CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
A PCA-CCA network for RGB-D object recognition
Sun, Shiying1,2; An, Ning1,2; Zhao, Xiaoguang1; Tan, Min1

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.

KeywordObject Recognition Pcanet 3d Perception Canonical Correlation Analysis Deep Learning
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61673378 ; (B132011) ; 61421004)
WOS Research AreaRobotics
WOS SubjectRobotics
WOS IDWOS:000422918400001
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.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
Recommended Citation
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).
Files in This Item: Download All
File Name/Size DocType Version Access License
1729881417752820.pdf(734KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Sun, Shiying]'s Articles
[An, Ning]'s Articles
[Zhao, Xiaoguang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Sun, Shiying]'s Articles
[An, Ning]'s Articles
[Zhao, Xiaoguang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Sun, Shiying]'s Articles
[An, Ning]'s Articles
[Zhao, Xiaoguang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 1729881417752820.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.