Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution

In this paper, we present a joint iterative anaglyph stereo matching and colorization framework for obtaining a set of disparity maps and colorized images. Conventional stereo matching algorithms fail when addressing anaglyph images that do not have similar intensities on their two respective view i...

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Bibliographic Details
Main Authors: Williem (Author), Park, In Kyu (Author), Raskar, Ramesh (Contributor)
Other Authors: Massachusetts Institute of Technology. Media Laboratory (Contributor), Program in Media Arts and Sciences (Massachusetts Institute of Technology) (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2016-10-19T14:49:20Z.
Subjects:
Online Access:Get fulltext
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042 |a dc 
100 1 0 |a Williem  |e author 
100 1 0 |a Massachusetts Institute of Technology. Media Laboratory  |e contributor 
100 1 0 |a Program in Media Arts and Sciences   |q  (Massachusetts Institute of Technology)   |e contributor 
100 1 0 |a Raskar, Ramesh  |e contributor 
700 1 0 |a Park, In Kyu  |e author 
700 1 0 |a Raskar, Ramesh  |e author 
245 0 0 |a Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution 
260 |b Institute of Electrical and Electronics Engineers (IEEE),   |c 2016-10-19T14:49:20Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/104847 
520 |a In this paper, we present a joint iterative anaglyph stereo matching and colorization framework for obtaining a set of disparity maps and colorized images. Conventional stereo matching algorithms fail when addressing anaglyph images that do not have similar intensities on their two respective view images. To resolve this problem, we propose two novel data costs using local color prior and reverse intensity distribution factor for obtaining accurate depth maps. To colorize an anaglyph image, each pixel in one view is warped to another view using the obtained disparity values of non-occluded regions. A colorization algorithm using optimization is then employed with additional constraint to colorize the remaining occluded regions. Experimental results confirm that the proposed unified framework is robust and produces accurate depth maps and colorized stereo images. 
520 |a National Research Foundation of Korea (Basic Science Research Program (Ministry of Education, NRF-2012R1A1A2009495)) 
520 |a National Research Foundation of Korea (Korea government (MSIP), grant No. NRF-2013R1A2A2A01069181) 
546 |a en_US 
655 7 |a Article 
773 |t 2015 IEEE International Conference on Computer Vision (ICCV)