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Area-based depth estimation for monochromatic feature-sparse orthographic capture

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Area-based depth estimation for monochromatic feature-sparse orthographic capture

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dc.contributor.author	Li, Yongwei
dc.contributor.author	Scrofani, G.
dc.contributor.author	Sjöström, Marten
dc.contributor.author	Martínez Corral, Manuel
dc.date.accessioned	2019-06-25T12:40:44Z
dc.date.available	2019-06-25T12:40:44Z
dc.date.issued	2018
dc.identifier.citation	Li, Yongwei Scrofani, G. Sjöström, Marten Martínez Corral, Manuel 2018 Area-based depth estimation for monochromatic feature-sparse orthographic capture European Signal Processing Conference 8553336 206 210
dc.identifier.uri	https://hdl.handle.net/10550/70582
dc.description.abstract	With the rapid development of light field technology, depth estimation has been highlighted as one of the critical problems in the field, and a number of approaches have been proposed to extract the depth of the scene. However, depth estimation by stereo matching becomes difficult and unreliable when the captured images lack both color and feature information. In this paper, we propose a scheme that extracts robust depth from monochromatic, feature-sparse scenes recorded in orthographic sub-aperture images. Unlike approaches which rely on the rich color and texture information across the sub-aperture views, our approach is based on depth from focus techniques. First, we superimpose shifted sub-aperture images on top of an arbitrarily chosen central image. To focus on different depths, the shift amount is varied based on the micro-lens array properties. Next, an area-based depth estimation approach is applied to find the best match among the focal stack and generate the dense depth map. This process is repeated for each sub-aperture image. Finally, occlusions are handled by merging depth maps generated from different central images followed by a voting process. Results show that the proposed scheme is more suitable than conventional depth estimation approaches in the context of orthographic captures that have insufficient color and feature information, such as microscopic fluorescence imaging.
dc.language.iso	eng
dc.relation.ispartof	European Signal Processing Conference, 2018, vol. 8553336, p. 206-210
dc.subject	microscòpia
dc.subject	imatges processament
dc.title	Area-based depth estimation for monochromatic feature-sparse orthographic capture
dc.type	journal article	es_ES
dc.date.updated	2019-06-25T12:40:44Z
dc.identifier.doi	10.23919/EUSIPCO.2018.8553336
dc.identifier.idgrec	133435
dc.rights.accessRights	open access	es_ES