An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature

doi:10.3390/app7060517

CORC > 合肥物质科学研究院 > 中国科学院合肥物质科学研究院 > 中科院合肥智能机械研究所

	An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature
	He, Lixin 1,2,3; Yang, Jing2 ; Kong, Bin 2; Wang, Can 2
刊名	APPLIED SCIENCES-BASEL
	2017-06-01
卷号	7 期号:6 页码:1-17
关键词	Monocular Image Image Segment Sift Depth Measurement Convex Hull
DOI	10.3390/app7060517
文献子类	Article
英文摘要	Recovering depth information of objects from two-dimensional images is one of the very important and basic problems in the field of computer vision. In view of the shortcomings of existing methods of depth estimation, a novel approach based on SIFT (the Scale Invariant Feature Transform) is presented in this paper. The approach can estimate the depths of objects in two images which are captured by an un-calibrated ordinary monocular camera. In this approach, above all, the first image is captured. All of the camera parameters remain unchanged, and the second image is acquired after moving the camera a distance d along the optical axis. Then image segmentation and SIFT feature extraction are implemented on the two images separately, and objects in the images are matched. Lastly, an object's depth can be computed by the lengths of a pair of straight line segments. In order to ensure that the most appropriate pair of straight line segments are chosen, and also reduce computation, convex hull theory and knowledge of triangle similarity are employed. The experimental results show our approach is effective and practical.
WOS关键词	FITTING ENERGY ; FIELD ; CAMERAS ; DEFOCUS ; FOCUS ; SHAPE
WOS研究方向	Chemistry ; Materials Science ; Physics
语种	英语
WOS记录号	WOS:000404449800007
资助机构	National Natural Science Foundation of China(91120307 ; National Natural Science Foundation of China(91120307 ; National Natural Science Foundation of China(91120307 ; National Natural Science Foundation of China(91120307 ; Scientific Research Foundation of Education Department of Anhui Province(KJ2017A541 ; Scientific Research Foundation of Education Department of Anhui Province(KJ2017A541 ; Scientific Research Foundation of Education Department of Anhui Province(KJ2017A541 ; Scientific Research Foundation of Education Department of Anhui Province(KJ2017A541 ; Key Constructive Discipline Project of Hefei University(2016xk05) ; Key Constructive Discipline Project of Hefei University(2016xk05) ; Key Constructive Discipline Project of Hefei University(2016xk05) ; Key Constructive Discipline Project of Hefei University(2016xk05) ; Quality Engineering of Higher Education of AnHui Province(2015ckjh047 ; Quality Engineering of Higher Education of AnHui Province(2015ckjh047 ; Quality Engineering of Higher Education of AnHui Province(2015ckjh047 ; Quality Engineering of Higher Education of AnHui Province(2015ckjh047 ; Outstanding Youth Talent Foundation of Hefei University(16YQ06RC) ; Outstanding Youth Talent Foundation of Hefei University(16YQ06RC) ; Outstanding Youth Talent Foundation of Hefei University(16YQ06RC) ; Outstanding Youth Talent Foundation of Hefei University(16YQ06RC) ; Scientific Research Foundation of Hefei University(16ZR14ZDA) ; Scientific Research Foundation of Hefei University(16ZR14ZDA) ; Scientific Research Foundation of Hefei University(16ZR14ZDA) ; Scientific Research Foundation of Hefei University(16ZR14ZDA) ; Education Research Foundation of Hefei University(2016mkjy04) ; Education Research Foundation of Hefei University(2016mkjy04) ; Education Research Foundation of Hefei University(2016mkjy04) ; Education Research Foundation of Hefei University(2016mkjy04) ; Pilot Project of Chinese Academy of Sciences(XDA08040109) ; Pilot Project of Chinese Academy of Sciences(XDA08040109) ; Pilot Project of Chinese Academy of Sciences(XDA08040109) ; Pilot Project of Chinese Academy of Sciences(XDA08040109) ; 91320301 ; 91320301 ; 91320301 ; 91320301 ; KJ2015A162 ; KJ2015A162 ; KJ2015A162 ; KJ2015A162 ; 2015ckjh048 ; 2015ckjh048 ; 2015ckjh048 ; 2015ckjh048 ; 61304122 ; 61304122 ; 61304122 ; 61304122 ; KJ2013B230 ; KJ2013B230 ; KJ2013B230 ; KJ2013B230 ; 2015ckjh058 ; 2015ckjh058 ; 2015ckjh058 ; 2015ckjh058 ; 61672204) ; 61672204) ; 61672204) ; 61672204) ; KJ2013A226) ; KJ2013A226) ; KJ2013A226) ; KJ2013A226) ; 2015ckjh061 ; 2015ckjh061 ; 2015ckjh061 ; 2015ckjh061 ; 2015zy054 ; 2015zy054 ; 2015zy054 ; 2015zy054 ; 2015zjjh026 ; 2015zjjh026 ; 2015zjjh026 ; 2015zjjh026 ; 2015zdjy141) ; 2015zdjy141) ; 2015zdjy141) ; 2015zdjy141) ; National Natural Science Foundation of China(91120307 ; National Natural Science Foundation of China(91120307 ; National Natural Science Foundation of China(91120307 ; National Natural Science Foundation of China(91120307 ; Scientific Research Foundation of Education Department of Anhui Province(KJ2017A541 ; Scientific Research Foundation of Education Department of Anhui Province(KJ2017A541 ; Scientific Research Foundation of Education Department of Anhui Province(KJ2017A541 ; Scientific Research Foundation of Education Department of Anhui Province(KJ2017A541 ; Key Constructive Discipline Project of Hefei University(2016xk05) ; Key Constructive Discipline Project of Hefei University(2016xk05) ; Key Constructive Discipline Project of Hefei University(2016xk05) ; Key Constructive Discipline Project of Hefei University(2016xk05) ; Quality Engineering of Higher Education of AnHui Province(2015ckjh047 ; Quality Engineering of Higher Education of AnHui Province(2015ckjh047 ; Quality Engineering of Higher Education of AnHui Province(2015ckjh047 ; Quality Engineering of Higher Education of AnHui Province(2015ckjh047 ; Outstanding Youth Talent Foundation of Hefei University(16YQ06RC) ; Outstanding Youth Talent Foundation of Hefei University(16YQ06RC) ; Outstanding Youth Talent Foundation of Hefei University(16YQ06RC) ; Outstanding Youth Talent Foundation of Hefei University(16YQ06RC) ; Scientific Research Foundation of Hefei University(16ZR14ZDA) ; Scientific Research Foundation of Hefei University(16ZR14ZDA) ; Scientific Research Foundation of Hefei University(16ZR14ZDA) ; Scientific Research Foundation of Hefei University(16ZR14ZDA) ; Education Research Foundation of Hefei University(2016mkjy04) ; Education Research Foundation of Hefei University(2016mkjy04) ; Education Research Foundation of Hefei University(2016mkjy04) ; Education Research Foundation of Hefei University(2016mkjy04) ; Pilot Project of Chinese Academy of Sciences(XDA08040109) ; Pilot Project of Chinese Academy of Sciences(XDA08040109) ; Pilot Project of Chinese Academy of Sciences(XDA08040109) ; Pilot Project of Chinese Academy of Sciences(XDA08040109) ; 91320301 ; 91320301 ; 91320301 ; 91320301 ; KJ2015A162 ; KJ2015A162 ; KJ2015A162 ; KJ2015A162 ; 2015ckjh048 ; 2015ckjh048 ; 2015ckjh048 ; 2015ckjh048 ; 61304122 ; 61304122 ; 61304122 ; 61304122 ; KJ2013B230 ; KJ2013B230 ; KJ2013B230 ; KJ2013B230 ; 2015ckjh058 ; 2015ckjh058 ; 2015ckjh058 ; 2015ckjh058 ; 61672204) ; 61672204) ; 61672204) ; 61672204) ; KJ2013A226) ; KJ2013A226) ; KJ2013A226) ; KJ2013A226) ; 2015ckjh061 ; 2015ckjh061 ; 2015ckjh061 ; 2015ckjh061 ; 2015zy054 ; 2015zy054 ; 2015zy054 ; 2015zy054 ; 2015zjjh026 ; 2015zjjh026 ; 2015zjjh026 ; 2015zjjh026 ; 2015zdjy141) ; 2015zdjy141) ; 2015zdjy141) ; 2015zdjy141)
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/31998]
专题	合肥物质科学研究院_中科院合肥智能机械研究所
作者单位	1.Univ Sci & Technol China, Dept Automat, Hefei 230027, Peoples R China 2.Chinese Acad Sci, Hefei Inst Intelligent Machines, Hefei 230031, Peoples R China 3.Hefei Univ, Key Lab Network & Intelligent Informat Proc, Hefei 230601, Peoples R China
推荐引用方式 GB/T 7714	He, Lixin,Yang, Jing,Kong, Bin,et al. An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature[J]. APPLIED SCIENCES-BASEL,2017,7(6):1-17.
APA	He, Lixin,Yang, Jing,Kong, Bin,&Wang, Can.(2017).An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature.APPLIED SCIENCES-BASEL,7(6),1-17.
MLA	He, Lixin,et al."An Automatic Measurement Method for Absolute Depth of Objects in Two Monocular Images Based on SIFT Feature".APPLIED SCIENCES-BASEL 7.6(2017):1-17.