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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.
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.
Related paper: [http://www.umiacs.umd.edu/%7Ecteo/public-shared/CVPR15_BorderOwnership_final.pdf C.L. Teo, C. Fermüller, Y. Aloimonos. Fast 2D Border Ownership Assignment. IEEE Conf. on
Related paper: [http://www.umiacs.umd.edu/%7Ecteo/public-shared/CVPR15_BorderOwnership_final.pdf PDF] C.L. Teo, C. Fermüller, Y. Aloimonos. Fast 2D Border Ownership Assignment. IEEE Conf. on
Computer Vision and Pattern Recognition (CVPR), to appear, 2015.]
Computer Vision and Pattern Recognition (CVPR), to appear, 2015.
==Past Semesters==
==Past Semesters==