014 Springer International Publishing Switzerland
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
- Note
- Book: Computer Vision – ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I
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