IEEE Trans Image Process - Monocular depth ordering using T-junctions and convexity occlusion cues.

Tópicos

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Resumo

This paper proposes a system that relates objects in an image using occlusion cues and arranges them according to depth. The system does not rely on a priori knowledge of the scene structure and focuses on detecting special points, such as T-junctions and highly convex contours, to infer the depth relationships between objects in the scene. The system makes extensive use of the binary partition tree as hierarchical region-based image representation jointly with a new approach for candidate T-junction estimation. Since some regions may not involve T-junctions, occlusion is also detected by examining convex shapes on region boundaries. Combining T-junctions and convexity leads to a system which only relies on low level depth cues and does not rely on semantic information. However, it shows a similar or better performance with the state-of-the-art while not assuming any type of scene. As an extension of the automatic depth ordering system, a semi-automatic approach is also proposed. If the user provides the depth order for a subset of regions in the image, the system is able to easily integrate this user information to the final depth order for the complete image. For some applications, user interaction can naturally be integrated, improving the quality of the automatically generated depth map.

Resumo Limpo

paper propos system relat object imag use occlus cue arrang accord depth system reli priori knowledg scene structur focus detect special point tjunction high convex contour infer depth relationship object scene system make extens use binari partit tree hierarch regionbas imag represent joint new approach candid tjunction estim sinc region may involv tjunction occlus also detect examin convex shape region boundari combin tjunction convex lead system reli low level depth cue reli semant inform howev show similar better perform stateoftheart assum type scene extens automat depth order system semiautomat approach also propos user provid depth order subset region imag system abl easili integr user inform final depth order complet imag applic user interact can natur integr improv qualiti automat generat depth map

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