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2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Boston, MA, USA
June 7, 2015 to June 12, 2015
ISSN: 1063-6919
ISBN: 978-1-4673-6963-3
pp: 5117-5125
Ching L. Teo , Computer Vision Lab, University of Maryland, College Park, 20742, USA
Cornelia Fermuller , Computer Vision Lab, University of Maryland, College Park, 20742, USA
Yiannis Aloimonos , Computer Vision Lab, University of Maryland, College Park, 20742, USA
ABSTRACT
A method for efficient border ownership assignment in 2D images is proposed. Leveraging on recent advances using Structured Random Forests (SRF) for boundary detection [8], we impose a novel border ownership structure that detects both boundaries and border ownership at the same time. Key to this work are features that predict ownership cues from 2D images. To this end, we use several different local cues: shape, spectral properties of boundary patches, and semi-global grouping cues that are indicative of perceived depth. For shape, we use HoG-like descriptors that encode local curvature (convexity and concavity). For spectral properties, such as extremal edges [28], we first learn an orthonormal basis spanned by the top K eigenvectors via PCA over common types of contour tokens [23]. For grouping, we introduce a novel mid-level descriptor that captures patterns near edges and indicates ownership information of the boundary. Experimental results over a subset of the Berkeley Segmentation Dataset (BSDS) [24] and the NYU Depth V2 [34] dataset show that our method's performance exceeds current state-of-the-art multi-stage approaches that use more complex features.
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CITATION

C. L. Teo, C. Fermuller and Y. Aloimonos, "Fast 2D border ownership assignment," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015, pp. 5117-5125.
doi:10.1109/CVPR.2015.7299147
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