NDLI: Edge-based depth gradient refinement for 2D to 3D learned prior conversion

Content Provider	IEEE Xplore Digital Library
Author	Herrera, J.L. del-Blanco, C.R. Garcıa, N.
Copyright Year	2015
Description	Author affiliation: Grupo de Tratamiento de Imagenes, Univ. Politec. de Madrid, Madrid, Spain (Herrera, J.L.; del-Blanco, C.R.; Garcıa, N.)
Abstract	2D-to-3D conversion is an important task for reducing the current gap between the number of 3D displays and the available 3D content. Here, we present an automatic 2D-to-3D image conversion approach based on machine learning principles. Stemming from the hypothesis that images with a similar structure have likely a similar 3D structure, the depth of a query color image is estimated using a color plus depth image dataset. Clusters with common scene structure are computed offline. Then, a matching process is performed to select the cluster centroid which is the most similar to the query image. A prior depth map is computed fusing the depth maps of the images in this cluster. Then, an edge-based post-processing stage is applied to the prior depth map estimation to enhance the final scene depth estimation. Promising results are obtained in two commonly used databases achieving a similar performance to other much complex state-of-the-art approaches.
Starting Page	1
Ending Page	4
File Size	615856
Page Count	4
File Format	PDF
ISBN	9781467380904
ISSN	2161203X
DOI	10.1109/3DTV.2015.7169364
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-08
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement depth maps Three-dimensional displays Databases Image edge detection Estimation Clustering algorithms depth prior Color clustering 2D-to-3D conversion machine learning
Content Type	Text
Resource Type	Article

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