RGBD Camera based Material Recognition via Surface Roughness Estimation

被引:11
|
作者
Kim, Jungjun [1 ]
Lim, Hwasup [2 ]
Ahn, Sang Chul [2 ]
Lee, Seungkyu [1 ]
机构
[1] Kyung Hee Univ, Dept Comp Sci Engn, Seoul, South Korea
[2] KIST, Ctr Imaging Media Res, Seoul, South Korea
基金
新加坡国家研究基金会;
关键词
D O I
10.1109/WACV.2018.00217
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Real world objects can be characterized effectively by their shape, color and material types. Material recognition of an arbitrary object at a distance is an important task for the improvement of object recognition, scene understanding, realistic rendering and various virtual and augmented reality applications. Researchers have tried to recognize material types based on color features, however material type of an object is not completely correlated with its visual appearance. In this paper, we propose a simple but effective surface roughness estimation method using single time-of-flight (ToF) camera. A set of features extracted from the estimated roughness together with conventional color features are used for material type recognition. Experimental results on our material data set with 122 subjects show promising material type recognition results.
引用
收藏
页码:1963 / 1971
页数:9
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