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Monocular depth ordering using T-junctions and convexity occlusion cues.
IEEE Trans Image Process ; 22(5): 1926-39, 2013 May.
Artigo em Inglês | MEDLINE | ID: mdl-23335666
ABSTRACT
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.
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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Modelos Teóricos Tipo de estudo: Avaliação econômica em saúde Idioma: Inglês Revista: IEEE Trans Image Process Assunto da revista: Informática Médica Ano de publicação: 2013 Tipo de documento: Artigo País de afiliação: Espanha