DocumentCode :
2503174
Title :
Super-Resolution Texture Mapping from Multiple View Images
Author :
Iiyama, Masaaki ; Kakusho, Koh ; Minoh, Michihiko
Author_Institution :
Kyoto Univ., Kyoto, Japan
fYear :
2010
fDate :
23-26 Aug. 2010
Firstpage :
1820
Lastpage :
1823
Abstract :
This paper presents an artifact-free super resolution texture mapping from multiple-view images. The multiple-view images are upscaled with a learning-based super resolution technique and are mapped onto a 3D mesh model. However, mapping multiple-view images onto a 3D model is not an easy task, because artifacts may appear when different upscaled images are mapped onto neighboring meshes. We define a cost function that becomes large when artifacts appear on neighboring meshes, and our method seeks the image-and mesh assignment that minimizes the cost function. Experimental results with real images demonstrate the effectiveness of our method.
Keywords :
image resolution; image texture; learning (artificial intelligence); mesh generation; realistic images; solid modelling; 3D mesh model; artifact-free super resolution texture mapping; cost function; image-and mesh assignment; learning-based super resolution technique; multiple view images; neighboring meshes; real images; super-resolution texture mapping; upscaled images; Computational modeling; Cost function; Image resolution; Minimization; Signal resolution; Solid modeling; Three dimensional displays; graph cut; super resolution; texture mapping;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
ISSN :
1051-4651
Print_ISBN :
978-1-4244-7542-1
Type :
conf
DOI :
10.1109/ICPR.2010.449
Filename :
5597208
Link To Document :
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