Knowledge Commons of Institute of Automation,CAS
A PCA-CCA network for RGB-D object recognition | |
Sun, Shiying1,2; An, Ning1,2; Zhao, Xiaoguang1; Tan, Min1 | |
发表期刊 | INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS |
2018-01-17 | |
卷号 | 15期号:1 |
文章类型 | Article |
摘要 | Object recognition is one of the essential issues in computer vision and robotics. Recently, deep learning methods have achieved excellent performance in red-green-blue (RGB) object recognition. However, the introduction of depth information presents a new challenge: How can we exploit this RGB-D data to characterize an object more adequately? In this article, we propose a principal component analysis-canonical correlation analysis network for RGB-D object recognition. In this new method, two stages of cascaded filter layers are constructed and followed by binary hashing and block histograms. In the first layer, the network separately learns principal component analysis filters for RGB and depth. Then, in the second layer, canonical correlation analysis filters are learned jointly using the two modalities. In this way, the different characteristics of the RGB and depth modalities are considered by our network as well as the characteristics of the correlation between the two modalities. Experimental results on the most widely used RGB-D object data set show that the proposed method achieves an accuracy which is comparable to state-of-the-art methods. Moreover, our method has a simpler structure and is efficient even without graphics processing unit acceleration. |
关键词 | Object Recognition Pcanet 3d Perception Canonical Correlation Analysis Deep Learning |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1177/1729881417752820 |
关键词[WOS] | FEATURES ; CLASSIFICATION ; CATEGORY ; SCENE |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | National Natural Science Foundation of China(61673378 ; (B132011) ; 61421004) |
WOS研究方向 | Robotics |
WOS类目 | Robotics |
WOS记录号 | WOS:000422918400001 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/21932 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Sun, Shiying,An, Ning,Zhao, Xiaoguang,et al. A PCA-CCA network for RGB-D object recognition[J]. INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,2018,15(1). |
APA | Sun, Shiying,An, Ning,Zhao, Xiaoguang,&Tan, Min.(2018).A PCA-CCA network for RGB-D object recognition.INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,15(1). |
MLA | Sun, Shiying,et al."A PCA-CCA network for RGB-D object recognition".INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 15.1(2018). |
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