3D Texture Recognition for RGB-D Images

被引：0

作者：

Zhong, Guoqiang ^{[1
]}

Mao, Xin ^{[1
]}

Shi, Yaxin ^{[1
]}

Dong, Junyu ^{[1
]}

机构：

[1] Ocean Univ China, Dept Comp Sci & Technol, Qingdao 266100, Peoples R China

来源：

COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2015, PT II | 2015年 / 9257卷

关键词：

3D object recognition; RGB-D images; 3D texture recognition; 3D texture reconstruction; Metric learning; CLASSIFICATION; SCALE;

D O I：

10.1007/978-3-319-23117-4_45

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present a novel 3D object recognition system. In this system, we capture both the color and depth information of 3D objects using Kinect, and represent them in RGB-D images. To alleviate the deformations and partial defects of the obtained 3D surface textures, 3D texture reconstruction techniques are applied. In order to improve the recognition accuracy, we exploit metric learning methods for the K-nearest neighbor (KNN) classifier. Promising results are obtained on a real-world 3D object recognition application.

引用

页码：518 / 528

页数：11

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