OPT OpenIR  > 光电测量技术实验室
Feature Fusion Information Statistics for feature matching in cluttered scenes
Zhou, Wei1; Ma, Caiwen2; Liao, Shenghui6; Shi, Jinjing6; Yao, Tong2,3; Chang, Peng4; Kuijper, Arjan5
Department光电测量技术实验室
2018-12
Source PublicationComputers and Graphics (Pergamon)
ISSN00978493
Volume77Pages:50-64
Contribution Rank2
AbstractObject recognizing in cluttered scenes remains a largely unsolved problem, especially when applying feature matching to cluttered scenes there are many feature mismatches between the scenes and models. We propose our Feature Fusion Information Statistics (FFIS) as the calculation framework for extracting salient information from a Local Surface Patch (LSP) by a Local Reference Frame (LRF). Our LRF is defined on each LSP by projecting the scatter matrix's eigenvectors to a plane which is perpendicular to the normal of the LSP. Based on this, our FFIS descriptor of each LSP is calculated, for which we use the combined distribution of mesh and point information in a local domain. Finally, we evaluate the speed, robustness and descriptiveness of our FFIS with the state-of-the-art methods on several public benchmarks. Our experiments show that our FFIS is fast and obtains a more reliable matching rate than other approaches in cluttered situations. © 2018 Elsevier Ltd
DOI10.1016/j.cag.2018.09.012
Indexed ByEI
Language英语
EI Accession Number20184205947119
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30680
Collection光电测量技术实验室
Corresponding AuthorZhou, Wei
Affiliation1.Multimedia Research Center, Shenzhen Institutes of Advanced Technology of CAS, 1068 Xueyuan Avenue, Shenzhen; 518055, China;
2.Xi'an Institute of Optics and Precision Mechanics of CAS, No.17 Xinxi Road, Xi'an; 7101199, China;
3.University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing; 100049, China;
4.Northeastern University, 360 Huntington Ave., Boston; 02115, United States;
5.Fraunhofer IGD & TU Darmstadt, Fraunhoferstrasse 5, Darmstadt; 64283, Germany;
6.School of Information Science & Engineering, Central South University, Changsha; 410083, China
Recommended Citation
GB/T 7714
Zhou, Wei,Ma, Caiwen,Liao, Shenghui,et al. Feature Fusion Information Statistics for feature matching in cluttered scenes[J]. Computers and Graphics (Pergamon),2018,77:50-64.
APA Zhou, Wei.,Ma, Caiwen.,Liao, Shenghui.,Shi, Jinjing.,Yao, Tong.,...&Kuijper, Arjan.(2018).Feature Fusion Information Statistics for feature matching in cluttered scenes.Computers and Graphics (Pergamon),77,50-64.
MLA Zhou, Wei,et al."Feature Fusion Information Statistics for feature matching in cluttered scenes".Computers and Graphics (Pergamon) 77(2018):50-64.
Files in This Item:
File Name/Size DocType Version Access License
Feature Fusion Infor(6416KB)期刊论文出版稿开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhou, Wei]'s Articles
[Ma, Caiwen]'s Articles
[Liao, Shenghui]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhou, Wei]'s Articles
[Ma, Caiwen]'s Articles
[Liao, Shenghui]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhou, Wei]'s Articles
[Ma, Caiwen]'s Articles
[Liao, Shenghui]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Feature Fusion Information Statistics for feature matching in cluttered scenes.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

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