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Depth extraction from a single image based on block-matching and robust regression

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Title Depth extraction from a single image based on block-matching and robust regression
Title of Series Stereoscopic Displays and Applications XXVII (SD&A 2016)
Part Number 13
Number of Parts 31
Author Jeong, Hyeongju
Contributors Oh, Changjae
Kim, Youngjung
License CC Attribution - NoDerivatives 2.0 UK: England & Wales:
You are free to use, copy, distribute and transmit the work or content in unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
DOI 10.5446/32239
Publisher IS&T Electronic Imaging (EI) Symposium
Release Date 2016
Language English

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Subject Area Information technology
Abstract In this paper, we propose a data-driven approach for automatically estimating a plausible depth map from a single monocular image. Instead of using complicated parametric model, we cast the estimation as a simple yet effective regression problem. We first retrieve semantically similar RGB and depth candidates from database using an activation descriptor. Then, initial estimates are synthesized based on block-matching and robust patch regression. Finally, a weighted median filter (WMF) is adapted to further align depth boundaries to RGB edges. We explicitly take texture-removing technique into consideration for visually plausible results. Experimental results on natural images show that the proposed method outperforms existing approaches in term of both qualitative and quantitative evaluations. © 2016, Society for Imaging Science and Technology (IS&T).

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