Link: [http://arxiv.org/abs/1503.08909 Preliminary Version] (To appear in CVPR 2015)
Link: [http://arxiv.org/abs/1503.08909 Preliminary Version] (To appear in CVPR 2015)
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===Fast 2D Border Ownership Assignment===
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Speaker: Ching-Lik Teo
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A method for efficient border ownership assignment in 2D images is proposed. Leveraging on recent advances using Structured Random Forests (SRF) for boundary detection, we impose a novel border ownership structure that detects both boundaries and border ownership at the
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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 (EE), we first learn an orthonormal basis spanned by the top K eigenvectors via PCA over common types of contour tokens from which we reproject the patches to extract the most important spectral features. 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) and the NYU Depth V2 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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Related paper: C.L. Teo, C. Fermüller, Y. Aloimonos. Fast 2D Border Ownership Assignment. IEEE Conf. on
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Computer Vision and Pattern Recognition (CVPR), to appear, 2015.