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               <dc:title>Monocular depth ordering using perceptual occlusion cues</dc:title>
               <dc:creator>Rezaeirowshan, Babak</dc:creator>
               <dc:creator>Ballester, Coloma</dc:creator>
               <dc:creator>Haro Ortega, Gloria</dc:creator>
               <dc:subject>Monocular depth</dc:subject>
               <dc:subject>Ordinal depth</dc:subject>
               <dc:subject>Depth layering</dc:subject>
               <dc:subject>Occlusion reasoning</dc:subject>
               <dc:subject>Convexity</dc:subject>
               <dc:subject>T-junctions</dc:subject>
               <dc:subject>Boundary ownership</dc:subject>
               <dc:subject>2.1D</dc:subject>
               <dc:description>Comunicació presentada al congrés International Conference on Computer Vision Theory and Applications  celebrat del 27 al 29 de febrer de 2016 a Roma, Itàlia.</dc:description>
               <dc:description>In this paper we propose a method to estimate a global depth order between the objects of a scene using information from a single image coming from an uncalibrated camera. The method we present stems from early vision cues such as occlusion and convexity and uses them to infer both a local and a global depth order. Monocular occlusion cues, namely, T-junctions and convexities, contain information suggesting a local depth order between neighbouring objects. A combination of these cues is more suitable, because, while information conveyed by T-junctions is perceptually stronger, they are not as prevalent as convexity cues in natural images. We propose a novel convexity detector that also establishes a local depth order. The partial order is extracted in T-junctions by using a curvature-based multi-scale feature. Finally, a global depth order, i.e., a full order of all shapes that is as consistent as possible with the computed partial orders that can tolerate conflicting partial or ders is computed. An integration scheme based on a Markov chain approximation of the rank aggregation problem is used for this purpose. The experiments conducted show that the proposed method compares favorably with the state of the art.</dc:description>
               <dc:description>The authors acknowledge partial support by MICINN&#xd;
project, reference MTM2012-30772, and by GRC reference&#xd;
2014 SGR 1301, Generalitat de Catalunya.</dc:description>
               <dc:date>2018-11-22T09:50:48Z</dc:date>
               <dc:date>2018-11-22T09:50:48Z</dc:date>
               <dc:date>2016</dc:date>
               <dc:type>info:eu-repo/semantics/conferenceObject</dc:type>
               <dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
               <dc:relation>Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP) - Volume 4; 2016 Feb 27-29; Rome, Italy. Setúbal: Scitepress; 2016.</dc:relation>
               <dc:rights>© 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved</dc:rights>
               <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
               <dc:publisher>SCITEPRESS – Science and Technology Publications, Lda.</dc:publisher>
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