a novel fast method for l&infin; problems in multiview geometry

ISCAS OpenIR

	a novel fast method for l∞ problems in multiview geometry
	Dai Zhijun; Wu Yihong; Zhang Fengjun; Wang Hongan
	2012
会议名称	12th European Conference on Computer Vision, ECCV 2012
会议录名称	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
页码	116-129
会议日期	October 7, 2012 - October 13, 2012
会议地点	Florence, Italy
收录类别	EI
ISSN	0302-9743
ISBN	9783642337147
部门归属	(1) Intelligence Engineering Lab. Institute of Software Chinese Academy of Sciences China; (2) State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences China; (3) National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China
摘要	Optimization using the L∞ norm is an increasingly important area in multiview geometry. Previous work has shown that globally optimal solutions can be computed reliably using the formulation of generalized fractional programming, in which algorithms solve a sequence of convex problems independently to approximate the optimal L∞ norm error. We found the sequence of convex problems are highly related and we propose a method to derive a Newton-like step from any given point. In our method, the feasible region of the current involved convex problem is contracted gradually along with the Newton-like steps, and the updated point locates on the boundary of the new feasible region. We propose an effective strategy to make the boundary point become an interior point through one dimension augmentation and relaxation. Results are presented and compared to the state of the art algorithms on simulated and real data for some multiview geometry problems with improved performance on both runtime and Newton-like iterations. © 2012 Springer-Verlag.; Optimization using the L∞ norm is an increasingly important area in multiview geometry. Previous work has shown that globally optimal solutions can be computed reliably using the formulation of generalized fractional programming, in which algorithms solve a sequence of convex problems independently to approximate the optimal L∞ norm error. We found the sequence of convex problems are highly related and we propose a method to derive a Newton-like step from any given point. In our method, the feasible region of the current involved convex problem is contracted gradually along with the Newton-like steps, and the updated point locates on the boundary of the new feasible region. We propose an effective strategy to make the boundary point become an interior point through one dimension augmentation and relaxation. Results are presented and compared to the state of the art algorithms on simulated and real data for some multiview geometry problems with improved performance on both runtime and Newton-like iterations. © 2012 Springer-Verlag.
关键词	Algorithms Geometry Optimization
主办者	Google; National Robotics Engineering Center (NREC); Adobe; Microsoft Research; Mitsubishi Electric
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15826
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Dai Zhijun,Wu Yihong,Zhang Fengjun,et al. a novel fast method for l∞ problems in multiview geometry[C],2012:116-129.